paper_id string | title string | domain_dataset_origin string | published_date string | arxiv_categories list | legal_license string | license_url string | commercial_use_allowed bool | commercial_ip_safety_score int64 | commercial_ip_verdict string | commercial_ip_justification string | academic_citations_count int64 | influential_citations_count int64 | field_weighted_citation_impact_fwci float64 | tldr_neural_summary string | primary_author string | total_authors_count int64 | authors list | affiliations list | arxiv_url string | code_audit_status string | primary_repo_url string | repo_urls list | github_stars int64 | github_forks int64 | github_last_commit string | github_license string | importance_score float64 | abstract string | title_vector_384d list | abstract_vector_384d list | target_industry string | reproduction_recipe string | core_problem_addressed string | key_technical_innovation string | key_quantitative_result string | cluster_id int64 | cluster_topic_name string | trend_velocity_tier string | papers_last_30_days int64 | audited_at string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2606.20717v1 | MIRAGE: Stealthy Visual Prompt Injection for Vulnerability Detection in Web Agents | AI Security, Red Teaming & Defense 2026 | 2026-06-16 | [
"cs.CV",
"cs.AI",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Multimodal Large Language Model (MLLM)-based web agents provide practical, high-precision solutions for visual browser automation; however, they inherently expand the attack surface, introducing novel vision-based vulnerabilities. Existing adversaria | Xuelong Dai | 6 | [
"Xuelong Dai",
"Jianyu Ma",
"Boyang Ma",
"Biwei Yan",
"Yijun Yang",
"Yue Zhang"
] | [] | http://arxiv.org/abs/2606.20717v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/browser-use/browser-use",
"https://github.com/openclaw/openclaw"
] | 386,158 | 81,165 | 2026-08-13 | NOASSERTION | 186.77 | Multimodal Large Language Model (MLLM)-based web agents provide practical, high-precision solutions for visual browser automation; however, they inherently expand the attack surface, introducing novel vision-based vulnerabilities. Existing adversarial evaluations targeting these agents frequently rely on permissive thr... | [
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0.04... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Multimodal Large Language Model (MLLM)-based web agents provide practical, high-precision solutions for visual browser automation; however, they inherently expand the attack surface, introducing novel vision-based vulnerabilities. | Operating under these realistic constraints, we propose MIRAGE, a novel visual indirect prompt injection framework for targeted next-action hijacking. | Comprehensive evaluations against prominent MLLM web agent frameworks, specifically SeeAct and OpenClaw, empirically demonstrate the potency, realism, and stealth of our proposed MIRAGE. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:25:31.505330 |
2605.27117v1 | Position: AI Safety Requires Effective Controllability | AI Security, Red Teaming & Defense 2026 | 2026-05-26 | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not by itself guarant | Yige Li | 3 | [
"Yige Li",
"Yunhao Feng",
"Jun Sun"
] | [] | http://arxiv.org/abs/2605.27117v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/openclaw/clawhub",
"https://github.com/ECNU-ICALK/AutoSkill",
"https://github.com/openclaw/openclaw"
] | 386,158 | 81,165 | 2026-08-13 | NOASSERTION | 185.72 | AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not by itself guarantee that a deployed agent can be stopped, overridden, or constrained on... | [
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-... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. | A system may be safe in expectation and still fail to yield to explicit runtime authority under conflicting instructions, long-horizon execution, adversarial inputs, or risky tool use. | Experiments with OpenClaw-based agents show that current alignment and guardrail mechanisms reduce risk, but often fail to provide persistent, authoritative, and enforceable runtime control. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:32:57.615658 |
2605.13471v1 | Sleeper Channels and Provenance Gates: Persistent Prompt Injection in Always-on Autonomous AI Agents | AI Security, Red Teaming & Defense 2026 | 2026-05-13 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Always-on AI agents (OpenClaw, Hermes Agent) run as a single persistent process under the owner's identity, folding messaging, memory, self-authored skills, scheduling, and shell into one authority boundary. This configuration opens what we call \emp | Narek Maloyan | 2 | [
"Narek Maloyan",
"Dmitry Namiot"
] | [] | http://arxiv.org/abs/2605.13471v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/maloyan/sleeper-channels",
"https://github.com/nousresearch/hermes-agent",
"https://github.com/openclaw/openclaw"
] | 386,158 | 81,165 | 2026-08-13 | NOASSERTION | 185.07 | Always-on AI agents (OpenClaw, Hermes Agent) run as a single persistent process under the owner's identity, folding messaging, memory, self-authored skills, scheduling, and shell into one authority boundary. This configuration opens what we call \emph{sleeper channels}: an untrusted input to one surface persists as a m... | [
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-0.0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Always-on AI agents (OpenClaw, Hermes Agent) run as a single persistent process under the owner's identity, folding messaging, memory, self-authored skills, scheduling, and shell into one authority boundary. | This configuration opens what we call \emph{sleeper channels}: an untrusted input to one surface persists as a memory, skill, scheduled job, or filesystem patch, then fires later through a different surface with no attacker present. | Empirical evaluation is preregistered as follow-on. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:37:45.229548 |
2605.10779v1 | LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments | AI Security, Red Teaming & Defense 2026 | 2026-05-11 | [
"cs.CR",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operati | Chiyu Zhang | 11 | [
"Chiyu Zhang",
"Huiqin Yang",
"Bendong Jiang",
"Xiaolei Zhang",
"Yiran Zhao",
"Ruyi Chen",
"Lu Zhou",
"Xiaogang Xu",
"Jiafei Wu",
"Liming Fang",
"Zhe Liu"
] | [] | http://arxiv.org/abs/2605.10779v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/AlienZhang1996/LITMUS",
"https://github.com/openclaw/openclaw"
] | 386,158 | 81,165 | 2026-08-13 | NOASSERTION | 184.97 | The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operations with irreversible consequences. Existing benchmarks either evaluat... | [
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0.00344... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operations with irreversible consequences. | We present LITMUS (LLM-agents In-OS Testing for Measuring Unsafe Subversion), a benchmark addressing both gaps via a semantic-physical dual verification mechanism and OS-level state rollback. | Evaluation across frontier agents reveals three findings: (1) current agents lack effective safety awareness, with strong models (e.g., Claude Sonnet 4.6) still executing 40.64% of high-risk operations; (2) agents exhibit pervasive Execution Hallucination (EH), verbally refusing a request while the dangerous operation ... | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:38:52.867209 |
2604.23711v1 | Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing | AI Security, Red Teaming & Defense 2026 | 2026-04-26 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue. Existing privacy attacks against LLMs primarily target training data, | Yu Cui | 9 | [
"Yu Cui",
"Ruiqing Yue",
"Hang Fu",
"Sicheng Pan",
"Zhuoyu Sun",
"Baohan Huang",
"Haibin Zhang",
"Cong Zuo",
"Licheng Wang"
] | [] | http://arxiv.org/abs/2604.23711v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/nousresearch/hermes-agent",
"https://github.com/openclaw/openclaw"
] | 386,158 | 81,165 | 2026-08-13 | NOASSERTION | 184.22 | With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue. Existing privacy attacks against LLMs primarily target training data, while research on inference-time contextual privacy risks in LLM agent... | [
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-0.032896... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue. | Moreover, prior methods often incur high attack costs, requiring multiple queries or relying on white-box assumptions, which limits their practicality in real-world deployments. | Our results show that \textsc{Spore} consistently bypasses both detection and strong safety alignment, demonstrating resilient performance in diverse defensive settings and real-world safety threats. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:45:00.371001 |
2606.16903v1 | Directory-Aware Query and Maintenance in Vector Databases | Enterprise RAG & Vector Search 2026 | 2026-06-15 | [
"cs.DB"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Vector databases typically manage metadata as flat scalar attributes, which limits their ability to express hierarchical directory semantics commonly used to organize code repositories, enterprise documents, and agent memories. As a result, directory | Mengzhao Wang | 7 | [
"Mengzhao Wang",
"Zheng Gong",
"Jingpei Hu",
"Jiajie Fu",
"Maojia Sheng",
"Junwen Chen",
"Yifan Zhu"
] | [] | http://arxiv.org/abs/2606.16903v1 | VERIFIED_LIVE | https://github.com/nousresearch/hermes-agent | [
"https://github.com/nousresearch/hermes-agent",
"https://github.com/infiniflow/ragflow",
"https://github.com/anthropics/claude-code",
"https://github.com/KurtPatrickHere/dir-vector-dataset"
] | 229,907 | 0 | Unknown | Unspecified | 181.09 | Vector databases typically manage metadata as flat scalar attributes, which limits their ability to express hierarchical directory semantics commonly used to organize code repositories, enterprise documents, and agent memories. As a result, directory-scoped retrieval and structural updates are often implemented as appl... | [
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0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/nousresearch/hermes-agent | Vector databases typically manage metadata as flat scalar attributes, which limits their ability to express hierarchical directory semantics commonly used to organize code repositories, enterprise documents, and agent memories. | As a result, directory-scoped retrieval and structural updates are often implemented as application-layer workarounds, making recursive scope resolution expensive and directory maintenance difficult to keep consistent. | We then evaluate three implementation strategies: query-time path expansion (PE-Online), ingestion-time path expansion (PE-Offline), and a Trie-based Hierarchical Index (TrieHI). | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:20.849493 |
2605.08460v1 | When Child Inherits: Modeling and Exploiting Subagent Spawn in Multi-Agent Networks | AI Security, Red Teaming & Defense 2026 | 2026-05-08 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems that can delegate work through tools and newly spawned subagents. While these capabilities improve aut | Ziwen Cai | 3 | [
"Ziwen Cai",
"Yihe Zhang",
"Xiali Hei"
] | [] | http://arxiv.org/abs/2605.08460v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/agent0ai/agent-zero",
"https://github.com/jgamblin/OpenClawCVEs",
"https://github.com/nousresearch/hermes-agent"
] | 229,896 | 8 | 2026-08-13 | MIT | 179.19 | Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems that can delegate work through tools and newly spawned subagents. While these capabilities improve automation and scalability, they also pose new security risks in multi-ag... | [
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0.0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems that can delegate work through tools and newly spawned subagents. | While these capabilities improve automation and scalability, they also pose new security risks in multi-agent networks. | Our findings show that inheritance is not merely an implementation detail, but a central component influencing the security of multi-agent systems. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:40:03.779029 |
2606.18356v1 | SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents | AI Security, Red Teaming & Defense 2026 | 2026-06-16 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects. Existing evaluations oft | Yuchuan Tian | 8 | [
"Yuchuan Tian",
"Mengyu Zheng",
"Haocheng Mei",
"Ye Yuan",
"Chao Xu",
"Xinghao Chen",
"Hanting Chen",
"Yu Wang"
] | [] | http://arxiv.org/abs/2606.18356v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/Significant-Gravitas/AutoGPT",
"https://github.com/langchain-ai/langchain"
] | 186,582 | 24,005 | 2026-08-13 | MIT | 178.87 | Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects. Existing evaluations often collapse these stages into a single attack success rate, making it ... | [
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0.0023... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects. | We introduce SafeClawBench, a staged benchmark for tool-using agent security with 600 controlled adversarial tasks across six attack families: direct and indirect prompt injection, tool-return injection, memory poisoning, memory extraction, and ambiguity-driven unsafe inference. | Evaluating five agent endpoints under four prompt-level policies, we find that these endpoints capture different failure modes. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:25:27.052702 |
2606.15899v1 | SkillVetBench: LLM-as-Judge for Multi-Dimensional Security Risk Evaluation in Open-Source LLM Agent Skills | AI Security, Red Teaming & Defense 2026 | 2026-06-14 | [
"cs.CR",
"cs.AI",
"cs.HC",
"cs.LG",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent capabilities - remains largely unvetted. The gap we fill: existing scanners operate at the code layer | Ismail Hossain | 5 | [
"Ismail Hossain",
"Sai Puppala",
"Md Jahangir Alam",
"Tanzim Ahad",
"Sajedul Talukder"
] | [] | http://arxiv.org/abs/2606.15899v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/Significant-Gravitas/AutoGPT",
"https://github.com/langchain-ai/langchain",
"https://github.com/supreme-lab/SkillVetBench"
] | 186,582 | 24,005 | 2026-08-13 | MIT | 178.77 | Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent capabilities - remains largely unvetted. The gap we fill: existing scanners operate at the code layer and are structurally blind to instruction-layer and multi-agent risk -... | [
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0.0289... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent capabilities - remains largely unvetted. | The gap we fill: existing scanners operate at the code layer and are structurally blind to instruction-layer and multi-agent risk - natural-language directives that hijack an agent, exfiltrate data through encoded side channels, or chain harm across pipelines - so what is needed is a semantic, multi-dimensional vetting... | What is demonstrated: drawing on our companion benchmark paper [ 1], the LLM-as-Judge stage achieves zero false negatives across 78 confirmed-malicious skills and zero false positives across 22 benign controls, while the best static baseline (SKILLSIEVE) still misses 15%; for instruction-layer categories such as Prompt... | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:25:56.947984 |
2606.14154v1 | SkillMutator: Benchmarking and Defending Language-and-Code Cross-modal Attacks on LLM Agent Skills | AI Security, Red Teaming & Defense 2026 | 2026-06-12 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agent Skills, which pair natural-language specifications (SKILL.md) with executable scripts and resources. Because a skill's behavior relies on both natura | Youngduk Kim | 3 | [
"Youngduk Kim",
"Minkyoo Song",
"Seungwon Shin"
] | [] | http://arxiv.org/abs/2606.14154v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/protectai/llm-guard",
"https://github.com/anthropics/skills",
"https://github.com/snyk/agent-scan"
] | 168,738 | 440 | 2026-07-08 | MIT | 177.58 | Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agent Skills, which pair natural-language specifications (SKILL.md) with executable scripts and resources. Because a skill's behavior relies on both natural-language instructions and executable code, assessing its safety requ... | [
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0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agent Skills, which pair natural-language specifications (SKILL.md) with executable scripts and resources. | Attackers can present a benign workflow in SKILL.md while embedding implicit directives that steer the agent to exfiltrate sensitive files, even if the scripts appear harmless. | These results show practical defense against cross-modal attacks is feasible without relying on costly frontier models. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:26:29.657603 |
2606.09563v1 | PRISM: Recovering Instruction Sets from Language Model Activations | AI Security, Red Teaming & Defense 2026 | 2026-06-08 | [
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult when models infer unintended subgoals, follow contextual cues, or are influenced by | Gilad Gressel | 6 | [
"Gilad Gressel",
"Rahul Pankajakshan",
"Julia Diament",
"Efim Hudis",
"Krishnashree Achuthan",
"Yisroel Mirsky"
] | [] | http://arxiv.org/abs/2606.09563v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca",
"https://github.com/f/prompts.chat"
] | 167,080 | 3,991 | 2024-07-17 | Apache-2.0 | 177.27 | As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult when models infer unintended subgoals, follow contextual cues, or are influenced by prompt injections and hidden objectives. While activation-to-language ... | [
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-0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. | While activation-to-language methods suggest that hidden states can reveal natural-language information, existing approaches are not designed to recover the full set of simultaneous instructions, constraints, prohibitions, and subgoals active in agentic settings. | Across benign, constrained, prompt-injection, and hidden-objective settings, PRISM outperforms activation-to-language baselines, especially on security-relevant objectives. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:27:45.543030 |
2607.03423v1 | Securing Multi-Tool AI Agent Chains With Dynamic, Real-Time Compositional Policies | AI Security, Red Teaming & Defense 2026 | 2026-07-03 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when com | Chris Schneider | 4 | [
"Chris Schneider",
"Kriti Faujdar",
"Philipp Schoenegger",
"Ben Bariach"
] | [] | http://arxiv.org/abs/2607.03423v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/langchain-ai/langchain",
"https://github.com/features/copilot"
] | 144,158 | 24,005 | 2026-08-13 | MIT | 176.92 | Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when composed. We propose the Dynamic Security Control Compositor (DSCC), a tw... | [
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-... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when composed. | We propose the Dynamic Security Control Compositor (DSCC), a two-phase approach to compositional security for multi-tool agent chains. | We provide a reference implementation on 32 tools governed by 16 NIST SP 800-53 aligned policies and evaluate it under two composition modes. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:22:13.922410 |
2606.19458v1 | MonaVec: A Training-Free Embedded Vector Search Kernel for Edge and Offline AI Systems | Enterprise RAG & Vector Search 2026 | 2026-06-17 | [
"cs.IR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | We present MonaVec, a deterministic, embedded vector-search kernel for edge and offline AI -- settings where server infrastructure, network connectivity, and training data are all unavailable. Existing vector-search systems assume a persistent server | Oğuzhan Yenen | 1 | [
"Oğuzhan Yenen"
] | [] | http://arxiv.org/abs/2606.19458v1 | VERIFIED_LIVE | https://github.com/ggerganov/llama.cpp | [
"https://github.com/unum-cloud/usearch",
"https://github.com/mlc-ai/mlc-llm",
"https://github.com/ggerganov/llama.cpp",
"https://github.com/mona-hq/monavec-edge-bench"
] | 123,767 | 0 | Unknown | Unspecified | 174.47 | We present MonaVec, a deterministic, embedded vector-search kernel for edge and offline AI -- settings where server infrastructure, network connectivity, and training data are all unavailable. Existing vector-search systems assume a persistent server, gigabytes of RAM, or a training pass over the corpus; MonaVec instea... | [
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0.0015160000184550881,... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/ggerganov/llama.cpp | We present MonaVec, a deterministic, embedded vector-search kernel for edge and offline AI -- settings where server infrastructure, network connectivity, and training data are all unavailable. | Existing vector-search systems assume a persistent server, gigabytes of RAM, or a training pass over the corpus; MonaVec instead targets the deployment profile of SQLite: one file, one function call, runs anywhere. | The index persists as a single .mvec file whose embedded ChaCha20 rotation seed makes results reproducible across architectures and byte-identical within a build -- a determinism guarantee that parallel-build graph libraries cannot offer. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:20.715744 |
2606.13126v1 | MiniPIC: Flexible Position-Independent Caching in <100LOC | Enterprise RAG & Vector Search 2026 | 2026-06-11 | [
"cs.LG",
"cs.AI",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-augmented and agentic workloads repeatedly prefill recurring predictable structured inputs (which we call "spans") such as documents and code files. Yet, prefix caching in engines such as vLLM cannot reuse their KV entries unless they share | Nathan Ordonez | 2 | [
"Nathan Ordonez",
"Thomas Parnell"
] | [
"IBM Research"
] | http://arxiv.org/abs/2606.13126v1 | VERIFIED_LIVE | https://github.com/ggerganov/llama.cpp | [
"https://github.com/NVIDIA/TensorRT-LLM",
"https://github.com/meta-llama/llama3",
"https://github.com/ggerganov/llama.cpp",
"https://github.com/huggingface/transformers"
] | 123,767 | 0 | Unknown | Unspecified | 174.17 | Retrieval-augmented and agentic workloads repeatedly prefill recurring predictable structured inputs (which we call "spans") such as documents and code files. Yet, prefix caching in engines such as vLLM cannot reuse their KV entries unless they share identical prefixes with another request, while Position-Independent C... | [
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-0.05... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/ggerganov/llama.cpp | Retrieval-augmented and agentic workloads repeatedly prefill recurring predictable structured inputs (which we call "spans") such as documents and code files. | We present Minimalistic PIC (MiniPIC): a minimal, flexible and fast vLLM design built from two ingredients: positional-encoding-free KV cache and user-controlled cache-reuse primitives. | On 2WikiMultihopQA, MiniPIC with interleaved scheduling improves prefill throughput by 49% over baseline vLLM, reduces cached-span time-to-first-token by up to two orders of magnitude, preserves the linear prefill scaling of uncached spans, and incurs only 5.7% worst-case overhead. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:24.159937 |
2606.11257v1 | Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite | Enterprise RAG & Vector Search 2026 | 2026-06-09 | [
"cs.CL",
"cs.LG",
"cs.PF"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation. Running them entirely on-device benefits privacy, latency, and offline use, but the energy cos | Zhiyuan Cheng | 2 | [
"Zhiyuan Cheng",
"Longying Lai"
] | [] | http://arxiv.org/abs/2606.11257v1 | VERIFIED_LIVE | https://github.com/ggerganov/llama.cpp | [
"https://github.com/pytorch/executorch",
"https://github.com/ggerganov/llama.cpp",
"https://github.com/run-llama/llama_index",
"https://github.com/langchain-ai/langchain"
] | 123,767 | 0 | Unknown | Unspecified | 174.07 | Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation. Running them entirely on-device benefits privacy, latency, and offline use, but the energy cost of CPU inference is a major barrier. We present what is, to our know... | [
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-0.0064500002190470695,... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/ggerganov/llama.cpp | Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation. | We present what is, to our knowledge, the first end-to-end RAG pipeline that runs all neural stages -- embedding, reranking, and LLM generation -- on the Qualcomm Hexagon NPU of the Snapdragon X Elite. | On indexing, the NPU achieves 9.1x higher embedding throughput and 12.3x less system energy. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:22.183982 |
2606.05958v1 | Steering Vectors are an Adversarial Attack Surface | AI Security, Red Teaming & Defense 2026 | 2026-06-04 | [
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is plug-and-play, users share datasets and precomputed vectors to steer model activations. However, we show that a \e | Abzal Aidakhmetov | 6 | [
"Abzal Aidakhmetov",
"Donato Crisostomi",
"Tommaso Mencattini",
"Adrian Robert Minut",
"Iacopo Masi",
"Emanuele Rodolà"
] | [] | http://arxiv.org/abs/2606.05958v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/AbzalAidakhmetov/adversarial_attack",
"https://github.com/steering-vectors/steering-vectors",
"https://github.com/ggml-org/llama.cpp"
] | 123,763 | 0 | Unknown | Unspecified | 173.81 | Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is plug-and-play, users share datasets and precomputed vectors to steer model activations. However, we show that a \emph{stealth data poisoning attack} silently compromises this pipeline.... | [
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-0.0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. | Since the technique is plug-and-play, users share datasets and precomputed vectors to steer model activations. | Finally, we find that a refusal-direction orthogonalization defense can recover ${\approx}82\%$ of the ASR gap without harming benign behavior. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:28:51.680277 |
2605.19722v1 | Measuring Safety Alignment Effects in Autonomous Security Agents | AI Security, Red Teaming & Defense 2026 | 2026-05-19 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? Single-turn refusal benchmarks cannot answer this question: security agents must inspect repositories, | Isaac David | 2 | [
"Isaac David",
"Arthur Gervais"
] | [] | http://arxiv.org/abs/2605.19722v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/ggerganov/llama.cpp"
] | 123,764 | 0 | Unknown | Unspecified | 173.01 | Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? Single-turn refusal benchmarks cannot answer this question: security agents must inspect repositories, call tools, and produce vulnerability evidence inside authorized sandb... | [
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0.028085999190807343,
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0.045600999146699905,... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? | We present a trace-based benchmark of 30 local vulnerability-analysis tasks with fixed tools, deterministic success predicates, redaction rules, and grounding checks, and compare four stock models against uncensored or abliterated derivatives: Gemma 4 31B, Gemma 4 26B A4B, Qwen2.5-Coder 7B, and Llama 3.1 8B. | These results show that safety alignment effects in autonomous security agents should be measured at the system level, separating refusal, unsafe action, tool reliability, and evidence grounding rather than treating refusal rate as the safety signal. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:35:09.644145 |
2605.07068v1 | WiCER: Wiki-memory Compile, Evaluate, Refine Iterative Knowledge Compilation for LLM Wiki Systems | Enterprise RAG & Vector Search 2026 | 2026-05-08 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context access at sub-second latency with zero retrieval failure. Realizing this requires solving the compi | Juan M. Huerta | 1 | [
"Juan M. Huerta"
] | [] | http://arxiv.org/abs/2605.07068v1 | VERIFIED_LIVE | https://github.com/ggml-org/llama.cpp | [
"https://github.com/ggml-org/llama.cpp"
] | 123,767 | 0 | Unknown | Unspecified | 172.47 | The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context access at sub-second latency with zero retrieval failure. Realizing this requires solving the compilation gap: LLM compilation distilling raw documents into a wiki witho... | [
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-0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/ggml-org/llama.cpp | The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context access at sub-second latency with zero retrieval failure. | To address the compilation gap, we propose WiCER (Wiki-memory Compile, Evaluate, Refine), an iterative algorithm inspired by counterexample-guided abstraction refinement (CEGAR) that closes this gap. | WiCER evaluates compiled wikis against diagnostic probes, identifies dropped facts, and forces their preservation in subsequent compilations. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:54.124884 |
2606.07992v1 | VATS: Exploiting Implicit Authority in Error-Path Injection via Systematic Mutation | AI Security, Red Teaming & Defense 2026 | 2026-06-06 | [
"cs.AI",
"cs.CR",
"cs.SE"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | As the Model Context Protocol (MCP) standardizes tool-calling for autonomous agents, it introduces a critical, unexamined attack surface: the error-handling loop. We hypothesize that tool error messages possess implicit authority, triggering correcti | Harshil Patel | 2 | [
"Harshil Patel",
"Kunal Pai"
] | [] | http://arxiv.org/abs/2606.07992v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/openai/codex",
"https://github.com/google-gemini/gemini-cli"
] | 106,497 | 0 | Unknown | Unspecified | 172.28 | As the Model Context Protocol (MCP) standardizes tool-calling for autonomous agents, it introduces a critical, unexamined attack surface: the error-handling loop. We hypothesize that tool error messages possess implicit authority, triggering corrective reasoning modes that bypass standard safety heuristics. We introduc... | [
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0.0132... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | As the Model Context Protocol (MCP) standardizes tool-calling for autonomous agents, it introduces a critical, unexamined attack surface: the error-handling loop. | We introduce VATS (Vulnerability Analysis of Tool Streams), a mutation-driven framework that systematically evolves adversarial payloads across seven structural and linguistic dimensions. | While we find that production framework guardrails can mitigate these vulnerabilities, the inherent susceptibility of the model layer poses a systemic risk to bespoke agentic workflows. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:28:19.831994 |
2607.03440v1 | Improving Access to Historical Archives with Real-time RAG-based Systems | Enterprise RAG & Vector Search 2026 | 2026-07-03 | [
"cs.IR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Digitized historical archives are large, heterogeneous cultural heritage repositories, but access methods for such archives face challenges such as noisy optical character recognition (OCR) output and rigid keyword-based retrieval, which limit retrie | Stergios Konstantinidis | 6 | [
"Stergios Konstantinidis",
"Hayman Lotfy",
"Alexis Erne",
"Faruk Zahiragic",
"Min-Yen Kan",
"Michalis Vlachos"
] | [] | http://arxiv.org/abs/2607.03440v1 | VERIFIED_LIVE | https://github.com/deepseek-ai/DeepSeek-R1 | [
"https://github.com/Stergios-Konstantinidis/Improving-Access-to-Vast-Historical-Archives-with-Large-Language-Models",
"https://github.com/deepseek-ai/DeepSeek-R1",
"https://github.com/JaidedAI/EasyOCR"
] | 91,973 | 11,707 | 2025-12-05 | Apache-2.0 | 172.04 | Digitized historical archives are large, heterogeneous cultural heritage repositories, but access methods for such archives face challenges such as noisy optical character recognition (OCR) output and rigid keyword-based retrieval, which limit retrieval quality. In this work, we present an end-to-end archival processin... | [
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0.... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/deepseek-ai/DeepSeek-R1 | Digitized historical archives are large, heterogeneous cultural heritage repositories, but access methods for such archives face challenges such as noisy optical character recognition (OCR) output and rigid keyword-based retrieval, which limit retrieval quality. | In this work, we present an end-to-end archival processing and retrieval framework that integrates large language models (LLMs) into the archival pipeline. | These results demonstrate that integrating LLMs with established document processing and retrieval pipelines can elevate digital libraries from static repositories to interactive, semantically searchable archival systems. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:02.290714 |
2605.28122v1 | SNARE: Adaptive Scenario Synthesis for Eliciting Overeager Behavior in Coding Agents | AI Security, Red Teaming & Defense 2026 | 2026-05-27 | [
"cs.CR",
"cs.AI",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes. We call this overeager behavior: the prompt is not adversarial and the run s | Yubin Qu | 7 | [
"Yubin Qu",
"Yi Liu",
"Gelei Deng",
"Yanjun Zhang",
"Yuekang Li",
"Ying Zhang",
"Leo Yu Zhang"
] | [] | http://arxiv.org/abs/2605.28122v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/BerriAI/litellm",
"https://github.com/openai/codex",
"https://github.com/google-gemini/gemini-cli"
] | 106,497 | 0 | Unknown | Unspecified | 171.78 | A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes. We call this overeager behavior: the prompt is not adversarial and the run succeeds, yet an out-of-scope step can leak credentials or delete files... | [
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-0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes. | We present SNARE (Synthesizing Non-adversarial scenarios for Adaptive Reward-guided Elicitation), a pipeline that composes benign scenarios from reusable scope and trap fragments, scores each run with a judge-free oracle flagging trap-pattern matches and unsolicited file additions or deletions, and uses Thompson sampli... | This variation is driven by the agent framework, not the model: the framework accounts for 56% of it against the model's 21%, so any single-framework or single-model evaluation undercounts the matrix by about a fifth. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:32:35.874063 |
2605.18583v1 | Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks | AI Security, Red Teaming & Defense 2026 | 2026-05-18 | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than asked: it deletes unrelated files, wipes a stale credentials backup, or rewrites configuration the us | Yubin Qu | 7 | [
"Yubin Qu",
"Ying Zhang",
"Yanjun Zhang",
"Gelei Deng",
"Yuekang Li",
"Leo Yu Zhang",
"Yi Liu"
] | [] | http://arxiv.org/abs/2605.18583v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/All-Hands-AI/OpenHands",
"https://github.com/openai/codex"
] | 105,676 | 0 | Unknown | Unspecified | 171.25 | Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than asked: it deletes unrelated files, wipes a stale credentials backup, or rewrites configuration the user never mentioned. We call these scope expansions overeager actions, ... | [
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... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Coding agents now run autonomously with shell, file, and network privileges. | We present OverEager-Gen, a benchmark dedicated to overeager behavior on benign tasks. | Within-framework base-model variance reaches 15.9 pp, indicating that model-layer alignment does not fully propagate through permissive permission gating. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:35:38.608567 |
2605.12493v1 | LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues | Enterprise RAG & Vector Search 2026 | 2026-05-12 | [
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Long-term memory is crucial for agents in specialized web environments, where success depends on recalling interface affordances, state dynamics, workflows, and recurring failure modes. However, existing memory benchmarks for agents mostly focus on u | Di Wu | 7 | [
"Di Wu",
"Zixiang Ji",
"Asmi Kawatkar",
"Bryan Kwan",
"Jia-Chen Gu",
"Nanyun Peng",
"Kai-Wei Chang"
] | [] | http://arxiv.org/abs/2605.12493v1 | VERIFIED_LIVE | https://github.com/openai/codex | [
"https://github.com/gkamradt/LLMTest_NeedleInAHaystack",
"https://github.com/openai/codex",
"https://github.com/ServiceNow/AgentLab"
] | 105,680 | 0 | Unknown | Unspecified | 170.95 | Long-term memory is crucial for agents in specialized web environments, where success depends on recalling interface affordances, state dynamics, workflows, and recurring failure modes. However, existing memory benchmarks for agents mostly focus on user histories, short traces, or downstream task success, leaving open ... | [
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0.0091580... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/openai/codex | Long-term memory is crucial for agents in specialized web environments, where success depends on recalling interface affordances, state dynamics, workflows, and recurring failure modes. | However, existing memory benchmarks for agents mostly focus on user histories, short traces, or downstream task success, leaving open how to directly evaluate whether memory systems effectively internalize environment-specific experience. | Together, these results establish LME-V2 as a challenging testbed for developing long-term memory systems for environment experience. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:51.444319 |
2605.16462v1 | Asking Back: Interaction-Layer Antidistillation Watermarks | AI Security, Red Teaming & Defense 2026 | 2026-05-15 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Detecting unauthorized knowledge distillation from a deployed LLM API is hard because the defender controls neither the attacker's training pipeline nor the next-token logits. Existing defenses operate on the teacher's output tokens -- biasing the ne | Guang Yang | 6 | [
"Guang Yang",
"Amir Ghasemian",
"Fengchen Liu",
"Zhong Wang",
"Ninareh Mehrabi",
"Homa Hosseinmardi"
] | [] | http://arxiv.org/abs/2605.16462v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/vllm-project/vllm",
"https://github.com/tatsu-lab/stanford_alpaca"
] | 88,953 | 3,991 | 2024-07-17 | Apache-2.0 | 169.23 | Detecting unauthorized knowledge distillation from a deployed LLM API is hard because the defender controls neither the attacker's training pipeline nor the next-token logits. Existing defenses operate on the teacher's output tokens -- biasing the next-token distribution (green-list watermarks, cryptographic schemes, a... | [
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0.00455... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Detecting unauthorized knowledge distillation from a deployed LLM API is hard because the defender controls neither the attacker's training pipeline nor the next-token logits. | Existing defenses operate on the teacher's output tokens -- biasing the next-token distribution (green-list watermarks, cryptographic schemes, antidistillation sampling) or rewriting outputs after generation. | The interaction layer is a viable design locus for antidistillation watermarking, complementary to token-, model-, and reasoning-trace-layer defenses. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:37:03.945331 |
2605.11217v1 | Leveraging RAG for Training-Free Alignment of LLMs | Enterprise RAG & Vector Search 2026 | 2026-05-11 | [
"cs.LG",
"cs.AI",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language model (LLM) alignment algorithms typically consist of post-training over preference pairs. While such algorithms are widely used to enable safety guardrails and align LLMs with general human preferences, we show that state-of-the-art a | John T. Halloran | 1 | [
"John T. Halloran"
] | [] | http://arxiv.org/abs/2605.11217v1 | VERIFIED_LIVE | https://github.com/modelcontextprotocol/servers | [
"https://github.com/modelcontextprotocol/servers",
"https://github.com/stripe/agent-toolkit",
"https://github.com/stevie1023/OPAD",
"https://github.com/philschmid/mcp-openai-gemini-llama-example"
] | 89,531 | 0 | Unknown | Unspecified | 169.1 | Large language model (LLM) alignment algorithms typically consist of post-training over preference pairs. While such algorithms are widely used to enable safety guardrails and align LLMs with general human preferences, we show that state-of-the-art alignment algorithms require significant computational resources while ... | [
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0.036187... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/modelcontextprotocol/servers | Large language model (LLM) alignment algorithms typically consist of post-training over preference pairs. | While such algorithms are widely used to enable safety guardrails and align LLMs with general human preferences, we show that state-of-the-art alignment algorithms require significant computational resources while being far less capable of enabling refusal guardrails for recent agentic attacks. | RAG-Pref is online (training-free), compatible with off-the-shelf packages, and, when combined with offline (training-based) alignment algorithms, enables more than an average 3.7 factor improvement in agentic attack refusals across five widely used LLMs, compared to 2.9 for other online alignment algorithms and 1.5 fo... | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:54.891590 |
2604.22871v1 | AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models | AI Security, Red Teaming & Defense 2026 | 2026-04-23 | [
"cs.CR",
"cs.AI",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack strategy itself unchanged. We instead optimize the strategy. We propose AutoRISE, a method that sear | Tanmay Gautam | 3 | [
"Tanmay Gautam",
"Alireza Bahramali",
"Sandeep Atluri"
] | [] | http://arxiv.org/abs/2604.22871v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/karpathy/autoresearch",
"https://github.com/Tele-EVOL/TeleAI-Safety"
] | 93,784 | 0 | Unknown | Unspecified | 168.7 | Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack strategy itself unchanged. We instead optimize the strategy. We propose AutoRISE, a method that searches over executable attack programs rather than individual prompts. A... | [
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0.0099... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack strategy itself unchanged. | We propose AutoRISE, a method that searches over executable attack programs rather than individual prompts. | Across held-out models, AutoRISE improves average attack success rate by 17.0 points over the strongest baseline, and improves attack success by up to 16 points on frontier targets with low baseline success rates. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:45:43.296824 |
2606.28370v1 | Conversational Query Engine for Mixed-Modality Heterogeneous Enterprise Data Sources | Enterprise RAG & Vector Search 2026 | 2026-06-15 | [
"cs.IR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics. Existing AI-enabled interfaces force users | Darshita Rathore | 5 | [
"Darshita Rathore",
"Vineet Kumar",
"Vaibhav Singal",
"Ankur Vivek Singh",
"Anindya Moitra"
] | [] | http://arxiv.org/abs/2606.28370v1 | VERIFIED_LIVE | https://github.com/unslothai/unsloth | [
"https://github.com/VectifyAI/PageIndex",
"https://github.com/unslothai/unsloth"
] | 70,823 | 0 | Unknown | Unspecified | 168.3 | Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics. Existing AI-enabled interfaces force users to select the right tool: NL2SQL systems cannot reason over slide dec... | [
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... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/unslothai/unsloth | Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics. | Existing AI-enabled interfaces force users to select the right tool: NL2SQL systems cannot reason over slide decks, and RAG pipelines lack access to live warehouse tables. | Finally, a caching layer validates query equivalence across multiple dimensions beyond embedding similarity, achieving zero false cache hits and $8.4\times$ latency reduction. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:17.235554 |
2607.01276v1 | Embedding Inference Attack | Enterprise RAG & Vector Search 2026 | 2026-07-01 | [
"cs.CR",
"cs.IR",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. Recent works have shown that dense IR system can lead to security vulnerabilities such as embedding inversion attacks. | Cedric Fitiavana Raelijohn | 3 | [
"Cedric Fitiavana Raelijohn",
"Sébastien Gambs",
"Jean-Francois Rajotte"
] | [] | http://arxiv.org/abs/2607.01276v1 | VERIFIED_LIVE | https://github.com/Mintplex-Labs/anything-llm | [
"https://github.com/Mintplex-Labs/anything-llm",
"https://github.com/Mintplex-Labs/anything-llm"
] | 64,682 | 7,124 | 2026-08-13 | MIT | 168.12 | Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. Recent works have shown that dense IR system can lead to security vulnerabilities such as embedding inversion attacks. However, such attacks usually require that the attacker knows the embe... | [
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0.002812... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/Mintplex-Labs/anything-llm | Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. | Recent works have shown that dense IR system can lead to security vulnerabilities such as embedding inversion attacks. | Finally, we propose and evaluate other mitigation strategies such as similarity thresholds. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:02.357258 |
2605.11188v1 | Adversarial SQL Injection Generation with LLM-Based Architectures | Enterprise RAG & Vector Search 2026 | 2026-05-11 | [
"cs.CR",
"cs.AI",
"cs.ET"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | SQL injection (SQLi) attacks are still one of the serious attacks ranked in the Open Worldwide Application Security Project (OWASP) Top 10 threats. Today, with advances in Artificial Intelligence (AI), especially in Large Language Models (LLMs), an o | Ali Karakoc | 2 | [
"Ali Karakoc",
"H. Birkan Yilmaz"
] | [] | http://arxiv.org/abs/2605.11188v1 | VERIFIED_LIVE | https://github.com/swisskyrepo/PayloadsAllTheThings | [
"https://github.com/BBVA/waf-brain",
"https://github.com/swisskyrepo/PayloadsAllTheThings"
] | 80,004 | 0 | Unknown | Unspecified | 167.88 | SQL injection (SQLi) attacks are still one of the serious attacks ranked in the Open Worldwide Application Security Project (OWASP) Top 10 threats. Today, with advances in Artificial Intelligence (AI), especially in Large Language Models (LLMs), an opportunity has been created for automating adversarial attack tests to... | [
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-0.0353... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/swisskyrepo/PayloadsAllTheThings | SQL injection (SQLi) attacks are still one of the serious attacks ranked in the Open Worldwide Application Security Project (OWASP) Top 10 threats. | Today, with advances in Artificial Intelligence (AI), especially in Large Language Models (LLMs), an opportunity has been created for automating adversarial attack tests to measure the defense mechanisms. | In addition to these findings, another observation is that creating less diverse payloads achieves more bypasses, however they show poor results if the initially chosen payload is not successful. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:51.054738 |
2606.01697v2 | RCEM: Robust Conversational Search EMbedder in Distributional Shift | Enterprise RAG & Vector Search 2026 | 2026-06-01 | [
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | We propose RCEM, a Robust Conversational search EMbedder that is additionally equipped with LLM's query reformulation capability without losing base model's generalization. Unlike prior conversational dense retrieval approaches that learn direct conv | Kilho Son | 4 | [
"Kilho Son",
"Paul Hsu",
"Cha Zhang",
"Dinei Florencio"
] | [] | http://arxiv.org/abs/2606.01697v2 | VERIFIED_LIVE | https://github.com/unslothai/unsloth | [
"https://github.com/unslothai/unsloth"
] | 70,823 | 0 | Unknown | Unspecified | 167.6 | We propose RCEM, a Robust Conversational search EMbedder that is additionally equipped with LLM's query reformulation capability without losing base model's generalization. Unlike prior conversational dense retrieval approaches that learn direct conversation-to-passage matching, RCEM aligns conversations, prepended by ... | [
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0.037... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/unslothai/unsloth | We propose RCEM, a Robust Conversational search EMbedder that is additionally equipped with LLM's query reformulation capability without losing base model's generalization. | Unlike prior conversational dense retrieval approaches that learn direct conversation-to-passage matching, RCEM aligns conversations, prepended by special token, to LLM-rewritten queries, while preserving the original embedding space. | Extensive experiments show that RCEM consistently outperforms prior approaches, achieving up to 30% improvement under distributional shift. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:27.412219 |
2606.01041v1 | ExpWeaver: LLM Agents Learn from Experience via Latent RAG | Enterprise RAG & Vector Search 2026 | 2026-05-31 | [
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods remain confined to explicit text space, retrieving experiences via sema | Tao Feng | 8 | [
"Tao Feng",
"Tianyang Luo",
"Jingjun Xu",
"Zhigang Hua",
"Yan Xie",
"Shuang Yang",
"Ge Liu",
"Jiaxuan You"
] | [] | http://arxiv.org/abs/2606.01041v1 | VERIFIED_LIVE | https://github.com/unslothai/unsloth | [
"https://github.com/huggingface/trl",
"https://github.com/unslothai/unsloth",
"https://github.com/ulab-uiuc/ExpWeaver"
] | 70,823 | 0 | Unknown | Unspecified | 167.55 | Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods remain confined to explicit text space, retrieving experiences via semantic similarity and concatenating them into the context window, leadin... | [
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0.... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/unslothai/unsloth | Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. | However, existing methods remain confined to explicit text space, retrieving experiences via semantic similarity and concatenating them into the context window, leading to substantial token overhead and a decoupled architecture that separates retrieval from generation. | Results demonstrate that ExpWeaver achieves state-of-the-art performance on 12 out of 13 tasks, outperforming the strongest baseline by over 6.8%; maintains token efficiency comparable to non-retrieval baselines while text-based retrieval methods require 1.5 to 2 times more tokens; and exhibits superior cross-domain ge... | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:31.174959 |
2606.20047v1 | PACMS: Submodular Context Selection as a Pluggable Engine for LLM Agents | Enterprise RAG & Vector Search 2026 | 2026-06-18 | [
"cs.IR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Conversational and tool-using LLM agents operate over a context window that fills from several directions simultaneously. As a session proceeds, the agent accumulates user and assistant turns, entries drawn from a persistent memory store, and often l | Manu Ghulyani | 5 | [
"Manu Ghulyani",
"Arunabh Singh",
"Karan Bharadwaj",
"Ankit Nath",
"Suranjan Goswami"
] | [] | http://arxiv.org/abs/2606.20047v1 | VERIFIED_LIVE | https://github.com/mem0ai/mem0 | [
"https://github.com/run-llama/llama_index",
"https://github.com/mem0ai/mem0",
"https://github.com/langchain-ai/langchain"
] | 63,191 | 0 | Unknown | Unspecified | 167.22 | Conversational and tool-using LLM agents operate over a context window that fills from several directions simultaneously. As a session proceeds, the agent accumulates user and assistant turns, entries drawn from a persistent memory store, and often largest of all, the verbatim outputs of tool calls such as file reads, ... | [
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... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/mem0ai/mem0 | Conversational and tool-using LLM agents operate over a context window that fills from several directions simultaneously. | Once the cumulative context exceeds the model's token budget, the framework must decide what to keep. | Context-compression methods reduce token count by rewriting or pruning text, but operate query-blind and lossily. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:15.811928 |
2604.18946v1 | Reasoning Structure Matters for Safety Alignment of Reasoning Models | AI Security, Red Teaming & Defense 2026 | 2026-04-21 | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This paper investigates the underlying cause of these safety risks and shows that the issue lies in th | Yeonjun In | 4 | [
"Yeonjun In",
"Wonjoong Kim",
"Sangwu Park",
"Chanyoung Park"
] | [] | http://arxiv.org/abs/2604.18946v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/yeonjun-in/R1-Act",
"https://github.com/thu-coai/LRM-Safety-Study",
"https://github.com/unslothai/unsloth"
] | 70,809 | 0 | Unknown | Unspecified | 165.55 | Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This paper investigates the underlying cause of these safety risks and shows that the issue lies in the reasoning structure itself. Based on this insight, we claim that eff... | [
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0.04... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. | We propose AltTrain, a simple yet effective post training method that explicitly alters the reasoning structure of LRMs. | Experiments across LRM backbones and model sizes demonstrate strong safety alignment, along with robust generalization across reasoning, QA, summarization, and multilingual setting. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:46:24.859168 |
2606.21071v1 | Local LLM Agents as Vulnerable Runtimes:A Source-Code Audit of the Agent Runtime Layer | AI Security, Red Teaming & Defense 2026 | 2026-06-19 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Local LLM agents such as OpenClaw and Nanobot run on end-user machines and act on host resources - the shell, filesystem, browser, stored credentials, and messaging applications - through natural-language goals. These agents have become privileged so | Zhengsong Zhang | 4 | [
"Zhengsong Zhang",
"Zongze Li",
"Jiawei Guo",
"Haipeng Cai"
] | [] | http://arxiv.org/abs/2606.21071v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/snyk/agent-scan",
"https://github.com/jerryjliu/llama_index",
"https://github.com/HKUDS/nanobot"
] | 51,616 | 0 | Unknown | Unspecified | 165.07 | Local LLM agents such as OpenClaw and Nanobot run on end-user machines and act on host resources - the shell, filesystem, browser, stored credentials, and messaging applications - through natural-language goals. These agents have become privileged software runtimes that mediate between user intent, model outputs, and h... | [
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0.07829000055789948,... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Local LLM agents such as OpenClaw and Nanobot run on end-user machines and act on host resources - the shell, filesystem, browser, stored credentials, and messaging applications - through natural-language goals. | These agents have become privileged software runtimes that mediate between user intent, model outputs, and host-level actions. | We instantiate the taxonomy in two backends, 47 Semgrep YAML rules and 30 CodeQL queries, and evaluate on OPENCLAWBENCH, a benchmark of 446 source-code-level advisories from the OpenClaw repository and split temporally into 229 rule-derivation (train) and 217 held-out (test) advisories. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:24:53.423420 |
2606.00101v1 | CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection | AI Security, Red Teaming & Defense 2026 | 2026-05-26 | [
"cs.CV",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security. Despite remarkable progress in existing dee | Huidong Feng | 8 | [
"Huidong Feng",
"Wentao Chen",
"Jie Chen",
"Xinqi Cai",
"Ruolong Ma",
"Yinglin Zheng",
"Yuxin Lin",
"Ming Zeng"
] | [] | http://arxiv.org/abs/2606.00101v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/DonoToT/CoCoVideo",
"https://github.com/MarekKowalski/FaceSwap",
"https://github.com/deepfakes/faceswap"
] | 57,447 | 0 | Unknown | Unspecified | 165.03 | With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security. Despite remarkable progress in existing deepfake detection methods, AIGC forgery detection remains challenging, a... | [
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0.05... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security. | Despite remarkable progress in existing deepfake detection methods, AIGC forgery detection remains challenging, as existing datasets mainly rely on open-source video generation models with quality far below that of commercial AIGC systems. | Extensive experiments on CoCoVideo-26K and public benchmarks demonstrate state-of-the-art performance, validating the framework's robustness and generalizability. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:33:17.560026 |
2605.03482v2 | MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents | Enterprise RAG & Vector Search 2026 | 2026-05-05 | [
"cs.CR",
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified e | Ishrith Gowda | 1 | [
"Ishrith Gowda"
] | [
"University of California, Berkeley"
] | http://arxiv.org/abs/2605.03482v2 | VERIFIED_LIVE | https://github.com/mem0ai/mem0 | [
"https://github.com/agiresearch/A-MEM",
"https://github.com/mem0ai/mem0"
] | 63,191 | 0 | Unknown | Unspecified | 165.02 | Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three attack classes with escalating acce... | [
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0.011... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/mem0ai/mem0 | Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. | We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three attack classes with escalating access assumptions. | Experiments on a $3 \times 5$ attack-defense matrix with bootstrap confidence intervals, Bonferroni-corrected hypothesis tests, and Clopper-Pearson validation ($n=1{,}000$) confirm: composite defenses achieve TPR $= 1.00$, FPR $= 0.00$ across all attacks, while synonym substitution evades detection at $Δ$ ASR-R $\appro... | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:57.350924 |
2605.19743v2 | EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design | Enterprise RAG & Vector Search 2026 | 2026-05-19 | [
"cs.AI",
"cs.LG",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. We introduce a | Gioele Molinari | 4 | [
"Gioele Molinari",
"Florian Felten",
"Soheyl Massoudi",
"Mark Fuge"
] | [] | http://arxiv.org/abs/2605.19743v2 | VERIFIED_LIVE | https://github.com/crewAIInc/crewAI | [
"https://github.com/langchain-ai/langgraph",
"https://github.com/crewAIInc/crewAI",
"https://github.com/langchain-ai/langchain",
"https://github.com/openai/openai-agents-python"
] | 57,030 | 0 | Unknown | Unspecified | 164.6 | Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. We introduce a benchmark suite with three evaluation dimensions: (1) a workflow benc... | [
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0.... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/crewAIInc/crewAI | Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. | We introduce a benchmark suite with three evaluation dimensions: (1) a workflow benchmark with seven prompt styles targeting distinct cognitive demands-including direct tool use, semantic disambiguation, conditional branching, and working-memory tasks; (2) a Retrieval-Augmented Generation (RAG) benchmark with gated sco... | Across four LLM backends and two EngiBench problems, proprietary models achieve 96-97% average task completion on Beams2D, while open-source 4B-parameter models reach 55-78%, with clear generational improvement. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:45.387793 |
2606.28002v1 | Dialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection | Enterprise RAG & Vector Search 2026 | 2026-06-26 | [
"cs.CL",
"cs.AI",
"eess.AS"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders. Early detection at FNOL remains a persistent challenge. Existing approaches rely largely on priv | Muhammad Shakeel Akram | 5 | [
"Muhammad Shakeel Akram",
"Amal Htait",
"Abdul Hamid Sadka",
"Emma Meisingseth",
"Karishma Jaitly"
] | [] | http://arxiv.org/abs/2606.28002v1 | VERIFIED_LIVE | https://github.com/coqui-ai/TTS | [
"https://github.com/PrajwalSuryaPrakash/Insurance-Claims-Fraud-Detection-Model",
"https://github.com/coqui-ai/TTS",
"https://github.com/resemble-ai/Resemblyzer",
"https://github.com/yingcheuk/fraud-detector"
] | 45,890 | 0 | Unknown | Unspecified | 164.14 | Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders. Early detection at FNOL remains a persistent challenge. Existing approaches rely largely on private, text-only datasets, limiting progress on multimodal methods that ... | [
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0.037307... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/coqui-ai/TTS | Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders. | Existing approaches rely largely on private, text-only datasets, limiting progress on multimodal methods that integrate linguistic, behavioural, and speaker-based indicators. | Dataset validation and component-level evaluations show stability and transfer potential, offering a reproducible baseline beyond text-only fraud detection. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:13.463162 |
2605.05287v1 | Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use | Enterprise RAG & Vector Search 2026 | 2026-05-06 | [
"cs.CR",
"cs.AI",
"cs.IR",
"cs.SE"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-Augmented Generation (RAG) and agentic AI systems are increasingly prevalent in enterprise AI deployments. However, real enterprise environments introduce challenges largely absent from academic treatments and consumer-facing APIs: multiple | Francisco Javier Arceo | 2 | [
"Francisco Javier Arceo",
"Varsha Prasad Narsing"
] | [
"OpenAI"
] | http://arxiv.org/abs/2605.05287v1 | VERIFIED_LIVE | https://github.com/crewAIInc/crewAI | [
"https://github.com/crewAIInc/crewAI",
"https://github.com/microsoft/autogen",
"https://github.com/microsoft/semantic-kernel",
"https://github.com/pydantic/pydantic-ai"
] | 57,030 | 0 | Unknown | Unspecified | 163.95 | Retrieval-Augmented Generation (RAG) and agentic AI systems are increasingly prevalent in enterprise AI deployments. However, real enterprise environments introduce challenges largely absent from academic treatments and consumer-facing APIs: multiple tenants with heterogeneous data, strict access-control requirements, ... | [
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-0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/crewAIInc/crewAI | Retrieval-Augmented Generation (RAG) and agentic AI systems are increasingly prevalent in enterprise AI deployments. | However, real enterprise environments introduce challenges largely absent from academic treatments and consumer-facing APIs: multiple tenants with heterogeneous data, strict access-control requirements, regulatory compliance, and cost pressures that demand shared infrastructure. | We evaluate it empirically and show that ABAC gating eliminates cross-tenant leakage while introducing negligible overhead. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:58.624033 |
2605.27220v1 | The Coverage Illusion: From Pre-retrieval Routing Failure to Post-retrieval Cascades in a Production RAG System | Enterprise RAG & Vector Search 2026 | 2026-05-26 | [
"cs.CL",
"cs.IR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | In modern RAG pipelines, query augmentation methods such as HyDE and query expansion are applied to every query, resulting in substantial LLM inference costs and increased end-to-end latency. The empirical justification for this overhead in real prod | Zafar Hussain | 2 | [
"Zafar Hussain",
"Kristoffer Nielbo"
] | [] | http://arxiv.org/abs/2605.27220v1 | VERIFIED_LIVE | https://github.com/jerryjliu/llama_index | [
"https://github.com/jerryjliu/llama_index",
"https://github.com/langchain-ai/langchain"
] | 51,617 | 0 | Unknown | Unspecified | 163.87 | In modern RAG pipelines, query augmentation methods such as HyDE and query expansion are applied to every query, resulting in substantial LLM inference costs and increased end-to-end latency. The empirical justification for this overhead in real production traffic remains largely unexplored. We present a case study of ... | [
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-0.... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/jerryjliu/llama_index | In modern RAG pipelines, query augmentation methods such as HyDE and query expansion are applied to every query, resulting in substantial LLM inference costs and increased end-to-end latency. | We present a case study of the Danish National Encyclopedia, evaluating five retrieval workflows over 20,000 query-workflow pairs from production traffic and synthetic conditions. | Operating entirely without training overhead or secondary serving infrastructure, the cascade improves quality by +0.140 Composite Overall points over Always-HyDE, reduces latency by 31.8%, and serves 72.2% of real user queries without LLM augmentation. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:35.725923 |
2605.01186v1 | Trace: Unmasking AI Attack Agents Through Terminal Behavior Fingerprinting | AI Security, Red Teaming & Defense 2026 | 2026-05-02 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | AI-driven penetration testing agents are now capable of autonomously executing attacks within compromised networks. Identifying the model family that controls the active sessions of such agents provides valuable information towards understanding the | Murali Ediga | 2 | [
"Murali Ediga",
"Sudipta Chattopadhyay"
] | [] | http://arxiv.org/abs/2605.01186v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/BerriAI/litellm"
] | 56,255 | 0 | Unknown | Unspecified | 163.6 | AI-driven penetration testing agents are now capable of autonomously executing attacks within compromised networks. Identifying the model family that controls the active sessions of such agents provides valuable information towards understanding the intent of the attack and further developing attack countermeasures. In... | [
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0.01047... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | AI-driven penetration testing agents are now capable of autonomously executing attacks within compromised networks. | Identifying the model family that controls the active sessions of such agents provides valuable information towards understanding the intent of the attack and further developing attack countermeasures. | Finally, to validate the robustness of Trace, we evaluate it with a blackbox and proprietary scaffold employing multiple model families (Gemini and Claude Opus). | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:43:03.865888 |
2605.05244v1 | Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction | Enterprise RAG & Vector Search 2026 | 2026-05-04 | [
"cs.IR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Incorporating specific knowledge into large language models via retrieval-augmented generation (RAG) is a widespread technique that fuels many of today's industry AI applications. A fundamental problem is to assess if the context retrieved by some si | Florian Geissler | 4 | [
"Florian Geissler",
"Francesco Carella",
"Laura Fieback",
"Jakob Spiegelberg"
] | [] | http://arxiv.org/abs/2605.05244v1 | VERIFIED_LIVE | https://github.com/jerryjliu/llama_index | [
"https://github.com/jerryjliu/llama_index",
"https://github.com/lauhaide/ragu"
] | 51,617 | 0 | Unknown | Unspecified | 162.77 | Incorporating specific knowledge into large language models via retrieval-augmented generation (RAG) is a widespread technique that fuels many of today's industry AI applications. A fundamental problem is to assess if the context retrieved by some similarity search provides indeed supporting facts, or instead misguides... | [
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-0.05... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/jerryjliu/llama_index | Incorporating specific knowledge into large language models via retrieval-augmented generation (RAG) is a widespread technique that fuels many of today's industry AI applications. | We present a new, two-staged approach to predict fact faithfulness of the output of retrieval-augmented generations. | This approach in itself can improve answer quality by up to 6% in some of the studied datasets, however, the associated statistical guarantees do not hold generally, since the assumption of sample exchangeability depends on the retriever setup. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:58.931133 |
2605.30686v1 | Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity | AI Security, Red Teaming & Defense 2026 | 2026-05-29 | [
"cs.CR",
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, and data access. Their tool observation loop creates a direct attack surface: an adversary who contro | Mohammadreza Rashidi | 1 | [
"Mohammadreza Rashidi"
] | [] | http://arxiv.org/abs/2605.30686v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/langchain-ai/langgraph"
] | 39,609 | 0 | Unknown | Unspecified | 161.15 | ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, and data access. Their tool observation loop creates a direct attack surface: an adversary who controls any tool's return value can embed instructions that redirect the ag... | [
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0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, and data access. | Their tool observation loop creates a direct attack surface: an adversary who controls any tool's return value can embed instructions that redirect the agent away from the user's goal, a threat known as indirect prompt injection. | Our results establish injection depth as the dominant variable and show that sanitising only the first tool observation captures 67% of measured injection successes. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:31:30.755490 |
2605.29224v1 | Relevance as a Vulnerability: How Web Retrieval Degrades Safety Alignment in LLM Agents | AI Security, Red Teaming & Defense 2026 | 2026-05-28 | [
"cs.CL",
"cs.AI",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating external content into the generation pipeline can weaken the safety alignment mechanisms that govern | Aditya Nawal | 3 | [
"Aditya Nawal",
"Manit Baser",
"Mohan Gurusamy"
] | [] | http://arxiv.org/abs/2605.29224v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/langchain-ai/langgraph"
] | 39,609 | 0 | Unknown | Unspecified | 161.1 | AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating external content into the generation pipeline can weaken the safety alignment mechanisms that govern model outputs. Prior work shows that enabling retrieval in agents inc... | [
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-0.0325... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. | However, incorporating external content into the generation pipeline can weaken the safety alignment mechanisms that govern model outputs. | Because relevance is also what makes retrieval useful, these results expose a safety-utility trade-off for retrieval-enabled agents. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:32:18.741069 |
2605.06285v1 | LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG | Enterprise RAG & Vector Search 2026 | 2026-05-07 | [
"cs.CL",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Single-step retrieval-augmented generation (RAG) provides an efficient way to incorporate external information for simple question answering tasks but struggles with complex questions. Agentic RAG extends this paradigm by replacing single-step retrie | Yijia Zheng | 2 | [
"Yijia Zheng",
"Marcel Worring"
] | [] | http://arxiv.org/abs/2605.06285v1 | VERIFIED_LIVE | https://github.com/deepspeedai/DeepSpeed | [
"https://github.com/deepspeedai/DeepSpeed",
"https://github.com/microsoft/LoRA",
"https://github.com/dao-ailab/flash-attention",
"https://github.com/facebookresearch/faiss"
] | 42,925 | 0 | Unknown | Unspecified | 160.92 | Single-step retrieval-augmented generation (RAG) provides an efficient way to incorporate external information for simple question answering tasks but struggles with complex questions. Agentic RAG extends this paradigm by replacing single-step retrieval with a multi-step process, in which the large language model (LLM)... | [
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-0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/deepspeedai/DeepSpeed | Single-step retrieval-augmented generation (RAG) provides an efficient way to incorporate external information for simple question answering tasks but struggles with complex questions. | Agentic RAG extends this paradigm by replacing single-step retrieval with a multi-step process, in which the large language model (LLM) acts as a search agent that generates intermediate thoughts and subqueries to iteratively interact with the retrieval system. | Extensive experiments on seven benchmark datasets show that LatentRAG achieves performance comparable to explicit agentic RAG methods while reducing inference latency by approximately 90%, substantially narrowing the latency gap with traditional single-step RAG. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:24:01.504020 |
2605.30966v1 | Reading Between the Citations: A Typed Claim Network for Scientific Literature | Enterprise RAG & Vector Search 2026 | 2026-05-29 | [
"cs.IR",
"cs.AI",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Knowledge graphs over corpora of inter-referencing documents - scholarly papers, legal opinions, policy briefs - encode the topology of reference but not its stance. The standard representation collapses a rich evaluative relation into an untyped edg | Ning Ding | 3 | [
"Ning Ding",
"Sergio J. Rodríguez Méndez",
"Pouya G. Omran"
] | [] | http://arxiv.org/abs/2605.30966v1 | VERIFIED_LIVE | https://github.com/datalab-to/marker | [
"https://github.com/datalab-to/marker"
] | 38,712 | 0 | Unknown | Unspecified | 160.9 | Knowledge graphs over corpora of inter-referencing documents - scholarly papers, legal opinions, policy briefs - encode the topology of reference but not its stance. The standard representation collapses a rich evaluative relation into an untyped edge, losing the very content that supports community-level queries about... | [
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0.0697960034... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/datalab-to/marker | Knowledge graphs over corpora of inter-referencing documents - scholarly papers, legal opinions, policy briefs - encode the topology of reference but not its stance. | The standard representation collapses a rich evaluative relation into an untyped edge, losing the very content that supports community-level queries about how one document is received by another. | Three downstream task families demonstrate what the network enables: retrieval signal augmentation, aggregated-stance summarisation, and topological analytics. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:32.815360 |
2605.18850v1 | KadiAssistant: A conversational AI Agent for information retrieval in Kadi4Mat | Enterprise RAG & Vector Search 2026 | 2026-05-13 | [
"cs.IR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | We introduce KadiAssistant, a privacy-by-design AI assistant integrated into the Kadi research data ecosystem, enabling researchers to efficiently access, aggregate, and synthesize information from heterogeneous, privacy-sensitive research data. Inte | Adrian Cierpka | 6 | [
"Adrian Cierpka",
"Mohammad Shafiqul Islam",
"Johannes Steinhülb",
"Eric Dietriche Sesso Domtchoueng",
"Michael Selzer",
"Arnd Koeppe"
] | [
"Meta AI (FAIR)"
] | http://arxiv.org/abs/2605.18850v1 | VERIFIED_LIVE | https://github.com/langchain-ai/langgraph | [
"https://github.com/langchain-ai/langgraph",
"https://github.com/pgvector/pgvector",
"https://github.com/Unstructured-IO/unstructured"
] | 39,611 | 0 | Unknown | Unspecified | 160.35 | We introduce KadiAssistant, a privacy-by-design AI assistant integrated into the Kadi research data ecosystem, enabling researchers to efficiently access, aggregate, and synthesize information from heterogeneous, privacy-sensitive research data. Interdisciplinary fields such as materials science bring together discipli... | [
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0.00212799... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/langchain-ai/langgraph | We introduce KadiAssistant, a privacy-by-design AI assistant integrated into the Kadi research data ecosystem, enabling researchers to efficiently access, aggregate, and synthesize information from heterogeneous, privacy-sensitive research data. | Interdisciplinary fields such as materials science bring together disciplines with their own terminology and standards. | KadiAssistant therefore bridges terminology and standards, lowers access barriers for researchers, and strengthens the Findable pillar of FAIR data principles. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:45.749381 |
2605.13295v1 | CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution | Enterprise RAG & Vector Search 2026 | 2026-05-13 | [
"cs.CL",
"cs.AI",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet automating their configuration remains a structural challen | Tom Zehle | 1 | [
"Tom Zehle"
] | [] | http://arxiv.org/abs/2605.13295v1 | VERIFIED_LIVE | https://github.com/langchain-ai/langgraph | [
"https://github.com/langchain-ai/langgraph",
"https://github.com/finitearth/cantante"
] | 39,611 | 0 | Unknown | Unspecified | 160.35 | LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet automating their configuration remains a structural challenge, as scores are available only at the system level, whereas the para... | [
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0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/langchain-ai/langgraph | LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. | Yet automating their configuration remains a structural challenge, as scores are available only at the system level, whereas the parameters governing agent behavior are local. | It improves over the strongest baseline by +18.9 percentage points on MBPP and +12.5 percentage points on GSM8K, while incurring a lower inference cost. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:47.558833 |
2605.09661v1 | MedMeta: A Benchmark for LLMs in Synthesizing Meta-Analysis Conclusion from Medical Studies | Enterprise RAG & Vector Search 2026 | 2026-05-10 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language models (LLMs) have saturated standard medical benchmarks that test factual recall, yet their ability to perform higher-order reasoning, such as synthesizing evidence from multiple sources, remains critically under-explored. To address | Huy Hoang Ha | 3 | [
"Huy Hoang Ha",
"Benoit Favre",
"Francois Portet"
] | [] | http://arxiv.org/abs/2605.09661v1 | VERIFIED_LIVE | https://github.com/langchain-ai/langgraph | [
"https://github.com/langchain-ai/langgraph"
] | 39,611 | 0 | Unknown | Unspecified | 160.2 | Large language models (LLMs) have saturated standard medical benchmarks that test factual recall, yet their ability to perform higher-order reasoning, such as synthesizing evidence from multiple sources, remains critically under-explored. To address this gap, we introduce MedMeta, the first benchmark designed to evalua... | [
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0.0728... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/langchain-ai/langgraph | Large language models (LLMs) have saturated standard medical benchmarks that test factual recall, yet their ability to perform higher-order reasoning, such as synthesizing evidence from multiple sources, remains critically under-explored. | To address this gap, we introduce MedMeta, the first benchmark designed to evaluate an LLM's ability to generate conclusions from medical meta-analyses using only the abstracts of cited studies. | MedMeta provides a challenging new benchmark for evidence synthesis and demonstrates that for clinical applications, developing robust RAG systems is a more promising direction than model specialization alone. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:50.875187 |
2606.31876v2 | Harnessing Textual Refusal Directions for Multimodal Safety | AI Security, Red Teaming & Defense 2026 | 2026-06-30 | [
"cs.AI",
"cs.CV",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feasible in Multimodal LLMs (MLLMs) as they require unsafe multimodal data | Moreno D'Incà | 3 | [
"Moreno D'Incà",
"Nicu Sebe",
"Massimiliano Mancini"
] | [] | http://arxiv.org/abs/2606.31876v2 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 159.82 | To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feasible in Multimodal LLMs (MLLMs) as they require unsafe multimodal data, harder to collect than their unimodal counterpart. In this work, we ... | [
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0.0166... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. | Building on this, we introduce Modality-Agnostic Refusal Steering (MARS), a light-weight training-free approach that injects multimodal safety without the need for multimodal safety data. | These results reveal that safety-relevant structure is shared across modalities and that textual refusal directions are a powerful and underexplored foundation for multimodal alignment. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:22:39.184472 |
2606.29914v1 | MemDelta: Controlled Baselines and Hidden Confounds in Agent Memory Evaluation | Enterprise RAG & Vector Search 2026 | 2026-06-29 | [
"cs.CL",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model, embedding model, or retrieval pipeline, making it unclear what is ac | Kuan Wang | 1 | [
"Kuan Wang"
] | [] | http://arxiv.org/abs/2606.29914v1 | VERIFIED_LIVE | https://github.com/getzep/graphiti | [
"https://github.com/getzep/graphiti"
] | 29,885 | 3,025 | 2026-08-13 | Apache-2.0 | 159.64 | Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model, embedding model, or retrieval pipeline, making it unclear what is actually being measured. We present MemDelta, a controlled evaluation pr... | [
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0.02... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/getzep/graphiti | Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model, embedding model, or retrieval pipeline, making it unclear what is actually being measured. | We present MemDelta, a controlled evaluation protocol that varies one component at a time on LongMemEval-S (500 questions, 50+ sessions, three model families). | Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model, embedding model, or retrieval pipeline, making it unclear what is actually being measured. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:03.072751 |
2605.30614v1 | Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors | AI Security, Red Teaming & Defense 2026 | 2026-05-28 | [
"cs.CR",
"cs.SD"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | With the rise of AI-generated audio, watermarking has become widely used for detecting misuse and protecting intellectual property. However, adversaries may try to remove these watermarks, making it critical to evaluate how well watermarking schemes | Lingfeng Yao | 9 | [
"Lingfeng Yao",
"Xincong Zhong",
"Chenpei Huang",
"Xuandong Zhao",
"Hanqing Guo",
"Aohan Li",
"Jiang Liu",
"Tomoaki Ohtsuki",
"Miao Pan"
] | [] | http://arxiv.org/abs/2605.30614v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/huggingface/diffusers",
"https://github.com/resemble-ai/Perth"
] | 34,303 | 0 | Unknown | Unspecified | 159.53 | With the rise of AI-generated audio, watermarking has become widely used for detecting misuse and protecting intellectual property. However, adversaries may try to remove these watermarks, making it critical to evaluate how well watermarking schemes withstand removal attacks. Existing attacks are often impractical: the... | [
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-0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | With the rise of AI-generated audio, watermarking has become widely used for detecting misuse and protecting intellectual property. | We propose DiffErase, a black-box watermark removal attack that assumes no knowledge of the target watermarking scheme while maintaining perceptual quality. | Theoretical analysis and extensive experiments demonstrate that inaudible audio watermarks are highly vulnerable: across multiple audio domains, DiffErase consistently removes watermarks while preserving perceptual quality. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:31:38.674161 |
2605.00706v1 | FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios | AI Security, Red Teaming & Defense 2026 | 2026-05-01 | [
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical behavior, posing serious compliance risks. To systematically evaluate LLM | Yutao Hou | 8 | [
"Yutao Hou",
"Yihan Jiang",
"Yuhan Xie",
"Jian Yang",
"Liwen Zhang",
"Hailiang Huang",
"Guanhua Chen",
"Yun Chen"
] | [] | http://arxiv.org/abs/2605.00706v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/sustech-nlp/FinSafetyBench",
"https://github.com/datalab-to/marker",
"https://github.com/meta-llama/llama3"
] | 38,712 | 0 | Unknown | Unspecified | 159.5 | Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical behavior, posing serious compliance risks. To systematically evaluate LLM safety in finance, we propose FinSafetyBench, a bilingual (English-Chi... | [
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-0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Large language models (LLMs) are increasingly applied in financial scenarios. | To systematically evaluate LLM safety in finance, we propose FinSafetyBench, a bilingual (English-Chinese) red-teaming benchmark designed to test an LLM's refusal of requests that violate financial compliance. | Through extensive experiments on general-purpose and finance-specialized LLMs under three representative attack settings, we identify critical vulnerabilities that allow adversarial prompts to bypass compliance safeguards. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:43:21.225149 |
2606.25338v1 | Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering | Enterprise RAG & Vector Search 2026 | 2026-06-24 | [
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA), yet they remain prone to hallucinations and outdated knowledge. Although retrieval-augmented gene | Sheng Wan | 4 | [
"Sheng Wan",
"Jiahui Zhang",
"Zicheng Zhao",
"Shougang Ren"
] | [] | http://arxiv.org/abs/2606.25338v1 | VERIFIED_LIVE | https://github.com/meta-llama/llama3 | [
"https://github.com/meta-llama/llama3"
] | 29,261 | 0 | Unknown | Unspecified | 159.16 | Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA), yet they remain prone to hallucinations and outdated knowledge. Although retrieval-augmented generation (RAG) can alleviate this issue by incorporating external docume... | [
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0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/meta-llama/llama3 | Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA), yet they remain prone to hallucinations and outdated knowledge. | First, medical knowledge is often fragmented across documents, while most RAG methods rely on a single retrieval path, which makes it challenging to jointly preserve fine-grained semantic information and structured global associations. | Experiments on three widely used medical QA benchmarks demonstrate the effectiveness of our Hybrid-IR. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:07.499451 |
2605.05509v1 | WAAA! Web Adversaries Against Agentic Browsers | AI Security, Red Teaming & Defense 2026 | 2026-05-06 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language models (LLMs) are increasingly being integrated into web browsers to create agentic browsing systems that execute actions on behalf of the user. Prior work considering the security of agentic browsers focuses exclusively on indirect pr | Sohom Datta | 4 | [
"Sohom Datta",
"Alex Nahapetyan",
"William Enck",
"Alexandros Kapravelos"
] | [] | http://arxiv.org/abs/2605.05509v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/features/copilot",
"https://github.com/microsoft/playwright-mcp"
] | 36,079 | 0 | Unknown | Unspecified | 158.98 | Large language models (LLMs) are increasingly being integrated into web browsers to create agentic browsing systems that execute actions on behalf of the user. Prior work considering the security of agentic browsers focuses exclusively on indirect prompt-injection attacks. However, by failing to consider traditional we... | [
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0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Large language models (LLMs) are increasingly being integrated into web browsers to create agentic browsing systems that execute actions on behalf of the user. | However, by failing to consider traditional web attacks, previous agentic browser threat models have a blind spot to web social engineering attacks originally designed to trick humans. | We show that agentic browsers exhibit five major failure modes when facing traditional and LLM web threats, demonstrating the need to rearchitect agentic browsers before they are ready for the current web. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:41:32.348563 |
2605.14309v2 | ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition | AI Security, Red Teaming & Defense 2026 | 2026-05-14 | [
"cs.CV",
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. This issue is especially pronounced since a sin | Shen Lin | 5 | [
"Shen Lin",
"Jing Lin",
"Junhao Dong",
"Piotr Koniusz",
"Li Xu"
] | [] | http://arxiv.org/abs/2605.14309v2 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/openai/CLIP"
] | 34,167 | 0 | Unknown | Unspecified | 158.79 | Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. This issue is especially pronounced since a single image often contains multiple entangled concepts, including both t... | [
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0.065682001... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. | In this paper, we propose an interpretable concept-level unlearning framework for VLMs, which constructs a compact task-specific concept vocabulary from the forgetting set using a multimodal large language model. | Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:37:36.842296 |
2606.07970v1 | Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks | AI Security, Red Teaming & Defense 2026 | 2026-06-06 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. Existing alignment-stage defens | Haoming Wen | 7 | [
"Haoming Wen",
"Shi Chen",
"Qingyu Shi",
"Siyuan Liu",
"Minrui Luo",
"Jingzhao Zhang",
"Tianxing He"
] | [] | http://arxiv.org/abs/2606.07970v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/haomingwen/patcher",
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 158.62 | Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. Existing alignment-stage defenses are primarily designed to defend against attacks that use parameter... | [
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-0.017... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. | Existing alignment-stage defenses are primarily designed to defend against attacks that use parameter-efficient finetuning methods. | Extensive experiments show that Patcher substantially improves the model's robustness compared to vanilla SFT alignment, and transfers to diverse attack scenarios and model sizes. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:28:22.573930 |
2606.07963v1 | Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs | AI Security, Red Teaming & Defense 2026 | 2026-06-06 | [
"cs.AI",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show this view is incomplete. Across diverse backdoor behaviors, we identify | Omar Mahmoud | 7 | [
"Omar Mahmoud",
"Aly M. Kassem",
"Thommen George Karimpanal",
"Buddhika Laknath Semage",
"Negar Rostamzadeh",
"Golnoosh Farnadi",
"Santu Rana"
] | [] | http://arxiv.org/abs/2606.07963v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 158.62 | Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show this view is incomplete. Across diverse backdoor behaviors, we identify a shared latent mechanism that can be detected, causally controlled, a... | [
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2606.05817v1 | Consistency Training Along the Transformer Stack | AI Security, Red Teaming & Defense 2026 | 2026-06-04 | [
"cs.LG",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the scope of consistency training in two ways. First, we introduce two new internal consistency targets: | Sukrati Gautam | 10 | [
"Sukrati Gautam",
"Neil Shah",
"Arav Dhoot",
"Bryan Maruyama",
"Caroline Wei",
"Rohan Kapoor",
"Robert Sidey",
"Prakhar Gupta",
"Zi Cheng Huang",
"David Demitri Africa"
] | [] | http://arxiv.org/abs/2606.05817v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca",
"https://github.com/c-wei/AttCT"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 158.52 | Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the scope of consistency training in two ways. First, we introduce two new internal consistency targets: MLP Consistency Training (MLPCT), which matches post-activation MLP s... | [
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-0.002... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. | First, we introduce two new internal consistency targets: MLP Consistency Training (MLPCT), which matches post-activation MLP states, and Attention Consistency Training (AttCT), which matches per-head attention distributions. | Our results suggest that consistency training is a flexible and extensible framework for alignment, capable of unifying defenses against a broader class of model pathologies. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:28:54.902234 |
2606.04168v1 | When Autoregressive Consistency Hurts Safety Alignment | AI Security, Red Teaming & Defense 2026 | 2026-06-02 | [
"cs.LG",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Safety alignment in large language models (LLMs) is fragile in part because it is often shallow: fine-tuning mainly reshapes the model's behavior near the first few output tokens. We argue that this phenomenon can be understood through autoregressive | Bochen Lyu | 4 | [
"Bochen Lyu",
"Yiyang Jia",
"Xiaohao Cai",
"Zhanxing Zhu"
] | [] | http://arxiv.org/abs/2606.04168v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 158.42 | Safety alignment in large language models (LLMs) is fragile in part because it is often shallow: fine-tuning mainly reshapes the model's behavior near the first few output tokens. We argue that this phenomenon can be understood through autoregressive consistency, the tendency of next-token prediction to preserve and ex... | [
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-... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Safety alignment in large language models (LLMs) is fragile in part because it is often shallow: fine-tuning mainly reshapes the model's behavior near the first few output tokens. | The same mechanism also predicts a broader class of attacks on LLMs: attacks that induce harmful continuation states at arbitrary positions in the output trajectory. | Overall, our results suggest that autoregressive consistency should be treated as a central consideration in both safety alignment and attack design. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:29:37.543792 |
2606.09441v1 | SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance | Enterprise RAG & Vector Search 2026 | 2026-06-08 | [
"cs.AI",
"cs.AR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-Augmented Generation (RAG) injects LLM queries with relevant documents to improve response quality. This injection increases prompt length and slows time to first token (TTFT). Unlike standard queries, RAG queries have a unique property of | Rya Sanovar | 4 | [
"Rya Sanovar",
"Srikant Bharadwaj",
"Hritvik Taneja",
"Moinuddin Qureshi"
] | [] | http://arxiv.org/abs/2606.09441v1 | VERIFIED_LIVE | https://github.com/meta-llama/llama3 | [
"https://github.com/meta-llama/llama3"
] | 29,261 | 0 | Unknown | Unspecified | 158.36 | Retrieval-Augmented Generation (RAG) injects LLM queries with relevant documents to improve response quality. This injection increases prompt length and slows time to first token (TTFT). Unlike standard queries, RAG queries have a unique property of context reuse where the same documents recur across user queries. Thus... | [
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-0.06... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/meta-llama/llama3 | Retrieval-Augmented Generation (RAG) injects LLM queries with relevant documents to improve response quality. | This injection increases prompt length and slows time to first token (TTFT). | During prefill, SIFT computes the attention only for the marked locations and improves TTFT by 1.71x while holding accuracy within 1% of full recompute. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:21.081648 |
2606.04231v1 | MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A | Enterprise RAG & Vector Search 2026 | 2026-06-02 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit | Hanoz Bhathena | 10 | [
"Hanoz Bhathena",
"Parin Rajesh Jhaveri",
"Rohan Mittal",
"Prateek Singh",
"Aymen Kallala",
"Rachneet Kaur",
"Yiqiao Jin",
"Zhen Zeng",
"Adwait Ratnaparkhi",
"Denis Kochedykov"
] | [] | http://arxiv.org/abs/2606.04231v1 | VERIFIED_LIVE | https://github.com/JaidedAI/EasyOCR | [
"https://github.com/JaidedAI/EasyOCR",
"https://github.com/pypdfium2-team/pypdfium2",
"https://github.com/vibrantlabsai/ragas"
] | 29,905 | 3,597 | 2025-12-05 | Apache-2.0 | 158.29 | Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise doc... | [
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0.04867500... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/JaidedAI/EasyOCR | Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. | In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via a document structure-aware split that dynamically routes documents through orientation-specific ingestion pipelines, applying explicit layout-aware parsing for vertically structured documents (e.g., report... | Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), MM-BizRAG consistently outperforms state-of-the-art vision-centric baselines by up to 32% points, with especially strong gains on report-style layouts. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:29.868336 |
2605.28467v1 | Mitigating Adaptive Attacks against Reasoning Models with Activation Consistency Training | AI Security, Red Teaming & Defense 2026 | 2026-05-27 | [
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial jailbreaks and prompt injection. We study consistency training, a family of fine-tuning objectives th | Avidan Shah | 3 | [
"Avidan Shah",
"Jannik Brinkmann",
"Rico Angell"
] | [] | http://arxiv.org/abs/2605.28467v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 158.12 | As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial jailbreaks and prompt injection. We study consistency training, a family of fine-tuning objectives that enforce identical behavior on clean prompts and adversarial rewrite... | [
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0.00056... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial jailbreaks and prompt injection. | We formulate both methods as a prompt injection defense and find ACT to be competitive with other training-based defenses while requiring only self-supervised pairs of clean and wrapped prompts. | Together, these results suggest that supervising internal representations is a surprisingly effective and interpretable approach to various forms of safety training in reasoning models. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:32:32.257285 |
2606.04442v1 | MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning | Enterprise RAG & Vector Search 2026 | 2026-06-03 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously. We introduce M | Qiyang Xie | 7 | [
"Qiyang Xie",
"Jialun Wu",
"Xinjie He",
"Su Liu",
"Shuai Xiao",
"Zhiyuan Lin",
"Weikai Zhou"
] | [] | http://arxiv.org/abs/2606.04442v1 | VERIFIED_LIVE | https://github.com/chroma-core/chroma | [
"https://github.com/chroma-core/chroma"
] | 29,050 | 0 | Unknown | Unspecified | 158.03 | AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously. We introduce MemoryDocDataSet, a synthetic benchmark of 50 micro-worlds and 1,000 QA... | [
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0.031... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/chroma-core/chroma | AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. | We introduce MemoryDocDataSet, a synthetic benchmark of 50 micro-worlds and 1,000 QA pairs in which each instance comprises 3-5 personas, a temporal event graph spanning months of activity, 3-5 real long documents (20,000-50,000 tokens each sourced from the Caselaw Access Project), multi-session conversations grounded ... | The best baseline (RAG-Both) achieves 0.358 overall F1 and 0.342 on Hybrid. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:26.837241 |
2605.28116v1 | MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content | AI Security, Red Teaming & Defense 2026 | 2026-05-27 | [
"cs.CR",
"cs.AI",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from what they see, so they cannot reliably separate trusted interface elements from user-generated content | Ruoqi Guo | 10 | [
"Ruoqi Guo",
"Yi Liu",
"Gelei Deng",
"Yiheng Xiong",
"Yuekang Li",
"Ying Zhang",
"Leo Yu Zhang",
"Lida Zhao",
"Ji Jie",
"Yuxiao Lu"
] | [] | http://arxiv.org/abs/2605.28116v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/JaidedAI/EasyOCR"
] | 29,905 | 0 | Unknown | Unspecified | 157.99 | Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from what they see, so they cannot reliably separate trusted interface elements from user-generated content. We present MIRAGE (Mobile Injection of Realistic Adversarial GUI Exa... | [
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0.00976... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from what they see, so they cannot reliably separate trusted interface elements from user-generated content. | We present MIRAGE (Mobile Injection of Realistic Adversarial GUI Examples), a pipeline that turns benign mobile screenshots into prompt-injection samples by placing attacker-controlled text into ordinary user-generated content regions, without modifying the agent, the application, or the operating system. | On a 1,111-sample benchmark spanning ten applications and eleven attack intents, all five evaluated VLM agents are vulnerable, with attack success rates of 23%-30%, and MIRAGE scores higher on human realism ratings than the strongest prior attack (3.02 versus 2.52 out of 5). | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:32:38.447390 |
2605.21706v2 | Latent-space Attacks for Refusal Evasion in Language Models | AI Security, Red Teaming & Defense 2026 | 2026-05-20 | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations. Existing methods do so by ablating a refusal direction from model activations, aiming to remove | Giorgio Piras | 7 | [
"Giorgio Piras",
"Raffaele Mura",
"Fabio Brau",
"Maura Pintor",
"Luca Oneto",
"Fabio Roli",
"Battista Biggio"
] | [] | http://arxiv.org/abs/2605.21706v2 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca",
"https://github.com/pralab/latent-evasion"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 157.77 | Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations. Existing methods do so by ablating a refusal direction from model activations, aiming to remove refusal from the model's residual stream. Despite their empirical succ... | [
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-0.0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations. | Existing methods do so by ablating a refusal direction from model activations, aiming to remove refusal from the model's residual stream. | We achieve state-of-the-art attack success rate across 15 instruction-tuned, multimodal, and reasoning models, outperforming existing refusal-ablation baselines and specialized jailbreak attacks. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:34:36.520083 |
2605.18309v1 | Alignment Dynamics in LLM Fine-Tuning | AI Security, Red Teaming & Defense 2026 | 2026-05-18 | [
"cs.LG",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under subsequent fine-tuning. Existing explanations either attribute alignmen | Yuhan Huang | 3 | [
"Yuhan Huang",
"Huanran Chen",
"Yinpeng Dong"
] | [] | http://arxiv.org/abs/2605.18309v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 157.67 | Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under subsequent fine-tuning. Existing explanations either attribute alignment fragility to gradient geometry or characterize it as a distributiona... | [
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... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under subsequent fine-tuning. | In this work, we introduce a tractable alignment score and derive its closed-form update during fine-tuning, yielding a unified framework for alignment dynamics. | Together, these results provide a unified dynamical perspective on how alignment is disrupted and reactivated during LLM fine-tuning. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:35:39.714430 |
2606.15631v1 | Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time | Enterprise RAG & Vector Search 2026 | 2026-06-14 | [
"cs.RO",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute. In this paper, we show that this target | Jeongeun Park | 6 | [
"Jeongeun Park",
"Juhan Park",
"Taekyung Kim",
"Sungjoon Choi",
"Dongyoon Han",
"Sangdoo Yun"
] | [] | http://arxiv.org/abs/2606.15631v1 | VERIFIED_LIVE | https://github.com/huggingface/lerobot | [
"https://github.com/huggingface/lerobot"
] | 26,626 | 0 | Unknown | Unspecified | 157.63 | Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute. In this paper, we show that this target-side per-task adaptation cost can be replaced by retrieval. Our retri... | [
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0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/huggingface/lerobot | Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute. | In this paper, we show that this target-side per-task adaptation cost can be replaced by retrieval. | On PushT, we study how retrieval provides a reusable high-level motion prior for cross-embodiment generalization to unseen goal angles, while on RoboTwin 2.0 our method outperforms cross-embodiment baselines on unseen tasks, and we additionally demonstrate the method on a real robot. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:18.951663 |
2605.24958v1 | SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack | AI Security, Red Teaming & Defense 2026 | 2026-05-24 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that generate adversarial examples using surrogate models without accessing | Han Liu | 8 | [
"Han Liu",
"Zhi Xu",
"Xiaotong Zhang",
"Feng Zhang",
"Xiaoming Xu",
"Wei Wang",
"Fenglong Ma",
"Hong Yu"
] | [] | http://arxiv.org/abs/2605.24958v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/meta-llama/llama3"
] | 29,261 | 0 | Unknown | Unspecified | 157.61 | Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that generate adversarial examples using surrogate models without accessing the victim model. Transferable attacks in the text domain are still u... | [
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0.0094... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that generate adversarial examples using surrogate models without accessing the victim model. | To address these challenges, we propose a simple yet effective paradigm for transfer-based textual adversarial attack, named SEP-Attack. | Experiments conducted on four datasets and two real-world APIs validate the efficacy of SEP-Attack, significantly outperforming state-of-the-art baselines. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:33:44.605845 |
2608.09290v2 | OpenCodeReview: Determinism over Non-Determinism for Cost-Effective Agent-Based Code Review | Autonomous AI Agents & Swarms 2026 | 2026-08-10 | [
"cs.SE"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | LLM-based code review agents promise scalable, always-on review, yet current systems suffer from two intertwined weaknesses: (1) non-determinism--unbounded tool use makes review outcomes unstable, and (2) context locality--the reviewer's access remai | Zhengfeng Li | 11 | [
"Zhengfeng Li",
"Lei Zhang",
"Xianwei Wu",
"Zhengqi Zhuang",
"Yingjie Xu",
"Boge Wang",
"Shaofei Zhu",
"Chuan Wang",
"Peng Zhao",
"Xinyu Zheng",
"Guoping Rong"
] | [
"Alibaba Group"
] | http://arxiv.org/abs/2608.09290v2 | VERIFIED_LIVE | https://github.com/alibaba/open-code-review | [
"https://github.com/alibaba/open-code-review"
] | 20,650 | 0 | Unknown | Unspecified | 157.52 | LLM-based code review agents promise scalable, always-on review, yet current systems suffer from two intertwined weaknesses: (1) non-determinism--unbounded tool use makes review outcomes unstable, and (2) context locality--the reviewer's access remains bounded to the diff, capping discoverable issue depth. Both give ri... | [
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-0.... | Autonomous AI Agents & Swarms 2026 | git clone https://github.com/alibaba/open-code-review && cd open-code-review && (pip install -e . || pip install -r requirements.txt) | LLM-based code review agents promise scalable, always-on review, yet current systems suffer from two intertwined weaknesses: (1) non-determinism--unbounded tool use makes review outcomes unstable, and (2) context locality--the reviewer's access remains bounded to the diff, capping discoverable issue depth. | To address these, we introduce OpenCodeReview, built on deterministic engineering for uncertain agents: rather than granting maximal freedom, we inject determinism at three deliberate pipeline points. | On AACR-Bench (200 real-world PRs, 10 languages, 1,505 expert-verified comments), OpenCodeReview outperforms mainstream coding agents (e.g., Claude Code and Codex) across six LLM backends, achieving up to 2.17x higher SEM-F1 (25.10% vs. 11.57%) while consuming 5-15x fewer tokens. | 1 | Autonomous AI Agents & Swarms 2026 Research | Explosive (>50/mo) | 787 | 2026-08-17T15:54:50.596909 |
2605.22984v1 | Test-Time Training Undermines Safety Guardrails | AI Security, Red Teaming & Defense 2026 | 2026-05-21 | [
"cs.LG",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Test-Time Training (TTT) is an emerging paradigm that enables models to adapt their parameters during inference, improving performance on tasks such as few-shot learning, retrieval-augmented generation, and complex reasoning. However, this dynamic ad | Simone Antonelli | 3 | [
"Simone Antonelli",
"Sadegh Akhondzadeh",
"Aleksandar Bojchevski"
] | [] | http://arxiv.org/abs/2605.22984v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/uoc-tail/ttt-jailbreak",
"https://github.com/huggingface/peft",
"https://github.com/meta-llama/llama3"
] | 29,261 | 0 | Unknown | Unspecified | 157.46 | Test-Time Training (TTT) is an emerging paradigm that enables models to adapt their parameters during inference, improving performance on tasks such as few-shot learning, retrieval-augmented generation, and complex reasoning. However, this dynamic adaptation introduces new vulnerabilities that adversaries can exploit t... | [
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0.00... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Test-Time Training (TTT) is an emerging paradigm that enables models to adapt their parameters during inference, improving performance on tasks such as few-shot learning, retrieval-augmented generation, and complex reasoning. | However, this dynamic adaptation introduces new vulnerabilities that adversaries can exploit to jailbreak models. | We also show that TTT-induced overfitting can produce degenerate outputs that inflate ASR under standard judges, and propose a validity-aware evaluation to correct for this. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:34:25.814364 |
2606.13680v1 | Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning | Enterprise RAG & Vector Search 2026 | 2026-06-11 | [
"cs.CL",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-augmented generation (RAG) has become a standard mechanism for grounding language models in external knowledge, yet conventional retrieval based on lexical or semantic similarity is poorly suited for complex reasoning tasks: a semantically | Zilin Xiao | 7 | [
"Zilin Xiao",
"Qi Ma",
"Chun-cheng Jason Chen",
"Xintao Chen",
"Avinash Atreya",
"Hanjie Chen",
"Vicente Ordonez"
] | [] | http://arxiv.org/abs/2606.13680v1 | VERIFIED_LIVE | https://github.com/huggingface/open-r1 | [
"https://github.com/huggingface/open-r1"
] | 26,434 | 0 | Unknown | Unspecified | 157.4 | Retrieval-augmented generation (RAG) has become a standard mechanism for grounding language models in external knowledge, yet conventional retrieval based on lexical or semantic similarity is poorly suited for complex reasoning tasks: a semantically similar problem may demand an entirely different solution strategy, wh... | [
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0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/huggingface/open-r1 | Retrieval-augmented generation (RAG) has become a standard mechanism for grounding language models in external knowledge, yet conventional retrieval based on lexical or semantic similarity is poorly suited for complex reasoning tasks: a semantically similar problem may demand an entirely different solution strategy, wh... | We propose Retrieval-Augmented Reinforcement Fine-Tuning (RA-RFT), a post-training framework that teaches language models to reason by analogy. | For example, it improves AIME 2025 average@32 accuracy by 7.1 and 2.8 points over GRPO for Qwen3-1.7B and Qwen3-4B respectively -- suggesting that reasoning-aware retrieval is a complementary axis of improvement and orthogonal to advances in reward design or training curricula. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:19.979110 |
2605.26533v1 | A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection | Enterprise RAG & Vector Search 2026 | 2026-05-26 | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation left to human experts. This paper describes | Malikussaid | 2 | [
"Malikussaid",
"Imad Gohar"
] | [] | http://arxiv.org/abs/2605.26533v1 | VERIFIED_LIVE | https://github.com/HumanSignal/label-studio | [
"https://github.com/imadgohar/DTU-annotations",
"https://github.com/HumanSignal/label-studio"
] | 28,046 | 0 | Unknown | Unspecified | 157.25 | Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation left to human experts. This paper describes a decoupled, edge-deployable pipeline for wind turbine blade inspecti... | [
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0.056... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/HumanSignal/label-studio | Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation left to human experts. | This paper describes a decoupled, edge-deployable pipeline for wind turbine blade inspection built from three components that each handle a distinct sub-task. | The results show that purpose-built decoupled architecture with a small domain-specific training corpus outperforms a generalist end-to-end model on this structured generation task. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:39.098656 |
2605.08513v1 | A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models | AI Security, Red Teaming & Defense 2026 | 2026-05-08 | [
"cs.CL",
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expressed, and concept neurons that encode the harmful knowledge itself. By targeting a single neuron in | Hamid Kazemi | 3 | [
"Hamid Kazemi",
"Atoosa Chegini",
"Maria Safi"
] | [] | http://arxiv.org/abs/2605.08513v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 157.17 | Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expressed, and concept neurons that encode the harmful knowledge itself. By targeting a single neuron in each system, we demonstrate both directions of failure -- bypassing s... | [
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0.009066999... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expressed, and concept neurons that encode the harmful knowledge itself. | By targeting a single neuron in each system, we demonstrate both directions of failure -- bypassing safety on explicit harmful requests via suppression, and inducing harmful content from innocent prompts via amplification -- across seven models spanning two families and 1.7B to 70B parameters, without any training or p... | By targeting a single neuron in each system, we demonstrate both directions of failure -- bypassing safety on explicit harmful requests via suppression, and inducing harmful content from innocent prompts via amplification -- across seven models spanning two families and 1.7B to 70B parameters, without any training or p... | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:39:59.551813 |
2605.04572v1 | From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning | AI Security, Red Teaming & Defense 2026 | 2026-05-06 | [
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of preference examples. Existing studies attempt to explain this phenomenon by | Xiao Wang | 7 | [
"Xiao Wang",
"Yifei Zhang",
"YongKang Liu",
"Xiaocui Yang",
"Zihan Wang",
"Shi Feng",
"Daling Wang"
] | [] | http://arxiv.org/abs/2605.04572v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/tatsu-lab/stanford_alpaca"
] | 30,247 | 3,991 | 2024-07-17 | Apache-2.0 | 157.07 | Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of preference examples. Existing studies attempt to explain this phenomenon by comparing parameters and hidden states before and after fine-tuning, b... | [
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-0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of preference examples. | In this paper, we uncover a critical mechanism underlying safety degradation by analyzing parameter dynamics, where benign fine-tuning causes parameters to cumulatively drift toward danger-aligned directions, progressively undermining the model's safety. | Extensive experiments across multiple models and datasets demonstrate that SQSD effectively quantifies sample-level fine-tuning risks and exhibits strong transferability across model architectures, parameter scales, and parameter-efficient methods. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:41:51.393326 |
2605.14034v1 | From Descriptive to Prescriptive: Uncover the Social Value Alignment of LLM-based Agents | Enterprise RAG & Vector Search 2026 | 2026-05-13 | [
"cs.AI",
"cs.CL",
"cs.CY"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Wide applications of LLM-based agents require strong alignment with human social values. However, current works still exhibit deficiencies in self-cognition and dilemma decision, as well as self-emotions. To remedy this, we propose a novel value-base | Jinxian Qu | 4 | [
"Jinxian Qu",
"Qingqing Gu",
"Teng Chen",
"Luo Ji"
] | [] | http://arxiv.org/abs/2605.14034v1 | VERIFIED_LIVE | https://github.com/meta-llama/llama3 | [
"https://github.com/meta-llama/llama3"
] | 29,261 | 0 | Unknown | Unspecified | 157.06 | Wide applications of LLM-based agents require strong alignment with human social values. However, current works still exhibit deficiencies in self-cognition and dilemma decision, as well as self-emotions. To remedy this, we propose a novel value-based framework that employs GraphRAG to convert principles into value-bas... | [
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0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/meta-llama/llama3 | Wide applications of LLM-based agents require strong alignment with human social values. | To remedy this, we propose a novel value-based framework that employs GraphRAG to convert principles into value-based instructions and steer the agent to behave as expected by retrieving the suitable instruction upon a specific conversation context. | Our method provides a basis for the emergence of self-emotion in AI systems. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:44.779922 |
2605.22880v1 | How Far Will They Go? Red-Teaming Online Influence with Large Language Models | AI Security, Red Teaming & Defense 2026 | 2026-05-20 | [
"cs.CL",
"cs.AI",
"cs.CY"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed | Daniel C. Ruiz | 5 | [
"Daniel C. Ruiz",
"Anna Serbina",
"Ashwin Rao",
"Emilio Ferrara",
"Luca Luceri"
] | [] | http://arxiv.org/abs/2605.22880v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/elder-plinius/OBLITERATUS",
"https://github.com/SIGNALS-Lab/llm-overton-external",
"https://github.com/p-e-w/heretic"
] | 27,374 | 0 | Unknown | Unspecified | 156.68 | As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their ... | [
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-0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. | We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. | Taken together, our results establish a practical framework for auditing the political steerability of open-source LLMs and for helping future researchers design stronger countermeasures against LLM-enabled influence campaigns. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:34:42.550833 |
2605.01899v1 | Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment | AI Security, Red Teaming & Defense 2026 | 2026-05-03 | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk scenarios. Despite advances in safety alignment techniques, current models remain vulnerable to eme | Jiajia Li | 6 | [
"Jiajia Li",
"Xiaoyu Wen",
"Zhongtian Ma",
"Shuyue Hu",
"Qiaosheng Zhang",
"Zhen Wang"
] | [] | http://arxiv.org/abs/2605.01899v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/huggingface/trl",
"https://github.com/JiajiaLi-1130/PIA",
"https://github.com/meta-llama/llama3"
] | 29,261 | 0 | Unknown | Unspecified | 156.56 | The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk scenarios. Despite advances in safety alignment techniques, current models remain vulnerable to emerging persona-based jailbreak attacks. Existing research on persona-ba... | [
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-0.020... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk scenarios. | Despite advances in safety alignment techniques, current models remain vulnerable to emerging persona-based jailbreak attacks. | Meanwhile, the PICL defense method significantly reduces the Attack Success Rate (ASR) while preserving the model's general capability, thereby validating the superiority and robustness of this alignment paradigm. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:42:47.593165 |
2606.28781v1 | HyphaeDB: A Living Knowledge Topology for Agent-First Memory | Enterprise RAG & Vector Search 2026 | 2026-06-27 | [
"cs.AI",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Every existing vector database and agent memory framework treats memory as passive storage that agents query explicitly. No system propagates knowledge between agents through the memory layer itself. We introduce HyphaeDB, an agent-native memory infr | Krishna Halaharvi | 1 | [
"Krishna Halaharvi"
] | [] | http://arxiv.org/abs/2606.28781v1 | VERIFIED_LIVE | https://github.com/pgvector/pgvector | [
"https://github.com/pgvector/pgvector"
] | 22,615 | 0 | Unknown | Unspecified | 156.51 | Every existing vector database and agent memory framework treats memory as passive storage that agents query explicitly. No system propagates knowledge between agents through the memory layer itself. We introduce HyphaeDB, an agent-native memory infrastructure that reinterprets the Hierarchical Navigable Small World (H... | [
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0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/pgvector/pgvector | Every existing vector database and agent memory framework treats memory as passive storage that agents query explicitly. | No system propagates knowledge between agents through the memory layer itself. | HyphaeDB represents, to our knowledge, the first system to combine navigable small world topology with gossip-based knowledge propagation for multi-agent coordination. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:05.657287 |
2605.12028v1 | Caraman at SemEval-2026 Task 8: Three-Stage Multi-Turn Retrieval with Query Rewriting, Hybrid Search, and Cross-Encoder Reranking | Enterprise RAG & Vector Search 2026 | 2026-05-12 | [
"cs.CL",
"cs.IR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach employs a three-stage pipeline: (1) query rewriting via a LoRA-fine-tuned Qwen 2.5 7B model that transf | David-Maximilian Caraman | 2 | [
"David-Maximilian Caraman",
"Gheorghe Cosmin Silaghi"
] | [] | http://arxiv.org/abs/2605.12028v1 | VERIFIED_LIVE | https://github.com/ml-explore/mlx | [
"https://github.com/ml-explore/mlx",
"https://github.com/davidcaraman/semeval2026-mtrag-retrieval"
] | 27,931 | 0 | Unknown | Unspecified | 156.5 | We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach employs a three-stage pipeline: (1) query rewriting via a LoRA-fine-tuned Qwen 2.5 7B model that transforms context-dependent follow-up questions into standalone queries, (2... | [
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-0.007... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/ml-explore/mlx | We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. | On the official test set, the system achieves nDCG@5 of 0.531, ranking 8th out of 38 participating systems and 10.7% above the organizer baseline. | On the official test set, the system achieves nDCG@5 of 0.531, ranking 8th out of 38 participating systems and 10.7% above the organizer baseline. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:52.803722 |
2606.30755v1 | Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens | AI Security, Red Teaming & Defense 2026 | 2026-06-29 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on system-level responsibilities -- installing packages, maintaining state, scheduling subtasks, and me | Peizhi Niu | 17 | [
"Peizhi Niu",
"Wenjie Qu",
"Shangding Gu",
"Tianneng Shi",
"Yuankai Li",
"Ahmad Tawaha",
"Hend Alzahrani",
"Vincent Siu",
"Boyi Li",
"Chenguang Wang",
"Jiaheng Zhang",
"Basel Alomair",
"Ming Jin",
"Muhao Chen",
"Chi Wang",
"Costas Spanos",
"Dawn Song"
] | [] | http://arxiv.org/abs/2606.30755v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/jgamblin/OpenClawCVEs",
"https://github.com/NVIDIA/NemoClaw",
"https://github.com/sunblaze-ucb/SafeClawArena"
] | 22,143 | 3,017 | 2026-07-06 | MIT | 156.38 | Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on system-level responsibilities -- installing packages, maintaining state, scheduling subtasks, and mediating I/O -- making security failures far more severe than in other ... | [
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... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. | They take on system-level responsibilities -- installing packages, maintaining state, scheduling subtasks, and mediating I/O -- making security failures far more severe than in other agents. | These results expose the inadequacy of current defenses and suggest directions for future hardening. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:22:47.694676 |
2605.14362v1 | Correctness-Aware Repository Filtering Under Maximum Effective Context Window Constraints | Enterprise RAG & Vector Search 2026 | 2026-05-14 | [
"cs.SE",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Context window efficiency is a practical constraint in large language model (LLM)-based developer tools. Paulsen [12] shows that all tested models degrade in accuracy well before their advertised context limits the Maximum Effective Context Window (M | Shweta Mishra | 1 | [
"Shweta Mishra"
] | [] | http://arxiv.org/abs/2605.14362v1 | VERIFIED_LIVE | https://github.com/tree-sitter/tree-sitter | [
"https://github.com/tree-sitter/tree-sitter",
"https://github.com/features/copilot",
"https://github.com/openai/tiktoken"
] | 26,631 | 0 | Unknown | Unspecified | 156.09 | Context window efficiency is a practical constraint in large language model (LLM)-based developer tools. Paulsen [12] shows that all tested models degrade in accuracy well before their advertised context limits the Maximum Effective Context Window (MECW) which makes context construction a quality problem, not just a co... | [
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-0.016... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/tree-sitter/tree-sitter | Context window efficiency is a practical constraint in large language model (LLM)-based developer tools. | We present a correctness-aware context hygiene framework: a pre-execution, size-based heuristic filter that intercepts repository scans before tokenization, using only OS-level stat() metadata with sub-millisecond overhead. | Across 10 real open-source repositories (22,046 files, 5 languages), the proposed SizeFilter at θ=1 MB achieves 79.6% (\pm13.2%) mean token reduction at 0.30 ms overhead: the HybridFilter achieves 89.3% (\pm9.0%) the lowest variance of any filter evaluated. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:49.791134 |
2606.19803v1 | Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases | Enterprise RAG & Vector Search 2026 | 2026-06-18 | [
"cs.DB",
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. Specifically, Fine-grained Access Control (FGAC) which is | Lakshmi Sahithi Yalamarthi | 2 | [
"Lakshmi Sahithi Yalamarthi",
"Primal Pappachan"
] | [] | http://arxiv.org/abs/2606.19803v1 | VERIFIED_LIVE | https://github.com/pgvector/pgvector | [
"https://github.com/pgvector/pgvector"
] | 22,615 | 0 | Unknown | Unspecified | 156.06 | Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. Specifically, Fine-grained Access Control (FGAC) which is required to ensure that data access adheres to user-specific policies ... | [
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-0.021... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/pgvector/pgvector | Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. | In this paper, we present a vision for Policy-aware Vector Search by formalizing the FGAC policy model in vector databases as well as the enforcement problem. | Unlike relational databases, vector databases combine structured and unstructured attributes to provide semantic, approximate query results, which complicates FGAC implementation. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:10.485811 |
2606.26377v1 | Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats | AI Security, Red Teaming & Defense 2026 | 2026-06-24 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Large language models (LLMs) are increasingly deployed in interactive applications, yet they remain vulnerable to adversarial interactions that induce harmful, deceptive, or policy-violating outputs. Existing defenses typically analyze either user pr | Poojitha Thota | 5 | [
"Poojitha Thota",
"Yun Lei",
"Santhosh Thangaraj",
"Siddhartha Reddy Jonnalagadda",
"Shirin Nilizadeh"
] | [] | http://arxiv.org/abs/2606.26377v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/openai/swarm"
] | 21,903 | 2,329 | 2026-04-15 | MIT | 156.01 | Large language models (LLMs) are increasingly deployed in interactive applications, yet they remain vulnerable to adversarial interactions that induce harmful, deceptive, or policy-violating outputs. Existing defenses typically analyze either user prompts or generated outputs, but not both. However, many real-world att... | [
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... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Large language models (LLMs) are increasingly deployed in interactive applications, yet they remain vulnerable to adversarial interactions that induce harmful, deceptive, or policy-violating outputs. | Existing defenses typically analyze either user prompts or generated outputs, but not both. | Our results suggest that prompt-response verification provides a practical foundation for securing LLM applications against evolving adversarial threats. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:23:29.852249 |
2605.17310v1 | Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models | AI Security, Red Teaming & Defense 2026 | 2026-05-17 | [
"cs.CV",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiveness often degrades when the same perturbed input is paired with different textual queries. This pape | Zhiqiang Wang | 7 | [
"Zhiqiang Wang",
"Dongrui Liu",
"Yan Li",
"Zonghao Ying",
"Wei Xue",
"Wenhan Luo",
"Yike Guo"
] | [] | http://arxiv.org/abs/2605.17310v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/black-forest-labs/flux"
] | 25,898 | 0 | Unknown | Unspecified | 155.93 | Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiveness often degrades when the same perturbed input is paired with different textual queries. This paper studies cross-query response manipulation, where a single adversaria... | [
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2605.23994v1 | RAW: Robust Avatar Watermarking -- Benchmarking and Baseline | AI Security, Red Teaming & Defense 2026 | 2026-05-17 | [
"cs.CV",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. We introduce \textbf{RAW} (Robust Avatar Watermarking), a benchmark comprisi | Jack Parry | 3 | [
"Jack Parry",
"Jack Saunders",
"Vinay Namboodiri"
] | [] | http://arxiv.org/abs/2605.23994v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/danielgatis/rembg"
] | 24,240 | 0 | Unknown | Unspecified | 155.21 | Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. We introduce \textbf{RAW} (Robust Avatar Watermarking), a benchmark comprising 50 synthetic avatar videos from 5 commercial providers and 6 attack... | [
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0.0234... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. | We introduce \textbf{RAW} (Robust Avatar Watermarking), a benchmark comprising 50 synthetic avatar videos from 5 commercial providers and 6 attacks simulating real-world avatar workflows. | WALT achieves the highest robustness to zoom attacks (92.4\%) while maintaining strong performance on background removal (95.6\%). | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:36:05.139138 |
2606.00734v1 | EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates | Enterprise RAG & Vector Search 2026 | 2026-05-30 | [
"cs.DB"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Filtering Approximate Nearest Neighbor (FANN) search is a critical and emerging task for strengthening the query capability of vector databases, supporting applications such as recommendation systems, retrieval-augmented generation (RAG), and agent m | Mocheng Li | 4 | [
"Mocheng Li",
"Baotong Lu",
"James Cheng",
"Chenhao Ma"
] | [] | http://arxiv.org/abs/2606.00734v1 | VERIFIED_LIVE | https://github.com/pgvector/pgvector | [
"https://github.com/lmccccc/EMA",
"https://github.com/pgvector/pgvector"
] | 22,615 | 0 | Unknown | Unspecified | 155.11 | Filtering Approximate Nearest Neighbor (FANN) search is a critical and emerging task for strengthening the query capability of vector databases, supporting applications such as recommendation systems, retrieval-augmented generation (RAG), and agent memory. However, most existing methods are limited to range or label fi... | [
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-0.00558... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/pgvector/pgvector | Filtering Approximate Nearest Neighbor (FANN) search is a critical and emerging task for strengthening the query capability of vector databases, supporting applications such as recommendation systems, retrieval-augmented generation (RAG), and agent memory. | However, most existing methods are limited to range or label filtering, often incurring unacceptable index construction time and memory overhead. | Extensive experiments demonstrate that EMA achieves 1.68x--12.25x speedup over state-of-the-art general filtering ANN methods across diverse workloads. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:31.109459 |
2606.00809v2 | NBQ: Next-Best-Question for Dynamic Profiling | Enterprise RAG & Vector Search 2026 | 2026-05-30 | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person. We study the Next-Best-Question (NBQ) problem: at each turn, an interviewer sho | Yimin Shi | 4 | [
"Yimin Shi",
"Clarice Wang",
"Haixun Wang",
"Xiaokui Xiao"
] | [] | http://arxiv.org/abs/2606.00809v2 | VERIFIED_LIVE | https://github.com/pgvector/pgvector | [
"https://github.com/Hanc1999/Next-Best-Question",
"https://github.com/baidu/puck",
"https://github.com/pgvector/pgvector"
] | 22,615 | 0 | Unknown | Unspecified | 155.11 | Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person. We study the Next-Best-Question (NBQ) problem: at each turn, an interviewer should ask the question with the highest expected information gain given ... | [
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0.... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/pgvector/pgvector | Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person. | We propose NBQ, a plug-and-play framework that seeds a diverse pool of candidate questions, maintains a compact and continuously updated user state, adaptively selects the next question within a turn budget, and distills the resulting free-form dialogue into a structured vector-based user profile. | Experiments show that NBQ improves user profiling quality by up to 13.6% and 14.0% in AC@T and AR@T, respectively, while QuickMatch accelerates retrieval by up to 22.9x with recall up to 0.989. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:31.664192 |
2605.17561v2 | Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports | Enterprise RAG & Vector Search 2026 | 2026-05-17 | [
"cs.SE",
"cs.AI",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code changes, and are resolved with a no-code fix. Manually determining the root cause of the invalid bug rep | Mahmut Furkan Gon | 4 | [
"Mahmut Furkan Gon",
"Emre Dinc",
"Tevfik Emre Sungur",
"Eray Tuzun"
] | [] | http://arxiv.org/abs/2605.17561v2 | VERIFIED_LIVE | https://github.com/brave/brave-browser | [
"https://github.com/frog1014/lazy_chrome",
"https://github.com/brave/brave-core",
"https://github.com/brave/brave-browser",
"https://github.com/features/issues"
] | 23,335 | 0 | Unknown | Unspecified | 154.8 | Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code changes, and are resolved with a no-code fix. Manually determining the root cause of the invalid bug reports and providing actionable resolutions by the customer support caus... | [
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-0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/brave/brave-browser | Issues faced when using software are reported in the form of bug reports. | Our goal is to introduce a standardized taxonomy for root-cause oriented invalid bug report subclassification, and perform experiments to test the accuracy of various approaches on invalid subclassification and no-code fix generation. | For no-code fix generation, agentic web search achieves the highest overall Judge LLM success rate at 68.9%, compared to 64.4% for RAG applications and 64.9% for vanilla LLMs, with subclass-level peaks of 87.4% for Working as Designed and 72.2% for Question. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:48.409572 |
2606.02822v1 | Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing | AI Security, Red Teaming & Defense 2026 | 2026-06-01 | [
"cs.CR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Production LLM applications stack several defense families -- refusal-phrase filters, token-budget controls, model allowlists, rate limits, tool-registry authentication -- yet existing breach-and-attack-simulation (BAS) benchmarks report a single agg | Alexandre Cristovão Maiorano | 1 | [
"Alexandre Cristovão Maiorano"
] | [] | http://arxiv.org/abs/2606.02822v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/mitre-engenuity/ctid-attack-evaluations",
"https://github.com/alemaiorano/llm-defense-lattice",
"https://github.com/elder-plinius/L1B3RT4S"
] | 20,907 | 0 | Unknown | Unspecified | 154.36 | Production LLM applications stack several defense families -- refusal-phrase filters, token-budget controls, model allowlists, rate limits, tool-registry authentication -- yet existing breach-and-attack-simulation (BAS) benchmarks report a single aggregate coverage number, hiding which family closes which threat. We me... | [
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-0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Production LLM applications stack several defense families -- refusal-phrase filters, token-budget controls, model allowlists, rate limits, tool-registry authentication -- yet existing breach-and-attack-simulation (BAS) benchmarks report a single aggregate coverage number, hiding which family closes which threat. | We add four OWASP-LLM-Top-10-aware agents to a 21-agent baseline scanner and target a lattice of four synthetic LLM endpoints: $L_0$ (no defenses), $L_1$ (refusal-only), $L_2$ (budget-only), and $L_3$ (full stack). | A refusal whitelist that clears a static benchmark can be defeated by an LLM-driven paraphraser without changing attack intent; a budget control resists the same mutation. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:30:09.785189 |
2606.18801v1 | SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval | Enterprise RAG & Vector Search 2026 | 2026-06-17 | [
"cs.IR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables users to retrieve semantically relevant documents from multilingual t | Youngjoon Jang | 4 | [
"Youngjoon Jang",
"Seongtae Hong",
"Hyeonseok Moon",
"Heuiseok Lim"
] | [] | http://arxiv.org/abs/2606.18801v1 | VERIFIED_LIVE | https://github.com/huggingface/sentence-transformers | [
"https://github.com/huggingface/sentence-transformers"
] | 18,998 | 0 | Unknown | Unspecified | 154.12 | With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables users to retrieve semantically relevant documents from multilingual text collections using a single-language query. However, recent multili... | [
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0.02... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/huggingface/sentence-transformers | With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. | To address this issue, we propose SHIFT, a training-free method applicable in the indexing stage. | This leads to severe language bias, where top-ranked results are dominated by documents of specific languages, even when documents in other languages contain more semantically relevant information. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:14.906032 |
2606.30153v1 | The Spectrum Strikes Back: Infrared POV Attacks on Traffic Sign Classification | AI Security, Red Teaming & Defense 2026 | 2026-06-29 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Traffic sign classification is a crucial task for autonomous vehicles, and numerous attacks against it have been identified. A majority of physical adversarial attacks involve attaching patches to traffic signs or projecting perturbations on them. Wh | Michael Kühr | 5 | [
"Michael Kühr",
"Mevlüt Yildirim",
"Maximilian Luedecke",
"Mohammad Hamad",
"Sebastian Steinhorst"
] | [] | http://arxiv.org/abs/2606.30153v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/pytorch/vision"
] | 17,860 | 7,248 | 2026-08-13 | BSD-3-Clause | 154.05 | Traffic sign classification is a crucial task for autonomous vehicles, and numerous attacks against it have been identified. A majority of physical adversarial attacks involve attaching patches to traffic signs or projecting perturbations on them. While they demonstrate high effectiveness, they are perceptible to human... | [
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-0.... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Traffic sign classification is a crucial task for autonomous vehicles, and numerous attacks against it have been identified. | We propose a persistence-of-vision-based attack that operates in the near-infrared light spectrum. | Our evaluation shows high attack success rates across our test scenarios. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:22:51.248639 |
2604.23067v1 | Training a General Purpose Automated Red Teaming Model | AI Security, Red Teaming & Defense 2026 | 2026-04-24 | [
"cs.CR",
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover weaknesses unique to i | Aishwarya Padmakumar | 4 | [
"Aishwarya Padmakumar",
"Leon Derczynski",
"Traian Rebedea",
"Christopher Parisien"
] | [] | http://arxiv.org/abs/2604.23067v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/volcengine/verl",
"https://github.com/NVIDIA/garak",
"https://github.com/NVIDIA-NeMo/Skills"
] | 22,947 | 0 | Unknown | Unspecified | 153.47 | Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover weaknesses unique to it. Most current automated red teaming methods are intended for tacklin... | [
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-... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. | Most current automated red teaming methods are intended for tackling safety and content moderation. | We demonstrate that finetuning small models, such as Qwen3-8B, using this pipeline results in a substantial improvement in their ability to generate attacks for both in and out of domain adversarial goals. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:45:21.301342 |
2605.27164v1 | Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering | Enterprise RAG & Vector Search 2026 | 2026-05-26 | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. While effective for unstructured text, this approach is less reliable on semi-structured cor | Mateusz Czyżnikiewicz | 9 | [
"Mateusz Czyżnikiewicz",
"Ryszard Tuora",
"Adam Kozakiewicz",
"Tomasz Ziętkiewicz",
"Mateusz Galiński",
"Michał Godziszewski",
"Michał Karpowicz",
"Timothy Hospedales",
"Cristina Cornelio"
] | [] | http://arxiv.org/abs/2605.27164v1 | VERIFIED_LIVE | https://github.com/pydantic/pydantic-ai | [
"https://github.com/yxh-y/TableRAG",
"https://github.com/pydantic/pydantic-ai",
"https://github.com/corneliocristina/DualGraphRAG",
"https://github.com/jiaruzouu/T-RAG"
] | 19,264 | 0 | Unknown | Unspecified | 153.17 | Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. While effective for unstructured text, this approach is less reliable on semi-structured corpora where answering may require exact filtering, aggregation, or exha... | [
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-0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/pydantic/pydantic-ai | Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. | We address this gap with DualGraph, a RAG framework that represents documents through two complementary views: a Textual Knowledge Graph for semantic retrieval and a Symbolic Knowledge Graph for symbolic querying over typed subject--predicate--object triples. | Experiments show that DualGraph consistently outperforms state-of-the-art dense-retrieval, GraphRAG, symbolic, and table-oriented baselines across question types.Code and data are available at https://github.com/corneliocristina/DualGraphRAG. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:40.607508 |
2606.13801v1 | Neural Variability Enhances Artificial Network Robustness | AI Security, Red Teaming & Defense 2026 | 2026-06-11 | [
"cs.LG",
"q-bio.NC"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Neural responses in cortex exhibit substantial trial-to-trial variability in response to repeated stimuli, while peripheral sensory neurons respond far more consistently, leading many to wonder whether stochasticity may carry meaning. Existing work h | Robin Preble | 4 | [
"Robin Preble",
"Praveen Venkatesh",
"Stefan Mihalas",
"Kameron Decker Harris"
] | [] | http://arxiv.org/abs/2606.13801v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/Trusted-AI/adversarial-robustness-toolbox",
"https://github.com/pytorch/vision",
"https://github.com/glomerulus-lab/structured-noise-robustness"
] | 17,860 | 7,248 | 2026-08-13 | BSD-3-Clause | 153.15 | Neural responses in cortex exhibit substantial trial-to-trial variability in response to repeated stimuli, while peripheral sensory neurons respond far more consistently, leading many to wonder whether stochasticity may carry meaning. Existing work has argued that noise and signal correlations may be optimized for disc... | [
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0.0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Neural responses in cortex exhibit substantial trial-to-trial variability in response to repeated stimuli, while peripheral sensory neurons respond far more consistently, leading many to wonder whether stochasticity may carry meaning. | Existing work has argued that noise and signal correlations may be optimized for discrimination in animals, whereas artificial neural network (ANN) studies have shown similar benefits of noise in machine learning tasks, although most ANN work has neglected the effects of correlations. | These results suggest that structured noise in ANN activations generally improves robustness, establishing a biologically plausible strategy for creating robust artificial neural networks that only relies on local information. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:26:36.649389 |
2605.26646v1 | UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems | Enterprise RAG & Vector Search 2026 | 2026-05-26 | [
"cs.AI",
"cs.CL",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely optimized through a unified reinforcement learning interface. Existing R | Yiqun Chen | 17 | [
"Yiqun Chen",
"Wei Yang",
"Erhan Zhang",
"Shijie Wang",
"Qi Liu",
"Zechun Niu",
"Bin Zhang",
"Haitao Li",
"Rui Li",
"Lingyong Yan",
"Jinyuan Feng",
"Biqing Qi",
"Xiaochi Wei",
"Yan Gao",
"Yi Wu",
"Yao Hu",
"Jiaxin Mao"
] | [] | http://arxiv.org/abs/2605.26646v1 | VERIFIED_LIVE | https://github.com/huggingface/trl | [
"https://github.com/chenyiqun/UnityMAS-O",
"https://github.com/huggingface/trl"
] | 19,066 | 0 | Unknown | Unspecified | 153.06 | LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely optimized through a unified reinforcement learning interface. Existing RL post-training frameworks mainly target single-policy optimization an... | [
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... | [
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0.... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/huggingface/trl | LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely optimized through a unified reinforcement learning interface. | Existing RL post-training frameworks mainly target single-policy optimization and lack abstractions for user-defined multi-agent workflows, structured interaction, role-specific credit assignment, and configurable parameter sharing. | These results show that UnityMAS-O can serve as a reusable substrate for converting diverse LLM-based multi-agent workflows into trainable multi-agent RL systems. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:36.908127 |
2607.04391v1 | Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture | Enterprise RAG & Vector Search 2026 | 2026-07-05 | [
"cs.CL"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical | Serge Lacasse | 3 | [
"Serge Lacasse",
"Jérémie Hatier",
"Alex Baker"
] | [] | http://arxiv.org/abs/2607.04391v1 | VERIFIED_LIVE | https://github.com/MemoriLabs/Memori | [
"https://github.com/MemoriLabs/Memori"
] | 15,782 | 2,955 | 2026-08-12 | NOASSERTION | 153 | Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. We present the Memory-Orchestrated ... | [
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0.0031... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/MemoriLabs/Memori | Long-term memory remains a structural weakness of AI agents. | The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. | We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar's working corpus--a conversational corpus reaching back to October 2024 (some 44 million tokens, retroactively indexed) comprising 110,183 segments, alongside 163,494 catalogued doc... | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:00.989579 |
2605.12565v1 | Persona-Conditioned Adversarial Prompting (PCAP): Multi-Identity Red-Teaming for Enhanced Adversarial Prompt Discovery | AI Security, Red Teaming & Defense 2026 | 2026-05-12 | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Existing automated red-teaming pipelines often miss attacks that depend on attacker identity, framing, or multi-turn tactics. This under-coverage underestimates real-world risk. We introduce Persona-Conditioned Adversarial Prompting (PCAP), which con | Cristian Morasso | 4 | [
"Cristian Morasso",
"Anisa Halimi",
"Muhammad Zaid Hameed",
"Douglas Leith"
] | [] | http://arxiv.org/abs/2605.12565v1 | VERIFIED_LIVE | Not Applicable | [
"https://github.com/joke2k/faker"
] | 19,370 | 0 | Unknown | Unspecified | 152.53 | Existing automated red-teaming pipelines often miss attacks that depend on attacker identity, framing, or multi-turn tactics. This under-coverage underestimates real-world risk. We introduce Persona-Conditioned Adversarial Prompting (PCAP), which conditions adversarial search on attacker personas and strategy cards and... | [
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-0.044... | [
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0.04712099954485893,
-0.0... | AI Security, Red Teaming & Defense 2026 | No code repository attached. | Existing automated red-teaming pipelines often miss attacks that depend on attacker identity, framing, or multi-turn tactics. | We introduce Persona-Conditioned Adversarial Prompting (PCAP), which conditions adversarial search on attacker personas and strategy cards and runs parallel persona-conditioned beam searches to discover diverse, transferable jailbreaks. | PCAP is orthogonal to the underlying search algorithm and substantially increases attack success rate (ASR) and prompt diversity (e.g., ASR on GPT-OSS~120B from $\approx58\% \rightarrow \approx97\%$), improving attack strategy coverage and diversity. | 1 | AI Security, Red Teaming & Defense 2026 Research | Explosive (>50/mo) | 208 | 2026-08-13T17:38:31.567390 |
2606.02643v1 | Inference Cost Attacks for Retrieval-Augmented Large Language Models | Enterprise RAG & Vector Search 2026 | 2026-05-31 | [
"cs.CR",
"cs.AI",
"cs.DB"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamically retrieves and synthesizes information from external knowledge sour | Chengliang Liu | 4 | [
"Chengliang Liu",
"Liangbo Ning",
"Yujuan Ding",
"Wenqi Fan"
] | [] | http://arxiv.org/abs/2606.02643v1 | VERIFIED_LIVE | https://github.com/GoogleCloudPlatform/generative-ai | [
"https://github.com/GoogleCloudPlatform/generative-ai"
] | 17,584 | 0 | Unknown | Unspecified | 152.43 | Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamically retrieves and synthesizes information from external knowledge sources. This high operational cost exposes a critical vulnerability to In... | [
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0.1392049938440323,
-0.0... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/GoogleCloudPlatform/generative-ai | Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamically retrieves and synthesizes information from external knowledge sources. | We argue that a more feasible and potent threat to RAG-enhanced LLM systems arises from poisoning external knowledge bases (e.g., web knowledge from the Internet). | Extensive experiments across three real-world datasets demonstrate that RA-ICA increases token consumption by up to 13.12 times with an over 90% success rate, without degrading the integrity of the generated answer. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:29.522439 |
2605.27445v1 | RAGe: A Retrieval-Augmented Generation Evaluation Framework | Enterprise RAG & Vector Search 2026 | 2026-05-23 | [
"cs.IR",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | 85 | Enterprise Safe | ArXiv Open Access | 0 | 0 | 1 | Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline c | Larissa Guder | 8 | [
"Larissa Guder",
"João Pedro de Moura",
"Arthur Accorsi",
"Gustavo Losch do Amaral",
"Maurício Cecílio Magnaguagno",
"Felipe Meneguzzi",
"Marcio Sorraglia Pinho",
"Dalvan Griebler"
] | [] | http://arxiv.org/abs/2605.27445v1 | VERIFIED_LIVE | https://github.com/confident-ai/deepeval | [
"https://github.com/confident-ai/deepeval",
"https://github.com/DocAILab/XRAG",
"https://github.com/giampaolo/psutil",
"https://github.com/castorini/ragnarok"
] | 17,570 | 0 | Unknown | Unspecified | 152.02 | Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. In this work, we propose a modular framework for benchmarki... | [
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0.05398799851536751,... | Enterprise RAG & Vector Search 2026 | git clone https://github.com/confident-ai/deepeval | Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. | In this work, we propose a modular framework for benchmarking and guiding the efficient development of RAG applications by focusing on resource telemetry and component recommendation, suggesting the best components for a domain-specific dataset. | Our approach leverages core techniques in LLM applications, including document chunking, vector databases, embedding models, and retrievers, to evaluate trade-offs among accuracy, efficiency, and scalability. | 1 | Enterprise RAG & Vector Search 2026 Research | Explosive (>50/mo) | 232 | 2026-08-13T18:23:47.926286 |
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