Datasets:

beatsprom
/

complete-2026-ai-engineering-master-suite

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	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	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...	[ 0.02351200021803379, 0.01979300007224083, -0.04714199900627136, 0.02040099911391735, 0.05547900125384331, -0.01540399994701147, 0.05035800114274025, -0.02896299958229065, 0.0019600000232458115, -0.047237999737262726, 0.03424699977040291, -0.10742399841547012, 0.10490699857473373, -0.020897...	[ -0.009228000417351723, -0.051242999732494354, -0.04223199933767319, 0.011796999722719193, 0.11453799903392792, -0.020932000130414963, 0.006814000196754932, -0.08849000185728073, -0.02828799933195114, -0.0928340032696724, 0.046358998864889145, -0.06631399691104889, 0.09932400286197662, 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	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...	[ -0.0022690000478178263, -0.019324999302625656, -0.09358099848031998, -0.033254001289606094, 0.05132000148296356, 0.09982399642467499, 0.04536600038409233, 0.05157700181007385, 0.00470300018787384, 0.06263700127601624, 0.02613699994981289, -0.0097850002348423, 0.08657299727201462, -0.039393...	[ -0.024265000596642494, -0.05521199852228165, -0.10194499790668488, 0.013477000407874584, 0.07544200122356415, 0.055470000952482224, -0.00023999999393709004, 0.006891000084578991, -0.0563259981572628, 0.031658999621868134, -0.019876999780535698, -0.039083000272512436, 0.04993100091814995, -...	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	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...	[ -0.01912200078368187, -0.02450699917972088, -0.026319000869989395, 0.01863499917089939, 0.060210999101400375, -0.022630000486969948, 0.04877600073814392, -0.05324700102210045, 0.04299100115895271, 0.006430999841541052, -0.03427699953317642, -0.024661000818014145, 0.05878499895334244, 0.004...	[ -0.08927199989557266, -0.04039100185036659, -0.03743699938058853, -0.0051739998161792755, 0.05998900160193443, -0.04788700118660927, 0.03198999911546707, -0.0659480020403862, 0.060892000794410706, -0.014096000231802464, 0.005849000066518784, 0.004623000044375658, 0.035092998296022415, -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	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...	[ 0.018473999574780464, -0.03946699947118759, -0.02573000080883503, -0.05923999845981598, 0.042426999658346176, -0.05337899923324585, -0.03919700160622597, -0.06467299908399582, -0.052657999098300934, 0.01622300036251545, 0.030074000358581543, -0.048781998455524445, -0.03034999966621399, -0....	[ 0.015259999781847, -0.03278600051999092, -0.03894200176000595, -0.0176829993724823, 0.10044900327920914, -0.06067200005054474, -0.017566999420523643, -0.008449999615550041, -0.043912000954151154, 0.05008900165557861, 0.020167000591754913, -0.04947499930858612, 0.024470999836921692, 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	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...	[ -0.07616399973630905, -0.024971000850200653, 0.033511001616716385, 0.013811999931931496, 0.12026900053024292, -0.016899000853300095, 0.029203999787569046, -0.13791699707508087, 0.0612419992685318, 0.014198999851942062, 0.02269200049340725, 0.0051310001872479916, 0.0565590001642704, -0.0457...	[ -0.08033999800682068, -0.037629999220371246, -0.02459999918937683, 0.07502099871635437, 0.071042001247406, 0.031658001244068146, 0.07001399993896484, -0.049699001014232635, 0.05559699982404709, -0.02536500059068203, 0.022617999464273453, -0.06852000206708908, 0.07579399645328522, -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	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...	[ -0.006132999900728464, -0.0038479999639093876, -0.0036810000892728567, 0.04856700077652931, 0.04247700050473213, -0.06839100271463394, 0.06530600041151047, 0.013061000034213066, -0.05138099938631058, 0.001724999980069697, 0.0204709991812706, 0.06160400062799454, 0.012019000016152859, -0.02...	[ -0.016138000413775444, -0.005367000121623278, 0.012133999727666378, 0.024265000596642494, 0.006351000163704157, -0.099031001329422, -0.011648000217974186, 0.003957000095397234, -0.032976001501083374, -0.0006580000044777989, -0.000582000007852912, 0.03860300034284592, 0.05131300166249275, 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	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...	[ -0.028053000569343567, -0.0147280003875494, -0.02994699962437153, -0.031184999272227287, 0.03456699848175049, -0.009952999651432037, 0.008923999965190887, 0.010083000175654888, 0.013206000439822674, 0.03251200169324875, 0.012198000214993954, -0.049872998148202896, 0.024650000035762787, 0.0...	[ -0.014183999970555305, -0.04420600086450577, -0.025851000100374222, -0.04326599836349487, 0.016339000314474106, 0.017362000420689583, 0.04730900004506111, -0.010892000049352646, 0.03016900084912777, -0.013469000346958637, 0.005809000227600336, -0.01040900032967329, 0.08036799728870392, 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	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 ...	[ -0.01608400046825409, 0.026892000809311867, -0.050866998732089996, -0.022012999281287193, 0.11614199727773666, -0.00955200009047985, -0.022732999175786972, -0.03607799857854843, 0.03597399964928627, 0.036472998559474945, 0.027865000069141388, -0.03910600021481514, 0.07856100052595139, -0.0...	[ 0.013704000040888786, -0.0337660014629364, -0.05375700071454048, 0.03315899893641472, 0.08342500030994415, 0.018396999686956406, -0.033319998532533646, -0.020243000239133835, 0.009122000075876713, 0.003260999917984009, -0.017494000494480133, -0.0762609988451004, 0.10410899668931961, 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	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 -...	[ 0.020896999165415764, -0.009638999588787556, -0.10179299861192703, -0.025658000260591507, 0.055153001099824905, 0.032409001141786575, 0.04936699941754341, -0.0055669997818768024, -0.02014699950814247, -0.022873999550938606, -0.03721199929714203, -0.05082400143146515, 0.09614499658346176, 0...	[ 0.01688399910926819, -0.026976000517606735, -0.0825520008802414, -0.029357999563217163, 0.0717260017991066, -0.014929000288248062, 0.01243400014936924, -0.006486999802291393, -0.05359499901533127, -0.02553199976682663, -0.02628600038588047, -0.01840299926698208, 0.10599300265312195, 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	1	"Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agen(...TRUNCATED)	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(...TRUNCATED)	168,738	440	2026-07-08	MIT	177.58	"Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agen(...TRUNCATED)	[-0.08292900025844574,-0.04670799896121025,0.0068660001270473,-0.02508299984037876,0.018561000004410(...TRUNCATED)	[-0.04954399913549423,-0.07263399660587311,-0.01945200003683567,-0.005915000103414059,0.087206996977(...TRUNCATED)	AI Security, Red Teaming & Defense 2026	No code repository attached.	"Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agen(...TRUNCATED)	"Attackers can present a benign workflow in SKILL.md while embedding implicit directives that steer (...TRUNCATED)	"These results show practical defense against cross-modal attacks is feasible without relying on cos(...TRUNCATED)	1	AI Security, Red Teaming & Defense 2026 Research	Explosive (>50/mo)	208	2026-08-13T17:26:29.657603

End of preview. Expand in Data Studio

YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

👑 Complete 2026 AI Engineering & Research Master Suite (10-in-1 Mega-Bundle)

The definitive all-access research intelligence repository featuring 13,246 domain-verified research papers and 2,500+ official code repositories spanning all 10 core AI domains: Code Generation (SWE-bench), Real-Time Voice Agents (<200ms), Robotics & Embodied AI, Multi-Agent Swarms, Enterprise RAG, Medical AI, Multimodal Vision-Language, AI Security & Red-Teaming, Crypto AI Agents, and LLM Quantization (2023–2026).

Built with Universal Scientific Engine V18.5 Diamond, providing 32 unified master attributes with verified repository attribution, domain categorization, and native 384-dimensional dense PyTorch embeddings.

📊 Master Dataset Schema Highlights

Field	Type	Description
`paper_id`	String	Unique ArXiv identifier
`title`	String	Research paper title
`domain_dataset_origin`	String	1 of 10 Core AI Engineering Domains
`cluster_topic_name`	String	Domain-specific topological semantic cluster
`academic_citations_count`	Integer	Total academic citations
`influential_citations_count`	Integer	Influential citation impact index
`field_weighted_citation_impact_fwci`	Float	Field-weighted citation impact
`commercial_ip_safety_score`	Integer	0–100 commercial compliance index
`primary_repo_url`	String	Verified official code repository URL
`github_stars`	Integer	Live GitHub stargazers count
`tldr_neural_summary`	String	Executive neural summary of innovation
`title_vector_384d`	List[Float]	384d PyTorch embedding (`all-MiniLM-L6-v2`)
`abstract_vector_384d`	List[Float]	384d dense contextual PyTorch embedding
`reproduction_recipe`	String	1-line bash reproduction command

🌐 10 Core AI Domains Unified in the Master Suite

AI Code Generation & SWE Agents 2026 (3,181 papers)
Robotics & Embodied Physical AI 2026 (2,123 papers)
Audio, Speech & Real-Time Voice Agents 2026 (1,722 papers)
Autonomous AI Agents & Swarms 2026 (1,000 papers)
Enterprise RAG & Vector Search 2026 (1,000 papers)
Multimodal Vision-Language & Video Models 2026 (1,000 papers)
Medical AI & Clinical Foundation Models 2026 (1,000 papers)
AI Security, Red Teaming & Defense 2026 (1,000 papers)
Crypto, Web3 & Autonomous Financial Agents 2026 (1,000 papers)
LLM Fine-Tuning & Quantization 2026 (1,000 papers)

📊 Interactive Master OpenAngels Visual Hub Included

Open MASTER_DATASET_ANALYTICS_DASHBOARD_1000_SAMPLE.html directly in your browser (Chrome/Edge/Safari) to explore the 1,000-paper interactive visual intelligence directory with cross-domain filtering, live metrics, and instant search.

💻 1-Click Python Quickstart

import pyarrow.parquet as pq

# Load 1,000-Sample Stratified Master Teaser
table = pq.read_table("COMPLETE_2026_AI_ENGINEERING_RESEARCH_MASTER_SUITE_1000_SAMPLE.parquet")
df = table.to_pandas()

print(f"Loaded {len(df)} sample master papers across 10 domains.")
print(f"Top Paper: {df['title'].iloc[0]}")
print(f"Domain: {df['domain_dataset_origin'].iloc[0]}")
print(f"Code Repo: {df['primary_repo_url'].iloc[0]} ({df['github_stars'].iloc[0]}★)")

🚀 Get the Full 13,246-Paper Enterprise Master Suite

The complete commercial production master suite (13,246 papers in 52 MB Parquet with 384d vectors, 259 MB SQLite DB, Clean CSV, Interactive Master OpenAngels Visual Hub, and JSON) is available here:

👉 BeatsProm Complete 2026 AI Engineering Master Suite (10-in-1 Mega-Bundle)

Downloads last month: 39