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title: README |
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emoji: 🌖 |
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# 🌖 Durinn — AI Security |
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Durinn builds **AI security infrastructure** for high-assurance and regulated environments. |
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Our work focuses on **calibration**, **dataset poisoning detection**, and |
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**neuro-symbolic vulnerability analysis** for safer, more predictable agents. |
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We contribute research datasets, calibration tools, and security-focused evaluation |
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pipelines designed for GxP, healthcare, and enterprise LLM deployments. |
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## 🧪 Research Focus |
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Our work spans: |
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- Calibration of high-stakes LLM security classifiers |
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- Prompt-injection detection |
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- Dataset poisoning defense |
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- Neuro-symbolic vulnerability scoring |
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- Evaluation and benchmarking for regulated AI systems |
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Our Hacktoberfest-derived dataset supports real-world model calibration and |
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has demonstrated meaningful improvements when applied to production-grade PI classifiers. |
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## 🧭 Agent Safety, Guardrails & Calibration |
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Durinn calibrates state-of-the-art prompt-injection classifiers, including models |
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widely deployed in production security pipelines. |
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Calibration improves: |
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- Detection of subtle prompt injections |
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- Threshold placement (better true-positive recovery) |
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- Agent stability and predictability |
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- Decision-level robustness for regulated environments |
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These calibrated guardrails can be deployed in: |
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- **Internal inference pipelines** as an agent heartbeat |
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- **AIDR / SOC / cloud platforms** enhancing their LLM input-security layers |
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## 🧬 Dataset Poisoning & Model-Integrity Defense |
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Our work includes: |
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- Poisoning detection in training and inference datasets |
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- Checkpoint tampering & backdoor forensics |
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- Model-integrity drift analysis |
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- Provenance and chain-of-custody guidance for regulated AI stacks |
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We emphasize **verifiable integrity** for teams who cannot rely on opaque model behavior. |
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## 🔐 Neuro-Symbolic Vulnerability Detection |
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Durinn develops hybrid detection approaches that combine: |
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- Symbolic signals from program analysis |
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- LLM reasoning |
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- Safety-critic scoring |
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- Calibrated confidence thresholds |
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This architecture improves reliability without altering underlying model weights. |
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## 📚 Key Repositories |
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- **`durinn-calibration`** — Tools and experiments for calibrating security-critical classifiers, including prompt-injection detectors and safety-critic models. Contains evaluation scripts, threshold-optimization utilities, and datasets for benchmarking calibrated decisions in regulated AI environments. |
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- **`durinn-sandbox`** — A high-assurance execution environment for analyzing model behavior, running controlled adversarial tests, and validating agent outputs. Provides reproducible sandboxes for measuring failure modes, safety drift, and poisoning-related anomalies. |
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- **`durinn-agent-infrastructure`** — Shared infrastructure components for constructing and evaluating secure AI agents. Includes model wrappers, risk-scoring pipelines, input-validation hooks, telemetry collection, and integration utilities for enterprise inference stacks. |
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- **`durinn-ai-code-remediation`** — Research agent for neuro-symbolic vulnerability detection and compliant secure-rewrite workflows. Designed for GxP and regulated industries requiring traceability, safety justification, and audit-aligned remediation artifacts. |
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**Durinn — Secure, calibrated, and trustworthy AI for environments where accuracy and integrity matter.** |
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