title: README
emoji: 🌖
colorFrom: gray
colorTo: red
sdk: static
pinned: false
🌖 Durinn — AI Security
Durinn builds AI security infrastructure for high-assurance and regulated environments.
Our work focuses on calibration, dataset poisoning detection, and
neuro-symbolic vulnerability analysis for safer, more predictable agents.
We contribute research datasets, calibration tools, and security-focused evaluation
pipelines designed for GxP, healthcare, and enterprise LLM deployments.
🧪 Research Focus
Our work spans:
- Calibration of high-stakes LLM security classifiers
- Prompt-injection detection
- Dataset poisoning defense
- Neuro-symbolic vulnerability scoring
- Evaluation and benchmarking for regulated AI systems
Our Hacktoberfest-derived dataset supports real-world model calibration and
has demonstrated meaningful improvements when applied to production-grade PI classifiers.
🧭 Agent Safety, Guardrails & Calibration
Durinn calibrates state-of-the-art prompt-injection classifiers, including models
widely deployed in production security pipelines.
Calibration improves:
- Detection of subtle prompt injections
- Threshold placement (better true-positive recovery)
- Agent stability and predictability
- Decision-level robustness for regulated environments
These calibrated guardrails can be deployed in:
- Internal inference pipelines as an agent heartbeat
- AIDR / SOC / cloud platforms enhancing their LLM input-security layers
🧬 Dataset Poisoning & Model-Integrity Defense
Our work includes:
- Poisoning detection in training and inference datasets
- Checkpoint tampering & backdoor forensics
- Model-integrity drift analysis
- Provenance and chain-of-custody guidance for regulated AI stacks
We emphasize verifiable integrity for teams who cannot rely on opaque model behavior.
🔐 Neuro-Symbolic Vulnerability Detection
Durinn develops hybrid detection approaches that combine:
- Symbolic signals from program analysis
- LLM reasoning
- Safety-critic scoring
- Calibrated confidence thresholds
This architecture improves reliability without altering underlying model weights.
📚 Key Repositories
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.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.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.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.
Durinn — Secure, calibrated, and trustworthy AI for environments where accuracy and integrity matter.