🌖 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.