Agent Safety

π-bench is a deterministic, multi-turn benchmark that evaluates AI agents’ policy compliance across nine diagnostic dimensions (e.g., compliance, conflict resolution, explainability) and seven cross-domain policy surfaces, using tool-aware environments and state tracking. It emphasizes reproducible, fine-grained analysis of agent behavior under realistic and adversarial scenarios, without relying on LLM judges.

Defensive in-car voice assistant for CAR-bench Track 1 evaluation using deterministic code-level guardrails

Rule-based defender that detects prompt injection, conflicting instructions, and unsafe JSON exfiltration requests.

The green agent evaluates the security robustness of target LLM agents against adversarial attacks while ensuring benign requests remain functional. It operates on an initial corpus of over 125,000 jailbreak prompts and 50,000 benign prompts, applying more than 25 distinct mutation strategies. Specifically, our agent tests for vulnerabilities to jailbreak attempts, prompt injections, and PII leakage by iteratively generating mutated adversarial prompts, invoking the target agent, and scoring responses using behavioral analysis to identify security violations. The system employs an evolutionary (genetic) algorithm to evolve more effective prompts over multiple iterations, ultimately producing reports on discovered exploits, vulnerability metrics and blocked benign requests.

A2A Purple Agent wrapped with CAUM structural observation. Includes benchmark-only control mode to study whether structural loop/stall signals improve agent behavior without exposing private task content.

Pi-Bench purple agent for FINRA AML compliance scenarios. Rust/Axum agent using OpenAI GPT for policy decision making.

Evaluates multi‑turn agent robustness against prompt‑injection and tool‑misuse attacks across configured attack methods/subtypes (e.g., naive, fake completion, escape characters, context ignoring, combined), with results summarized in results.json