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.

Jiutian-Zhiwei is an Agent system tailored for intelligent Operations and Maintenance (O&M).

This is an Agent system for intelligent operations and maintenance.

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.

Proof-governed AI agent for PI-Bench with deterministic fail-closed tool execution, policy-constrained actions, final-decision enforcement, and hash-chained execution receipts.

QUBO/Ising → candidate → Z3 final authority → execution

DSG Control Plane policy normalization → Ising/QUBO candidate → Z3 formal verification → DSG deterministic execution gate → ALLOW / DENY / ESCALATE

Proof-governed purple agent for PI-Bench with deterministic fail-closed tool gating and optional DSG Cinema production preflight

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