AI safety evaluation framework testing LLM epistemic robustness under adversarial self-history manipulation

Benchmark LLM jailbreak resilience across providers with standardized tests, adversarial mode, rich analytics, and a clean Web UI.

A multi-agent safety engineering framework that subjects systems to adversarial audit. Orchestrates specialized agents (Engineer, Psychologist, Physicist) to find process risks and human factors.

LLM-powered fuzzing and adversarial testing framework for Solana programs. Generates intelligent attack scenarios, builds real transactions, and reports vulnerabilities with CWE classifications.

A dependency-aware Bayesian belief gate that resists correlated evidence and yields only under true independent verification.

Red team toolkit for stress-testing MCP security scanners — find detection gaps before attackers do

Generate adversarial pytest tests using LLM. Tries to find edge cases in your Python code.