LLM Adversarial Security Arena — Jailbreak → Detect → Defend → Verify

Elenchus MCP Server - Adversarial verification system for code review

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.

Adversarial MCP server benchmark suite for testing tool-calling security, drift detection, and proxy defenses

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

A governance doctrine for AI systems based on explicit oversight. Externalizes trust and uncertainty into auditable, adversarial, and constrainable layers. A design framework, not an implementation guide.

Analysis of ChatGPT-5 reviewer failure: speculative reasoning disguised as certainty. Captures how evidence-only review drifted into hypotheses, later admitted as review-process failure. Includes logs, checksums, screenshots, and external video.

Description URF Application Stress Test — adversarial and scalability tests for Unified Rigidity Framework applications, validating limits under load, noise, and edge cases.

Domain-expert evaluation framework for AI judgment quality in healthcare investing

Investigation into ChatGPT-5 reviewer misalignment: PDF claimed screenshots as evidence, but assistant denied their visibility. Includes JSONL + human-readable logs, screenshots, checksums, and video. Highlights structural risks in AI reviewer reliability.

Adversarial testing of LLMs on constraint satisfaction deadlocks

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

Adversarial testing and robustness evaluation for the Crucible framework

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

Extremely hard, multi-turn, open-source-grounded coding evaluations that reliably break every current frontier models (Claude, GPT, Grok, Gemini, Llama, etc.) on numerical stability, zero-allocation, autograd, SIMD, and long-chain correctness.

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

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.

API for generating LLM bot/agent personalities based on the Big Five personality model.

Red-team framework for discovering alignment failures in frontier language models.