AI Security / Agent Red-Teaming
A deterministic security scanner and adversarial flight simulator for AI agents, built for red-teaming LLM workflows and MCP-compatible setups.
The page clearly describes an AI security tool with a specific use case: adversarial testing and red-teaming of autonomous AI agents. The snapshot shows concrete features, benchmark results, supported MCP integration, multiple simulated tools, and a visible repository structure with docs, tests, examples, and results, which is enough for a useful public listing.
The snapshot does not show installation steps, a quickstart, or enough code-level examples to judge how easy it is to run locally. It also doesn’t expose the exact interface for the MCP server or how results are consumed in practice.
Last evaluated 11 days ago. Current rank #1549. Down 2 spots in the rankings.
Free hands-on labs for prompt injection and LLM security training, with real models, a context trace view of assembled prompts, and a multi-level Gauntlet challenge.
A security product for Claude Code that reviews application architecture inside the IDE via MCP, with contextual assessments, prioritized mitigations, and continuous re-checks as the codebase changes.
A Reco security research article showing an AI-powered agent that maps Salesforce Experience Cloud sites, probes exposed objects and Apex methods, and attempts autonomous exploitation to find data exposure.
