Security / AI Red Teaming
A hands-on kit for authorized red teaming of locally run LLMs, with jailbreak techniques, prompt-injection scenarios, streaming attack visualization, and support for OpenAI-compatible local model servers.
The page clearly describes a real AI-security project with a specific use case: testing jailbreaks and agent prompt injection against locally run, OpenAI-compatible models. The README gives enough detail for a public listing, including supported model families, quick-start steps, architecture notes, and a concrete explanation of what the lab demonstrates.
The snapshot does not show a demo, screenshots, or release history, so a visitor cannot tell how polished the UI is or how actively maintained the project is beyond the README and commit count.
Last evaluated 81 days ago. Current rank #898. Down 3 spots in the rankings.
Open-source security scanner for AI agents, MCP servers, and agent skills. It auto-discovers installed agent components and checks them for prompt injection, tool poisoning, secrets, malware payloads, and related risks.
An AI harness for long-running penetration tests, bug bounties, and code audits. It combines specialist agents, a Kali runtime, durable state, and second-agent verification for findings.
Open-source security agent framework for cloud, code, and runtime. It ships 45 built-in agents for AWS, GCP, Azure, Kubernetes, GitHub, and GitLab, and lets users create custom agents in a single markdown file.
Self-hosted AI security testing platform for authorized pentesting and bug bounty workflows, with a local web UI, live agent telemetry, verified findings, and branded PDF reports.
