The pentest-ai repository (0xSteph/pentest-ai) focuses on: Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path.. It belongs in this directory only insofar as it supports MCP and tool-calling integration, developer-centric engineering workflows, security and compliance automation, team collaboration integrations in AI products, agent systems, or developer tooling.
License
MIT
Stars
1,215
Homepage
https://pentestai.xyz/Features
- GitHub description for pentest-ai: Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path.
- pentest-ai is relevant for assessing security checks, risk detection, or compliance automation.
- pentest-ai shows how external tools or MCP-style capabilities may connect around the project.
- pentest-ai helps evaluate coordination, planning, or task-decomposition patterns in agent systems.
- pentest-ai lists MIT license metadata; review obligations before redistribution or hosted use.
- pentest-ai has about 520 GitHub stars in the local metadata snapshot.
Use Cases
- Test pentest-ai when the need is security and compliance workflows and the repo summary matches: Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-...
- Compare pentest-ai's implementation approach before committing to an internal build.
- Use pentest-ai to review security automation ideas against a working repository.
- Use pentest-ai to connect tool-enabled agent workflows to the repository capability.
- Use pentest-ai to test agent coordination patterns with a concrete open-source codebase.
- Complete a MIT license review before packaging pentest-ai into a commercial or hosted workflow.
FAQ
Start from the repository summary (Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path.), then verify maintenance status, integration boundaries, and whether its security and compliance workflows, MCP and tool-calling integration, agent orchestration focus matches the intended workflow. Repository: https://github.com/0xSteph/pentest-ai. Stars: about 520. License: MIT.
pentest-ai is best treated as a repository-level component or reference implementation for security and compliance workflows, MCP and tool-calling integration, agent orchestration. Good evaluation scenarios include: Test pentest-ai when the need is security and compliance workflows and the repo summary matches: Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-... Compare pentest-ai's implementation approach before committing to an internal build. Use pentest-ai to review security automation ideas against a working repository.
