Shannon is an AI pentester for web applications and APIs. It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.
Copy/paste detector for source code. 220+ languages, Rust engine, SARIF/HTML/badge reporters, GitHub Action, MCP server for AI agents.
π¦ MegaLinter analyzes 50 languages, 22 formats, 21 tooling formats, excessive copy-pastes, spelling mistakes and security issues in your repository sources with a GitHub Action, other CI tools or locally.
The independent security agent for AI-written software. Finds issues, investigates whether they are real, and shows you the evidence. Deterministic core, no API key needed, JSON and SARIF output.
Agentic SDLC infrastructure. Define guardrails. Collect signals. Enforce continuously. Signed records of what your AI coding agents and CI/CD pipelines did, checked against policies as code, with the loop closed in CI/CD. AI sessions, SBOMs, VEX, SARIF and SLSA provenance, built on Sigstore and in-toto. Apache 2.0.
π‘οΈ Security audit CLI for Model Context Protocol (MCP) servers β scan AI agent configs for tool poisoning, rug pulls, hardcoded secrets, command injection & supply-chain risks. Pure Python, SARIF + CI ready.
AI red-team platform. Autonomous LLM agents run a penetration test end to end inside a Kali container and write the report. LangGraph plan/act engine, provider-agnostic models via LiteLLM, PDF/JSON/SARIF output. FastAPI + React.
Evidence-grounded cryptographic inventory and post-quantum migration planning for software repositories. Local static analysis, zero mandatory runtime dependencies, SARIF and CycloneDX output.
Lint, search, and install Agent Skills (SKILL.md) β CLI + MCP server + lint engine
Evaluator-neutral AI evaluation evidence, baseline regression gates, and JSON, JUnit, and SARIF reports for CI.
Security testing that runs inside the coding agent you already use. Source-available, not open source.
Global-readiness toolkit for international forms across HTML, JSX, Vue, Svelte, and CI.
A multi-agent application-security review harness for Claude Code (and, later, other AI agents). One router skill dispatches to a full offensive-security pipeline that maps a codebase, hunts vulnerabilities with a per-class knowledge base, chains findings into escalations, verifies real impact, and reports to README/JSON/SARIF/doc/PDF.
Help your agents find the smoking gun they're looking for. Optimization evidence for agents: find complexity hotspots.
Static Ansible linting in Go: the 38 ansible-lint rules decidable from YAML alone, byte-for-byte pep8 output, no Ansible runtime.