Secure SDLC Agents for Claude and Cursor (MCP)
Eight specialist agents catch what Claude Code misses, but it's prompts not actual code analysis.
A multi‑agent security copilot that inventories MCP servers/tools, correlates them with vulnerability intelligence, and tests for prompt‑injection/tool‑misuse paths—producing an auditable “agentic attack surface report”
MCP-specific security scanning with LLM-powered attack simulation, but assumes MCP adoption maturity that doesn't exist yet.
MCP toolchain operators, AI/LLM platform engineers, security-conscious DevOps teams
Snyk · Trivy · CodeQL
I built MCPSEC, a security gatekeeper for MCP (Model Context Protocol) toolchains.
It scans MCP configs, correlates vulnerability intel (OSV / GHSA / NVD), simulates tool abuse with an LLM-based probe agent, generates a policy + patch plan, applies hardening, then re-scans and gates CI on the final risk score.
The design is intentionally agentic: - Inventory agent: parses MCP configs - Intel agent: pulls vuln data (OSV / GHSA / NVD) - Probe agent (LLM, optional): generates adversarial tool abuse prompts - Policy agent (LLM, optional): turns findings into concrete config changes - Orchestrator: merges results, scores risk, writes reports, applies patches
You can run it locally as a CLI or drop it into CI as a GitHub Action: - It produces before/apply/after reports as artifacts - It can fail PRs if the final risk score stays above a threshold - Without an LLM token it works as a deterministic scanner; with one it becomes a true “security copilot”
Repo: https://github.com/yuvrajgitwork/MCP-toolchain-security-GK Demo workflow: scan → apply → rescan → lower score
I built this because MCP toolchains are becoming powerful and over-privileged very quickly, and there’s basically no security gate for them yet.
Would love feedback from folks working in AI infra / security.
Eight specialist agents catch what Claude Code misses, but it's prompts not actual code analysis.
Bundles CI-friendly scanners that target agent-specific risks: 17 patterned secret detectors, prompt-injection and instruction‑malware heuristics, tool/SSRF and MCP auth checks, plus SARIF/JSON outputs for integration. Findings map to the OWASP Top 10 for Agentic Applications (2026) and it adds 'harden' profiles to apply safer defaults to OpenClaw/MCP installs — practical, focused ops tooling rather than a generic secret-finder.
Fills the EDR blind spot for AI tooling — timely and actually useful today.
ESLint for agent context files stops drift before it burns tokens and breaks workflows.
Policy enforcement layer stops AI agents from deleting files or leaking credentials—no prompt retraining needed.
Dual-network Docker isolation keeps AI agents from escaping the workspace.