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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”

3 starsPython

Mcpsec-A multi-agent SEC gate for MCP toolchains (scan →harden →rescan)

by Yuvraj_exe·Feb 19, 2026·1 point·0 comments

AI Analysis

●●SolidBig BrainBold Bet

MCP-specific security scanning with LLM-powered attack simulation, but assumes MCP adoption maturity that doesn't exist yet.

Strengths
  • Identifies genuine new attack surface: over-privileged tools, prompt injection via tool schemas, dangerous chaining—real problems that traditional scanners miss
  • Multi-agent architecture (inventory → intel → probe → policy) maps cleanly to security workflow; attack simulation corpus shows thoroughness
  • Dual mode (deterministic scanner + LLM copilot) lets teams adopt without AI dependency; CI gate + before/apply/after reports are audit-friendly
Weaknesses
  • Timing risk: MCP ecosystem is still nascent; this solves a problem many teams don't yet know they have or won't face for 6+ months
  • No public benchmarks or adversarial test cases; unclear if LLM probe agent actually finds novel vulnerabilities vs. regex + known patterns
Category
Target Audience

MCP toolchain operators, AI/LLM platform engineers, security-conscious DevOps teams

Similar To

Snyk · Trivy · CodeQL

Post Description

Hi HN,

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.

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