"A guide to coding a monorepo" matching MCP servers:

Enables evidence-first binary and firmware analysis through MCP, exposing tools to initialize projects, analyze artifacts, query structured claims, export results, and run evaluations.

A comprehensive MCP server for Frida dynamic instrumentation, enabling AI agents to manage devices, processes, scripts, memory, and ADB operations.

Connects AI assistants to a Kali Linux terminal for executing security tools, managing interactive shell sessions, and performing penetration testing, CTF challenges, and security research tasks.

Enables AI agents to run bounded security reconnaissance tools against a local OWASP Juice Shop target via MCP, including HTTP checks, header inspection, Nmap scanning, and web enumeration, without granting arbitrary shell access.

Agent-native "safe to ship?" security gate for AI-generated code. Uses real parsers and inter-rocedural taint analysis (JS/TS, Python, Go) to flag the classes AI coding agents get wrong — secrets, SQL injection, SS, SSRF, path traversal, command injection, weak JWT/CORS — and ranks findings by confidence. Exposes a scan tool over MCP.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

Enables passive security scanning of HAR files from QA test automation to detect missing security headers, insecure cookies, CORS misconfigurations, and leaked secrets, without making any live requests.

Enables AI coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.

A production-ready MCP server that wraps Nmap to enable AI agents to perform automated network security assessments, including port scanning, host discovery, service detection, OS fingerprinting, and vulnerability scanning.

Provides Claude Code with access to a comprehensive bug bounty knowledge base including techniques, payloads, wordlists, and real-world reports through 14 tools for searching, retrieving payloads, and assessing report quality.

Two-way Burp Suite MCP bridge enabling AI agents to capture traffic, analyze endpoints, queue scans, and send findings back to Burp.

Enables AI assistants to scan websites, public repositories, and OpenClaw/CLAW skills for security vulnerabilities, including local skill-package analysis before installation, cloud scans, and remediation guidance.

Enables users to retrieve and display CVE vulnerability information from the National Vulnerability Database (NVD) with support for keyword search and detailed lookup.

A local MCP server that scans repository dependencies for known vulnerabilities (CVEs) using OSV.dev, enriches findings with NVD and CISA KEV data, and supports triage, remediation, and accepted risk management directly from an AI coding assistant.

Read-only MCP server for precise lookup, search, and versioned retrieval of OWASP testing guides (WSTG, MASTG, ISTG, AITG) with full provenance on every result.

A security scanner for AI coding agents and autonomous assistants that scans code for vulnerabilities, detects hallucinated packages, blocks prompt injection, and provides LLM-powered semantic code review via MCP or CLI.

An intentionally vulnerable MCP server for security training, simulating a fictional company with 28 vulnerable tools and 38 challenges to learn AI agent attack and defense.

MCP server for AI-driven VAPT orchestration, enabling agents to plan and execute authorized security scans through a control plane that enforces scope, sanitization, budget, rate limits, human approval, and audit logging.

Provides access to Shodan API functionality, enabling AI assistants to query information about internet-connected devices for cybersecurity research and threat intelligence.

An authorization-first MCP-governed control plane for executing security-tool workflows in explicitly approved, sandboxed environments with auditable campaigns and AI collaboration boundaries.