"A tool for finding relevant academic publications and open-source projects on GitHub" matching MCP servers:

Enables an AI agent to reverse-engineer a compiled program with Ghidra — inspecting decompiled code, instructions, callers, memory bytes, and strings — without any source code. Every reply is labelled as fact, computed, or guess, and each claim traces back to hash-checked saved evidence.

Enables AI assistants to natively execute and manage 600+ Kali Linux security, penetration testing, and forensic tools, including search, help documentation, structured scanners, and workspace management.

MCP server that detects and guards against tool poisoning and prompt injection attacks in tool descriptions and schemas. It provides risk scoring, pattern detection, safe rewriting, and audit reports with zero external API cost.

Enables AI clients to audit GitHub repositories for Dependabot, code scanning, and secret scanning alerts, generate conservative remediation plans, and execute verified fixes through MCP tools with fail-closed mutation controls.

An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

This MCP server enables users to sign in with GitHub and use AI agents to scan entire repositories for leaked secrets such as API keys, passwords, and tokens, delivering a report with the exact file and line plus remediation guidance.

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

Enables static AST-based dataflow analysis to trace untrusted sources to sensitive sinks, supporting automated code review and security auditing through MCP.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

Enables static AST-based taint flow tracing from untrusted sources to sensitive sinks, supporting AI code review and security analysis via MCP.

GDB Enhanced Features MCP server for remote debugging and CTF exploitation with 41 specialized tools including ROP search, format-string detection, and memory patching.

AI-powered security scanning MCP server that exposes 25+ professional tools, enabling penetration testing and security assessments through natural language interaction with AI agents like Claude Desktop.

Cyber Host Artificial Intelligence (C.H.A.I) An autonomous penetration testing MCP (Model Context Protocol) server with an integrated AI decision engine, multi-provider LLM support, and an extensible plugin architecture.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

This server enables AI agents to delegate GitHub pull request diffs for automated security auditing, returning structured vulnerability and architecture feedback while routing requests through a resilient multi-provider fallback pipeline.

Enables LLMs to perform automated penetration testing and Active Directory reconnaissance through Mythic, with tools for executing PowerShell, AD recon, domain user enumeration, and Kerberoasting.

Enables agents and operators to audit MCP tool permissions for least privilege, score blast radius, detect privilege-escalation chains, verify capability matrices, and produce cryptographic audit proofs with injection fuzzing and exfiltration canaries.

Enables real-time injection fuzzing, exfiltration canary detection, least-privilege tool sandboxing, and cryptographic audit proofs through the Model Context Protocol.

An MCP server that scans local codebases for quantum-vulnerable cryptography (secp256k1, Ed25519, RSA, etc.) and CI signing commands, classifying each finding as quantum-broken, post-quantum, or neither. It runs entirely locally with no network calls, providing a deterministic inventory for AI agents.

MCP security firewall (stdio): vet advertised tool definitions through static-scan → threat-feed → origin → pinning before they reach the model. Zero npm runtime deps. No secrets.

SkillTotal is a local stdio MCP server that lets an agent check a component before installing it. It statically analyzes MCP servers, agent skills, npm/PyPI packages and repos for supply-chain risks, dangerous capabilities (shell, filesystem, network, credentials), prompt-injection surfaces like tool poisoning, and exfiltration paths. Each finding cites file, line and snippet.

Enables AI agents to perform passive security scans on domains, checking email spoofing (DMARC/SPF/DKIM), TLS weaknesses, security headers, exposed files, and subdomain-takeover risk without needing an API key.

An MCP server that lets an AI agent probe a live URL and confirm whether sensitive files (e.g., .git, .env, source maps) are genuinely served by fetching and validating the content, avoiding false positives.

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.

Enables LLMs to access realtime CVE intelligence aggregated from NVD, CISA KEV, EPSS, GitHub advisories, PoC discovery, and Metasploit/Nuclei tooling, providing vulnerability details, exploitation signals, and prioritized triage verdicts.

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.

MCP server for auditing infrastructure-as-code attack paths, finding multi-hop chains from public internet to sensitive resources.

Enables AI coding assistants to scan projects for security issues such as leaked API keys, missing Supabase RLS, open Firebase rules, unauthenticated routes, and hallucinated packages, then fix and verify the results.

Open behavioral litmus for MCP servers — grades A–F across tool-output injection, egress, sensitive-data, and adversarial-input, with reproducible, content-addressed evidence. Tools: run_litmus, verify_attestation.

Connects MCP clients to pre-submission review that argues against a draft bug-bounty report or smart-contract finding the way a triager would, tying every claim to a supplied file and line and returning a submit, rewrite-then-submit, prove-first, hold-duplicate or drop verdict. Exposes tools to list review profiles, prepare reviews for the agent's own model, build hash-verified review packets, and — with a connection token — run hosted profiles on your own provider key.