"GitHub Project or Tool Related to Cursor Functionality" matching MCP servers:

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 providing diagnostic tools to analyze software architecture, security, REST API compliance, and dependencies for multiple programming languages. Enables AI agents to run scans and audits on codebases.

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.

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.

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.

Free deterministic security scan of a public git repository (GitHub, GitLab, Codeberg, Bitbucket): vulnerable dependencies via OSV.dev, secret patterns, and config lint, returned as structured JSON. Tools: scan_repository(url), audit_pricing(). Runs locally over stdio (python3 web/mcp_stdio.py or the Dockerfile) and is also hosted at https://project-feldspar.com/mcp. MIT licence, stdlib-only. Buil

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 AI agents to conduct authorized penetration tests by providing scope-enforced access to Metasploit Framework's RPC, with tools for reconnaissance, exploitation, session management, and post-exploitation.

Enables autonomous AI agents and penetration testers to conduct authorized security audits with persistent cross-session memory, zero-trust secret scrubbing, dynamic OWASP/ASVS checklists, and hallucination-free exploit PoC generation from captured traffic.

Wraps Kali Linux penetration testing tools for authorized security assessments, driven by a local LLM via LM Studio.

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 security analysts to carry structured investigation state across sessions by storing typed observations, hypotheses, tasks, evidence and versioned decisions in a local SQLite graph with full-text search. It also enforces authorized scope and request controls, redacted and content-hashed evidence, duplicate-test signatures and validation gates that leave findings in draft until reproducibly confirmed, then produces technical and executive reports for Antigravity, Codex and other stdio MCP clients.

Enables scanning of Claude Code skills, plugins, or MCP servers for malware before installation via static analysis.

Enables AI agents to access VirusTotal intelligence through MCP, supporting file, URL, domain, and IP lookups, plus analysis, via remote HTTP or local stdio.

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.

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.

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.

Enables users to scan MCP servers for security threats, check installed servers, and analyze config files for risks, all from AI assistants like Claude, Cursor, or Windsurf.

Provides a generic HTTP client tool allowing AI agents to make arbitrary HTTP requests from their environment, with support for environment-variable placeholders for secrets.