"JetBrains YouTrack issue and project tracking tool" 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.

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.

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

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 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 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.

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

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.

Enables AI agents to submit npm, PyPI, Go, or Maven packages and manifests to the free OSV.dev database and receive per-package and per-advisory results with CVE mappings, CVSS scores, severities, and the versions that fix each issue. It also supports filtering by minimum severity, de-duplicating alias advisories, and machine-readable error/warning rows for CI gates and upgrade recommendations.

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.

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.

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.

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.

Enables AI agents to check industrial and IT devices by vendor, product, and firmware version, returning affecting CVEs, fixes, vendor advisories, fix plans, and change tracking through read-only tools.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Security scanner for MCP servers: detects tool poisoning, hidden instructions, tool shadowing, excessive permissions, leaked secrets and weak transports before you install a server.

Provides security audit primitives for MCP servers, agent tool schemas, and system prompts, along with pentest utilities like JWT inspection and HTTP diffing.

Enables AI assistants to run Offensive360 SAST scans on local codebases, returning security findings with file/line, severity, and fixes, plus scan status tracking.

Enables MCP clients to work with DefAudit, supporting project and scan listing, starting scans, retrieving results, and marking or unmarking false positives.

Scans MCP servers for prompt-injection, tool-poisoning, and SSRF vulnerabilities using 30+ canonical rules across 5 severity tiers, with optional signed safety reports for procurement.

Enables AI assistants to run read-only exposure scans against MCP servers that front databases, surfacing unauthenticated access, role/tool leaks, write-capable tools, and locally stored credentials, then returning plain-language findings.

Chrome browser JS reverse engineering MCP tool for CTF/Web security, enabling dynamic analysis and restoration of login encryption logic.

Enables AI assistants to perform domain reconnaissance using theHarvester OSINT tool, including email harvesting, subdomain discovery, DNS brute force, and Shodan integration, via SSH on a Kali Linux host.

Security scanner for MCP servers that detects tool poisoning, shadowing, and other risks via static rules and semantic analysis. Exposes the scan_mcp_server tool for scanning MCP servers over stdio.

AI-powered penetration testing reasoning engine (MCP server) for attack path planning, step scoring, and tool recommendations using Beam Search and Monte Carlo Tree Search.

An MCP server that provides safeguard capabilities to protect against prompt injection and unsafe tool calls.

Enables security inspection of MCP tool metadata by analyzing tool names, descriptions, and input schemas, and returning SAFE, REVIEW, or HIGH RISK verdicts through the analyze_tools tool.

Enables AI assistants to perform deep security audits of Java/Spring projects by searching for dangerous sink methods across built-in vulnerability profiles, inventorying project structure and dependency versions, and assessing the real exploitability of vulnerable components by layer. Results are grouped and tagged by external exposure so audits stay focused without overloading context.