"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

A Docker-based MCP server exposing a curated set of Kali Linux security tools for authorized, hands-on network scanning on private ranges, with enforced trustworthiness and honest output.

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.

An MCP server that runs curated adversarial prompts against local Ollama models to test guardrails, scoring responses with heuristic verdicts for human review.

Enables AI models to search and retrieve offline security knowledge from over 20 curated sources including HackTricks, PayloadsAllTheThings, and OWASP guides, via the Model Context Protocol.

MCP server that wraps the Frida dynamic instrumentation toolkit, allowing users to attach to processes, hook functions, enumerate modules and exports, and manage scripts through natural language.

Read-only security audit for Supabase. Surfaces RLS gaps, permissive policies, exposed functions and grants, and returns scored findings with fix SQL that is never executed.

Enables large language models to read and write IDA Pro databases through structured tool calls for reverse engineering, binary analysis, and hook development. Provides broker-based routing, SQLite-backed static caching, multi-instance analysis, and both cached and live IDA API queries.

MCP server for security-testing AI models, exposing garak vulnerability scanning and URL/MCP-endpoint scanning as callable tools. It lets agents probe LLM endpoints for jailbreaks, prompt injection, and data leakage, and classify URLs or screen remote MCP endpoints for prompt-injection payloads.

Bridges large language models with the Cobalt Strike C2 framework, enabling AI assistants to control adversary simulation workflows through natural language.

Provides a bridge between large language models and the Metasploit Framework, enabling AI assistants to access and control penetration testing functionality through natural language.

Enables AI coding agents to audit Supabase or Postgres databases for Row Level Security and multi-tenant isolation holes, reading tables, grants, policies and functions for static findings and optionally probing those policies inside a rolled-back transaction. It reports each hole with an explanation and a fix, so an agent can verify the migrations it just wrote before declaring them done.

Enables querying current and historical exploitation-probability scores and percentiles for CVEs, discovering candidates by score bounds or recent dataset addition, and assigning remediation tiers by combining likelihood with caller-supplied CVSS severity. It also explains score interpretation, model transitions, and coverage conventions.

An MCP server that audits installed MCP servers, revealing their credentials, tool capabilities, and security risks. It enables users to assess and monitor the access granted to AI models.

Enables AI assistants to drive OWASP ZAP for authorized penetration testing and bug-bounty workflows, including authenticated scans and vulnerability triage through 67 curated safety-gated tools.

Enables professional penetration testers to orchestrate reconnaissance, web scanning, brute-force testing, exploitation, traffic capture, hash cracking, wordlist building, privilege escalation audits, and client report generation via STDIO/HTTP/SSE transports with optional bearer/OIDC authentication.

Enables AI assistants and security analysts to inspect PCAP/PCAPNG captures through MCP tools or REST endpoints, reassembling TCP/UDP streams, extracting payload artifacts, detecting CTF flags, credential leaks and network anomalies, and generating formal PDF or Markdown forensic reports. Every finding is bound to exact frame numbers, timestamps and endpoints with SHA-256 deduplicated evidence provenance.

A harness for building test MCP servers that reproduce common attack patterns, enabling detection evaluation via streaming HTTP or stdio.

Enables AI agents to reverse-engineer binaries they own or are authorized to test, exposing static analysis through Ghidra or IDA (functions, strings, xrefs, decompilation, disassembly, raw bytes) plus controlled Windows Sandbox runs. Every recorded claim must cite quoted evidence and addresses, which a verifier checks, and unexamined areas are reported as unknown rather than clean.

Enables reverse-engineering agents to export binaries to BinExport format and compare two builds, returning matched, changed, and build-unique functions with addresses, names, similarity, and confidence scores. Diff results are written as JSON files with overall similarity and confidence summaries.

Connects AI models and autonomous agents to the x64dbg debugger through the Model Context Protocol, exposing 234 tools for live execution control, register and memory inspection, disassembly, breakpoints, thread scheduling, console I/O, and PE analysis. It also parses saved .trace32/.trace64 trace files offline for instruction search, register lookups, and timeline navigation without x64dbg running.

Bridges large language models with the Metasploit Framework to enable natural language control over penetration testing workflows. It provides tools for searching modules, executing exploits, generating payloads, and managing active sessions.

Enables professional penetration testers to drive recon and exploitation tooling — nmap, gobuster, nikto, John the Ripper, hashcat, subfinder, httpx, ffuf, nuclei, hydra, sqlmap, and tcpdump — plus wordlist building and traffic capture from an MCP client. It also stores structured engagement records and generates client reports with scope-of-work handling, over stdio or authenticated Streamable HTTP (SSE deprecated).

Enables AI models to interact directly with network services via raw TCP sockets, supporting multiple concurrent connections, buffering, and automated responses.

Enables AI agents to control and analyze RFID hardware through natural language: read, crack, emulate, and clone cards via MCP tools, with safety gates, offline dump analysis, and a host-side card library.

Enables AI assistants to perform Volatility 3 memory forensics through natural language, including process and network analysis, injection detection, and shellcode extraction from memory dumps.

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

MCP server for Frida dynamic instrumentation, enabling AI agents to enumerate devices/processes, attach, inject JavaScript, hook functions, read memory, and collect results via MCP tools.

Enables local, persistent analysis of business workflows and application state from imported Burp XML or HAR traffic, building actor/entity/state graphs and generating testable hypotheses for manual validation during authorized security testing.

Enables AI agents and LLMs to query a codebase's structure and data flow by building Joern Code Property Graphs from repositories, local paths, or snippets, then running CPGQL queries for tracing, slicing, and vulnerability analysis across many languages.

Frida MCP server enables AI models to perform dynamic analysis on mobile and desktop platforms via standardized interfaces, with cross-platform device management, script injection, and built-in hook scripts.