"Understanding Inference Models" matching MCP servers:

An MCP server that provides cross-validated npm dependency fitness verdicts, including deprecation, malicious detection, and safe migration target inference.

Inspects documents for tampering, ineffective redactions, and text aimed at language models before they enter an agent's context, and compares documents to confirm whether they are unchanged.

Enables incremental model scoring over an append-only event log, with a tamper-evident, hash-chained audit trail so every decision can be reviewed and independently re-verified.

Enables users to manage OpenFGA authorization models, stores, and relationship tuples through natural language. It supports permission checks, batch tuple operations, and the deployment of authorization models across different environments.

A Model Context Protocol server that retrieves CVE information from the National Vulnerability Database, allowing AI models to access up-to-date vulnerability data.

Enables detection of AI-generated content from ChatGPT, GPT-4, and Gemini models through the BACH AI Content Detector API.

An implementation of the Model Context Protocol server that enables AI models to communicate with Edge Security Acceleration (ESA) services, allowing models to manage routines, deployments, routes, records, and sites through standardized protocols.

MCP server for SDC governance validation, exposing tools that agents call to validate governance content in XML instances against SDC data models and return XACML decisions (PERMIT, DENY, INDETERMINATE).

Enables AI models to analyze log files from the Chuangyu Shield cloud defense platform, including retrieving file info, reading log lines, searching logs, and analyzing attack types and IP statistics.

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 agents to reach upstream MCP tool servers through a central policy gateway that authenticates each agent, enforces least-privilege and default-deny rules, and applies response obligations such as redaction, inference stripping, PII masking, and truncation. It also records every decision in a tamper-evident, hash-chained audit log that can be verified later.

Provides real-time content safety protection for large language models by detecting and preventing risks in both input and output content across multiple dimensions including compliance, ethics, and security.

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.

Hardware trust layer for sovereign AI that attests firmware trust state and gates AI inference on verified results.

Enables large language models to interact with Velociraptor DFIR through natural language, allowing queries and analysis of forensic data, artifacts, hunts, collections, and VQL results.

A collection of MCP servers for Kali Linux that empower AI Agents in reverse engineering and security testing, providing network analysis, target sniffing, traffic analysis, binary understanding, and automation capabilities.

An MCP server that integrates ThreatBook's threat intelligence API, offering 15 specialized tools for security analysis. It enables AI models to perform IP reputation checks, domain investigations, file sandbox analysis, and vulnerability intelligence lookups.

Pre-attestation gate for AI inference that checks compliance conditions and issues Ed25519-signed clearances before execution, refusing any inference without a valid clearance.

Provides AI models with tools to check trust scores and safety details for MCP servers, search for servers, and view ecosystem statistics.

A production-ready Model Context Protocol (MCP) server for seamless integration between Wazuh SIEM and Large Language Models (LLMs).