"Most downloaded items or content" matching MCP servers:

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.

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.

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

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.

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.

MCP server for Android APK triage, providing tools to parse APK headers, list DEX classes, and decode AndroidManifest.xml using apktool or androguard backends.

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

Enables agents and CI pipelines to audit MCP servers and agent tool-chains by statically scanning repositories, local checkouts, tools/list exports, or live endpoints for risks such as destructive actions without confirmation, mismatched safety annotations, injection surfaces, credential or PII exposure, and unguarded command, path, or URL sinks. All checks are read-only and never execute the scanned code or call tools/call.

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

A security gate MCP server that audits agent extensions (skills, MCP servers, tools) by scanning for risks, adversarial analysis, and sandbox execution, returning a trust verdict of allow, quarantine, or block.

Enables AI assistants to perform web content discovery scans using feroxbuster on a remote system via SSH, with support for recursive scanning, filtering, and background execution.

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.

Integrates with Dradis Pro to manage projects, vulnerabilities, content blocks, and document properties through MCP-enabled tools.

Professional vulnerability report generator that creates standardized, well-formatted security reports with AI-generated content, supporting CVSS scoring, evidence management, and markdown export.

A Model Context Protocol (MCP) server for Kerberos abuse operations using Rubeus on Windows or Impacket on Linux/macOS.

Talk to your Flipper Zero from Claude Code — or any MCP client. Storage, app deployment, screen capture, button automation, JavaScript execution on the device, and honest answers about what is nearby.

A security research MCP server for testing tool call safety with deterministic policies like allowlists, path boundary enforcement, SSRF prevention, output redaction, and prompt injection detection, without requiring external LLMs or API keys.

The most comprehensive steganography analysis toolkit for AI agents, providing 60 tools across 7 categories for detecting and extracting hidden data from images, audio, text, and files, all running 100% offline.

Enables generating falsifiable fleet hypotheses, compiling them into minimal bounded probe plans with control cohorts, and evaluating observations to return supported, refuted, or inconclusive verdicts.