"Security vulnerabilities in MCP Server related to subprocess execution" matching MCP servers:

Verifies AI agent completion claims by checking that claimed output files exist and are fresh, acting as a lightweight physical telemetry gate.

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.

Enables AI assistants to scan project dependencies and Infrastructure as Code files for security vulnerabilities and misconfigurations. It also provides automated fixing capabilities to remediate identified security issues.

Enables a single Antigravity IDE to explicitly drive multiple isolated Antigravity CLI workers, choosing each worker's account, model, skills, task, and expected outputs while the bridge executes those decisions over STDIO with SQLite-backed task state. It returns bounded logs, cancellable task control, and immutable artifact exports whose chunks and whole files can be verified by SHA-256.

MCP server that provides a 216-rule, framework-aware Kotlin/JVM security analyzer, enabling AI coding agents to query real security findings while writing code.

Security for the MCP servers you use: detects tool poisoning, rug pulls, shadowing, secrets and supply-chain risk (OWASP MCP Top 10), with runtime guards and approved-server policy. Runs locally.

MCP server for AI agents to send notifications, copy to clipboard, request confirmations, and collect text input from users across their devices.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Enables file operations, npm package management, and configuration checking through MCP.

Audits npm package dependencies for security vulnerabilities, providing detailed reports and fix recommendations with MCP integration.

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

MCP server providing a searchable security knowledge base for AI coding assistants, enabling retrieval of secure-coding instructions and feature-specific security guidance.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Detect live website vulnerabilities and security flaws in GitHub repositories using automated DAST and SAST scanning. Safeguard applications by identifying exposed secrets, insecure dependencies, and common code patterns prone to exploitation. Receive structured fix plans with precise code remediation steps to resolve identified risks and improve security posture.

A TypeScript MCP server with strict schema validation and security guards, currently implementing echo, get_time, hash_text, uuid_generate, and json_validate tools.

Enables authenticated MCP clients to query Security Context tools against a git tag or commit ref while returning only vulnerabilities and fix fingerprints that predate that snapshot, so agents do not receive live bugs at the tag.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables AI agents to interact with Google NotebookLM and Gemini API for document analysis, deep research, and notebook creation with enterprise-grade security including post-quantum encryption and 14 security layers.

Converts natural language queries into valid GraphQL queries and executes them against GraphQL APIs. Includes schema introspection, query validation, execution with authentication, and query history tracking.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.