"AWS Secrets Manager" matching MCP servers:

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.

Enables passive security scanning of HAR files from QA test automation to detect missing security headers, insecure cookies, CORS misconfigurations, and leaked secrets, without making any live requests.

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 coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.

MCP server for Cursor that scans codebases for security issues including hardcoded secrets, SAST, vulnerable dependencies, and IaC misconfigurations.

Unified MCP server that gives AI agents security tools to audit MCP configs, redact secrets/PII, vet skills, check dependencies, and scan networks.

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.

Integrates Checkov, Semgrep, Bandit, and ASH to provide comprehensive code security analysis for AI coding assistants.

Provides AI agents with a hardened code security scanner via scan_code and scan_path, detecting leaked secrets and insecure patterns while enforcing strict path confinement and resource bounds.

Enables scanning projects for leaked secrets and security issues directly from Claude Code, detecting secret categories, dangerous code patterns, and git hygiene issues.

Security scanning for AI coding tools (Claude Code, Cursor, Windsurf) including secrets detection, MCP config vulnerabilities, agent instruction checks, threat modeling, prompt injection testing, pre-commit security checks, and dependency vulnerability scanning.

MCP server providing direct tool-access to security-testing primitives for bug bounty hunting, including recon, request replay, IDOR/BOLA fuzzing, vulnerability detection, secrets scanning, and persistent hunt memory with confidence-scored findings.

Enables deterministic security testing of AI agents that use tools by serving synthetic MCP environments with poisoned data, fake secrets, and privileged actions. Records agent tool calls and evaluates security invariants (e.g., canary leaks, forbidden access, approval binding) without an LLM judge or real systems.

Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.

Provides AI coding assistants with real-time security scanning superpowers, including SAST, secrets detection, dependency CVE scanning, and web vulnerability assessment.

Provides on-machine security scanning for Copilot-generated code, including SAST, secrets, dependency, container, and configuration checks, plus AI-specific risk detection, with findings and fixes available via MCP tools.

Predeploy security scanner for AI-generated code. 80+ vulnerability patterns across secrets, auth, injection, config, Supabase, and logging. Runs locally, code never leaves your machine. Optional x402 witnessed attestation.

MCP server that integrates with ARXsec.io API to execute security scans, compliance checks, secrets management, and remediation actions with policy enforcement, audit logging, and human approval workflows.

AI-powered security scanner for Python projects and GitHub repositories. Detects vulnerabilities, secrets, and provides AI risk assessment.