"Spring Security" matching MCP servers:

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 integration with Beagle Security API for managing security testing projects, applications, domain verification, and automated penetration tests. Provides 18 tools for creating, monitoring, and retrieving results from security assessments.

A lightweight MCP server for security reviews that injects security requirements before code generation, scans dependencies for CVEs, and verifies generated code without disrupting workflow.

Automatically detects security vulnerabilities in AI-generated code, scanning for hardcoded secrets, injection flaws, XSS, weak cryptography, authentication issues, path traversal, and vulnerable dependencies across JavaScript, Python, Java, and Go.

Provides offline, line-level code review for Spring and MyBatis changes, analyzing git diffs to catch issues like @Transactional self-invocation, N+1 queries, and ${} injection without needing an API key.

Spring AI Playground is a self-hosted web UI platform for building low-code tools and dynamically exposing them via built-in MCP server for AI agents.

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.

Automatically discovers and tests MCP services for security vulnerabilities including command injection, SQL injection, SSRF, path traversal, and sensitive data exposure with detailed reports and remediation guidance.

Provides penetration testing tools including nmap, nikto, sqlmap, wpscan, and exploit database searches for educational and authorized security testing purposes using Kali Linux tools.

Release gate agents call before they tell users a public website is ready to ship. Scans public websites across security, SEO, accessibility, legal compliance, and sustainability.

MCP server for AI security scanning. Detects prompt injections, jailbreaks, data exfiltration, and social engineering attacks in real-time using 42 regex patterns via the ClawGuard Shield API. Sub-10ms response times.

MCP server that reviews and fixes code across four dimensions (security, standards, production readiness, sustainability), runs tests to verify fixes, and provides findings with diffs.

AI-powered QA testing platform — catches JS errors, accessibility failures, visual regressions, API loops, security issues, and more via Chrome DevTools MCP

MCP server for https://oathe.ai security audits. Runtime behavioral analysis and security scanner for Ai systems. Check trust scores before installing MCP servers, plugins, or AI agent skills.

Static security linter for MCP servers. Scans tool definitions for vulnerabilities (path traversal, SQL injection, SSRF), scores description quality, and auto-rewrites descriptions for safer agent tool selection.

Security scanner for AI agent packages that enables AI agents to audit MCP servers and packages for vulnerabilities, prompt injection, and supply chain attacks.

Local, deterministic review of Git changes for coding agents: weakened tests and CI gates, security-boundary changes, input-to-sink flows, and blast radius. Runs the RepoPilot Rust CLI over stdio; no source leaves the machine and no model is called.

Enables AI coding agents to build and audit Garry's Mod GLua addons with source-aware search, static linting, net security vulnerability scanning, performance profiling, and headless testing.

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.

Verifies AI outputs in real-time across 6 dimensions, with automatic retry and failover to ensure correct, complete, and reliable LLM responses before they reach the user's editor.