"Créer des systèmes de prompt pour les modèles de langage (LLM)" matching MCP servers:

Enables security researchers to evaluate AI system defenses against prompt injection attacks through a comprehensive set of test vectors and analysis tools.

MCP server toolkit with payload library, WAF bypass generator, rate limiter, plugin system, and inter-MCP bridge for security testing.

An MCP server that runs curated adversarial prompts against local Ollama models to test guardrails, scoring responses with heuristic verdicts for human review.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Protects AI agents from prompt injection attacks, jailbreak attempts, and common web vulnerabilities by screening untrusted input through semantic LLM analysis and static pattern matching.

Enables LLM agents to browse and triage vulnerability observations, manage products, branches and rules, import scan reports and SBOMs, run scans and background jobs, and generate VEX documents via SecObserve's REST API.

A read-only MCP server for the Qualys PCI Merchant API that lets LLM assistants answer questions about PCI compliance posture, such as which hosts are failing PCI or listing high findings.

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.

14 atomic MCP tools for AppSec and AI Security engineers: source/schema/prompt audit primitives, JWT inspect, HTTP diff, pentest atoms (default creds, GraphQL introspect, phpggc, interactsh OOB), and a defensive helpers library that fixes the bugs the detectors flag. SARIF output, PyPI Trusted Publishing with Sigstore provenance.

Static security scanner for AI-generated (vibe-coded) Supabase and Next.js apps. Runs as an MCP server so an LLM can scan, explain findings, and produce fixes while it writes code, all locally and offline.

Enables AI agents to scan MCP servers and AI packages for vulnerabilities, prompt injection, and supply chain attacks.

Enables LLM agents to query SecurityTrails for DNS history, subdomain enumeration, WHOIS (current and historical), SSL certificates, and IPv4 intelligence via 17 read-only tools.

Security scanning, prompt injection detection, secret leak detection, and agent permission auditing for AI agent workflows.

Runs WCAG accessibility, SEO, performance, and security audits on URLs or raw HTML via SiteLint Auditor. Enables LLM agents to audit web pages and check WCAG criteria through MCP tools.

Provides anonymous, privacy-preserving tools for AI agents, including prompt-injection scanning, site file validation, endpoint liveness checks, attestation, and risk-pattern analysis.

Scans MCP servers for prompt-injection, tool-poisoning, and SSRF vulnerabilities using 30+ canonical rules across 5 severity tiers, with optional signed safety reports for procurement.

Enables an LLM to author, validate, and test Wirefilter WAF and Smart Firewall rules using live schema and real CVE exploit templates.

Exposes a pentest toolbox (sqlmap, nuclei, ffuf, etc.) via Docker to an LLM orchestrator for authorized security audits, with strict whitelisting and timeout controls.

MCP server wrapping Drozer for LLM-driven Android IPC security testing, enabling autonomous enumeration and exploitation of app components via natural language.

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.