pyrit

This bash script will install wifite2 and its other tools Pyrit, bully, wireshark, hcxtools, hcxdumptool, macchanger

Offensive security testing CLI for AI agents — 85 attack strategies (and counting) across 12 categories

It has been developed in order to facilitate the use of ready-made Brute Force tools in Kali-Linux operating systems and is written in Python language.

script for simplifying wifite to be easier, quicker, and more effective.

Projet issu du codelab Devfest Nantes 2025 “La guerre des prompts” : atelier de 2h pour apprendre à pirater des IA et comment les protéger via des frameworks open source

Autonomous AI Red Teaming laboratory validating the Microsoft AI Red Team Taxonomy using the PyRIT framework. Focused on Agentic AI security and strategic conversational persistence.

Runtime control, security, and governance for AI agents — decide what an agent may do at the moment it acts, revoke that authority instantly, and keep tamper-evident evidence of every decision.

Python + SQL toolkit for LLM threat intelligence — flags prompt injection, jailbreaks, and data exfiltration mapped to OWASP LLM Top 10 (2025) & MITRE ATLAS.

COMPDES 2026 workshop: securing MCP/RAG AI agents for small businesses. Build, break (Garak, PyRIT, Promptfoo), harden. / Taller de seguridad de agentes de IA.

AISRF, the AI Security & Research Framework: every LLM request from your apps and agents becomes a ticket a human approves before it reaches the backend. Analyzers, guardrails (Rebuff, LLM Guard, NeMo, Lakera), red teaming (garak, promptfoo, PyRIT), source code review, reports, MCP server.

Pyrit test with Palo Alto Networks AI Runtime Security

Arm any MCP-capable agent to autonomously red-team a target LLM. Microsoft PyRIT exposed as 31 composable MCP tools: attacks, scoring, converters & scenarios

Self-contained LLM red-team lab: deliberately-vulnerable app + garak/promptfoo/PyRIT harness mapped to OWASP LLM Top 10 (lab-only).

Chained Harm & Injection Measurement for Evaluating Risks in Agents — a red-team research framework that tests hosted models, agentic apps, RAG pipelines, MCP servers, AI coding assistants, macOS desktop apps, and browser extensions against context-pollution, prompt-injection, and chain-composition attacks.

Adversarial testing methodology, attack simulations, and MITRE ATLAS mappings for securing clinical AI and LLM systems.

Intentionally vulnerable RAG service (FastAPI, OpenAI): indirect prompt injection via poisoned documents leaks a system-prompt secret. Automated and scored with PyRIT and Garak.

Automated LLM Security Evaluation Framework

Three-tier AI red-teaming platform (PyRIT) testing this portfolio's own LLM/agent systems — real obfuscation attacks, Microsoft's Crescendo multi-turn technique, and agent manipulation testing, with vulnerabilities found and defense-in-depth verified against live infrastructure.

Production-grade multi-agent AI research platform: LangGraph 4-agent pipeline (Search→Summarize→Write→Verify) through a TensorZero gateway (gpt-4o + Groq fallback), AWS Bedrock guardrails, Redis STM, pgvector LTM, semantic cache, LangSmith scoring, PyRIT red-team tests; Terraform + GitHub Actions CI/CD with observability, retries, and auth.