Fully autonomous AI Agents system capable of performing complex penetration testing tasks

HexStrike AI MCP Agents is an advanced MCP server that lets AI agents (Claude, GPT, Copilot, etc.) autonomously run 150+ cybersecurity tools for automated pentesting, vulnerability discovery, bug bounty automation, and security research. Seamlessly bridge LLMs with real-world offensive security capabilities.

CyberStrikeAI is an AI-native security testing platform built in Go. It integrates 100+ security tools, an intelligent orchestration engine, role-based testing with predefined security roles, a skills system with specialized testing skills, and comprehensive lifecycle management capabilities.

Capability-based agent runtime with fine-grained policies . Brokering access directly within the agent's operating context, with zero setup and zero latency

PentestAgent is an AI agent framework for black-box security testing, supporting bug bounty, red-team, and penetration testing workflows.

LuaN1aoAgent is a cognitive-driven AI hacker. It is a fully autonomous AI penetration testing agent. Using dual-graph reasoning, LuaN1ao achieves a success rate of over 90% on the XBOW Benchmark.

Autonomous AI pentesting engine performing continuous offensive security across web, cloud, AD and Kubernetes. Uses agentic reasoning, real exploit execution and attack path analysis to deliver proof-based vulnerabilities.

AI EDR for developer workstations and autonomous agent fleets. Build Swarm Detection & Response platforms with Clawdstrike.

Static security scanner for LLM agents — prompt injection, MCP config auditing, taint analysis. 49 rules mapped to OWASP Agentic Top 10 (2026). Works with LangChain, CrewAI, AutoGen.

AI Red Teaming Range

Security scanner for Agent Skills — uncover hidden threats before deployment.

A comprehensive reference for securing Large Language Models (LLMs). Covers OWASP GenAI Top-10 risks, prompt injection, adversarial attacks, real-world incidents, and practical defenses. Includes catalogs of red-teaming tools, guardrails, and mitigation strategies to help developers, researchers, and security teams deploy AI responsibly.

The CoSAI Risk Map is a framework for identifying, analyzing, and mitigating security risks in Artificial Intelligence systems. As traditional software security practices are not always sufficient for AI, this project provides a shared understanding and a common language for addressing the unique security challenges of the AI development lifecycle.

MCP Security Solution for Agentic AI — real-time proxying, behavior analysis, and malicious tool detection

Secure mcp infrastructure to audit and control every data access by AI agents with minimal efforts

Fast local Rust scanner for AI-agent prompt injection, credential leaks, exfiltration, and risky tool calls

Client-side retrieval firewall for RAG systems — blocks prompt injection and secret leaks, re-ranks stale or untrusted content, and keeps all data inside your environment.

Security Dashboard for OpenClaw AI Agents - intercept, monitor, and control what OpenClaw does on your system.

Whitebox & Blackbox red-teaming framework for LLMs & Agentic AI apps. It analyzes your app's source code to discover tools, roles, and guardrails, then generates new attacks chains across several categories and adapts over multiple multi turn rounds to find vulnerabilities

Next-Gen Secret Scanner powered by Local AI (Ollama). Filters false positives by understanding code context.