Fully autonomous AI Agents system capable of performing complex penetration testing tasks
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Updated
Jul 11, 2026 - Go
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.
Agentic execution layer for modern cyber security, turning security intent into precise, governed, auditable action through AI agents, MCP-native tools, knowledge, approvals, and attack-chain context.
Sandbox any AI agent in seconds - zero setup, 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.
Autonomous AI pentesting engine, continuous offensive security across web, cloud, AD & Kubernetes. Agentic reasoning + real exploit execution deliver proof-based vulnerabilities. Privacy gateway: the LLM never sees your real IPs, hosts, creds or paths (deterministic placeholders rehydrated locally), nothing leaves your perimeter.
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. 51 rules mapped to OWASP Agentic Top 10 (2026). Works with LangChain, CrewAI, AutoGen.
Security scanner for Agent Skills — uncover hidden threats before deployment.
A professional AI security range for red teaming, vulnerability research, defensive validation, and hands-on AI/ML security training.
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
Fast local Rust scanner for AI-agent prompt injection, credential leaks, exfiltration, and risky tool calls
Secure mcp infrastructure to audit and control every data access by AI agents with minimal efforts
PentestCode - Multi-agent AI penetration testing system with persistent engagement state, strategic coordination, and parallel autonomous operations.
Free security assessment for your OpenClaw 🦞 environment. Scans gateway config, tool permissions, MCP servers, plugins, and chained attack paths.
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.
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