pentestagent and LuaN1aoAgent
Both AI agents are designed for autonomous black-box penetration testing and red-teaming, making them direct competitors offering different underlying AI models and reasoning approaches for achieving similar security testing goals.
About pentestagent
GH05TCREW/pentestagent
PentestAgent is an AI agent framework for black-box security testing, supporting bug bounty, red-team, and penetration testing workflows.
Built on LiteLLM for multi-model LLM support, PentestAgent features a hierarchical agent architecture where instances can spawn isolated child agents via stdio transport (`spawn_mcp_agent`), enabling parallel task delegation without external orchestration. It integrates MCP (Model Context Protocol) servers with automatic RAG-based tool optimization for large tool sets, includes prebuilt attack playbooks for structured assessments, and offers Docker isolation with both minimal and Kali Linux images containing pentesting tools like metasploit and sqlmap.
About LuaN1aoAgent
SanMuzZzZz/LuaN1aoAgent
LuaN1aoAgent is a cognitive-driven AI hacker. It is a fully autonomous AI penetration testing agent powered by DeepSeek V3.2. Using dual-graph reasoning, LuaN1ao achieves a success rate of over 90% on the XBOW Benchmark, with a median exploit cost of just $0.09.
Implements a decoupled **P-E-R (Planner-Executor-Reflector) agent framework** where independent cognitive roles collaborate via event buses, with the Planner generating graph-editing operations for dynamic task DAGs and the Executor orchestrating tools via MCP protocol. Constructs explicit causal graphs linking evidence→hypothesis→vulnerability→exploit to prevent hallucinations, with confidence scoring on each causal edge and mandatory evidence validation before advancing. Integrates multiple tool types (HTTP requests, shell commands, Python execution) through Model Context Protocol, with real-time parallel task discovery based on topological dependencies and context compression to manage token overhead.
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