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Pwnagent: a knowledge-guided multi-agent system for automatic exploit generation
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DOI：10.1186/s42400-026-00649-5.png)
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Abstract
En 中文
Automatic Exploit Generation (AEG) plays an important role in proactive assessment of software threats by identifying vulnerabilities and constructing functional payloads. Existing Large Language Model (LLM)-based methods, however, often struggle to reason about complex exploit logic and to perform runtime introspection, leaving a gap between static vulnerability analysis and dynamic memory behavior. We present PwnAgent, an LLM-driven multi-agent framework for end-to-end exploit generation that combines offensive domain knowledge with active runtime introspection. PwnAgent uses a hierarchical knowledge base for multi-stage exploit reasoning and a feedback-driven self-correction engine to calibrate dynamic memory parameters during execution. Because broad Capture The Flag (CTF) benchmarks offer limited binary-exploitation depth and pwn-specific evaluation must balance reproducibility, difficulty progression, and exploit diversity, we construct a 66-task pwn benchmark from public CTF-style challenges. The benchmark is primarily composed of Linux x86/x86-64 ELF binaries and stack-oriented tasks, with smaller format-string, heap, integer-overflow, ARM, and MIPS subsets used as limited probes beyond the dominant setting. Under the same recent Kimi-K2.6 backend, PwnAgent achieves a 62.12% end-to-end success rate, compared with 31.82% for the evaluated PwnGPT baseline, a 30.30 percentage-point gain. These paired results indicate that structured knowledge guidance, execution-grounded measurement, and feedback repair improve LLM-based exploit generation in the evaluated setting, while the absolute success rate shows that fully autonomous exploitation remains challenging.
Keywords:
Automatic exploit generation
Multi-agent systems
Large language models
Knowledge-guided reasoning
Dynamic introspection
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