The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.
Autopentest: Enhancing vulnerability management with autonomous llm agents
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A decoupled evaluation framework shows LLM penetration agents reach 90% exploitation success with ground-truth context but only 50% reconnaissance recall due to telemetry parsing failures across 50 vulnerabilities.
LLMVD.js uses LLM agents to confirm 84% of taint-style vulnerabilities on public benchmarks (vs. <22% for prior tools) and generates validated exploits for 36 of 260 new packages (vs. ≤2 for traditional tools).
Automation-Exploit is a multi-agent LLM system that uses conditional digital-twin validation to perform risk-mitigated exploitation of logical, web, and memory-corruption vulnerabilities in black-box targets.
xOffense automates penetration testing via a fine-tuned Qwen3-32B LLM in a multi-agent setup with specialized agents for reconnaissance, vulnerability scanning, and exploitation, reporting 79.17% sub-task completion on AutoPenBench and AI-Pentest-Benchmark.
citing papers explorer
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Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing
The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.
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Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing
A decoupled evaluation framework shows LLM penetration agents reach 90% exploitation success with ground-truth context but only 50% reconnaissance recall due to telemetry parsing failures across 50 vulnerabilities.
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Taint-Style Vulnerability Detection and Confirmation for Node.js Packages Using LLM Agent Reasoning
LLMVD.js uses LLM agents to confirm 84% of taint-style vulnerabilities on public benchmarks (vs. <22% for prior tools) and generates validated exploits for 36 of 260 new packages (vs. ≤2 for traditional tools).
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Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation
Automation-Exploit is a multi-agent LLM system that uses conditional digital-twin validation to perform risk-mitigated exploitation of logical, web, and memory-corruption vulnerabilities in black-box targets.
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xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models
xOffense automates penetration testing via a fine-tuned Qwen3-32B LLM in a multi-agent setup with specialized agents for reconnaissance, vulnerability scanning, and exploitation, reporting 79.17% sub-task completion on AutoPenBench and AI-Pentest-Benchmark.