APT-Agent automates penetration testing with LLMs using rectification and memory modules, achieving 84.29% end-to-end success on Metasploitable 2 versus lower rates for baselines.
arXiv preprint arXiv:2306.11489 , year=
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EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
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APT-Agent: Automated Penetration Testing using Large Language Models
APT-Agent automates penetration testing with LLMs using rectification and memory modules, achieving 84.29% end-to-end success on Metasploitable 2 versus lower rates for baselines.
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EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.