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cs.CR2026
What Makes a Good LLM Agent for Real-world Penetration Testing?
Gelei Deng, Yi Liu, Yuekang Li +5
LLM-based agents show promise for automating penetration testing, yet reported performance varies widely across systems and benchmarks. We analyze 28 LLM-based penetration testing…
cs.CR2026
Confundo: Learning to Generate Robust Poison for Practical RAG Systems
Haoyang Hu, Zhejun Jiang, Yueming Lyu +3
Retrieval-augmented generation (RAG) is increasingly deployed in real-world applications, where its reference-grounded design makes outputs appear trustworthy. This trust has spurr…
cs.CR2026
From Description to Score: Can LLMs Quantify Vulnerabilities?
Sima Jafarikhah, Daniel Thompson, Eva Deans +2
Manual vulnerability scoring, such as assigning Common Vulnerability Scoring System (CVSS) scores, is a resource-intensive process that is often influenced by subjective interpreta…