Alsharif Abuadbba
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The paper introduces MEntA, a highly query-efficient and surrogate-free membership inference attack that uses natural-language entailment to detect if a specific document was used by a RAG system, achieving high accuracy with only five queries.
The paper introduces APT-Agent, an automated LLM-driven framework that significantly improves penetration testing success rates by mitigating LLM hallucinations and maintaining long-term operational context.
Papers
APT-Agent: Automated Penetration Testing using Large Language Models
The paper introduces APT-Agent, an automated LLM-driven framework that significantly improves penetration testing success rates by mitigating LLM hallucinations and maintaining long-term operational c…