Author

Xin-Hong Xie

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#artificial intelligence Preprint Oct 2026

Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks

Security benchmarks for LLM-based agents often report the attack success rate (ASR) as a measure of model robustness and use these scores to compare different models and defense mechanisms, assuming that they describe the security of the agent. In this paper, we explore whether it also influences the benchmark's measur...

Neeraj Karamchandani, Piyush Nagasubramaniam, Xin-Hong Xie et al. · 0 citations

Preprint Sep 2026

LLM-Based Penetration Testing in the Presence of Honeypots

This work presents a systematic study of honeypot-aware budget allocation for LLM attack agents and shows that with the proposed detector-guided policy, LLM agent attackers can effectively allocate budget to compromise hosts in a host pool, highlighting the importance of dynamically allocating budget in a controlled mi...

Xin-Hong Xie, Piyush Nagasubramaniam, Neeraj Karamchandani et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit

Audits of LLM judges certify a bias by contrasting matched conditions, and the strongest designs difference twice: a within-item contrast between two candidate responses, differenced again across a manipulated attribute, read off a bounded rating scale. We show that this endpoint is not identified on the scale that rep...

Shu-Yi Fan, Boyuan Deng, Mengyu Xu et al. · 0 citations