Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models 事件

Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models arXiv:2606.11409v1 Announce Type: cross Abstract: Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally costly. In practice, the computational expense of different attack strategies can vary by orders of magnitude. Consequently, ASR at a fixed budget can obscure the true effort req