arXiv:2606.11409v1 Announce Type: new 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 required to jailbreak a model, thereby making it hard to determine whether an attack's cost justifies its payoff to the attacker. We propose a compute-aware evaluation framework based on computatio
The rapid advancement and deployment of large language models necessitate more sophisticated and realistic security evaluations that account for the practical constraints of attackers.
This framework shifts the focus from theoretical attack success rates to economically viable attack costs, providing a more accurate assessment of LLM security in real-world scenarios.
Evaluations of AI model robustness will now increasingly consider the computational resources required for successful attacks, leading to more robust and cost-aware defenses.
- · AI security researchers
- · Cloud computing providers
- · AI model developers with efficient defense mechanisms
- · Attackers with inefficient methods
- · AI model developers relying solely on fixed-budget ASR
Security benchmarks for LLMs will incorporate compute cost as a critical metric, driving the development of defenses that are expensive to bypass.
This could lead to a 'computational arms race' in AI security, where defensive measures aim to significantly raise the compute threshold for successful attacks, benefiting companies with significant compute resources.
The increased cost barrier for attacks might concentrate offensive capabilities in the hands of actors with deep pockets, potentially state-sponsored groups, altering the threat landscape for AI models.
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Read at arXiv cs.LG