Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with attacker effort across methods, model families, and harm types. We initiate a scaling-law framework for jailbreaks by treating each attack as a compute-bounded optimization procedure and measuring progress on a shared FLOPs axis. Our systematic evaluation spans four representative jailbreak paradigms, covering optimization-based attacks, self-refinement prompting, sampling-based selection, and genetic optimization, across multiple model families and scales on a diverse set of harmful goals. We investigate scaling laws that relate attacker budget t...
| Authors | Xiangwen Wang, Ananth Balashankar, Varun Chandrasekaran |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 18 March 2026 |
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