TACS: Trajectory-Aware Candidate Selection for LLM Jailbreak Suffix Optimization

Abstract:Gradient-based jailbreak suffix optimization methods typically update the suffix by retaining the candidate with the lowest current loss. We show that this seemingly natural design is fundamentally myopic: candidates that look better under the current-step proxy often fail to produce better jailbreak outcomes later in the search, revealing a form of selection-stage reward hacking. This suggests that candidate selection, rather than candidate generation alone, is a hidden bottleneck in suffix optimization. To address this issue, we propose \OURS{}, a trajectory-aware candidate selection framework for jailbreak suffix optimization. Instead of selecting candidates solely by their immediate loss, \OURS{} augments per-step evaluation with a trajectory-aware proxy and stabilizes selection with reference-policy regularization and a discriminator-estimated chi-squared correction, encouraging choices that remain effective beyond the current step. Experiments on HarmBench show that \OURS{} consistently outperforms strong baselines under the same search budget, substantially improving attack success rates while exhibiting more stable optimization behavior throughout the search. Our findings highlight that mitigating selection-stage reward hacking caused by myopic candidate selection is critical for improving jailbreak suffix optimization.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2608.29564 [cs.CL]

(or arXiv:2608.29564v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.29564 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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