When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs

Abstract:Jailbreak defenses are essential for protecting large language models (LLMs), but they can also introduce secondary costs that weaken model utility. We present a systematic study of these defense trade-offs along three dimensions: performance impact, over-refusal on benign inputs, and inference cost. Rather than treating defenses as a single class, we organize them by operational strategy and examine how different strategies correlate with different side-effect profiles. Across state-of-the-art defense methods, widely used benchmark datasets, and representative open-source LLMs, we find that defenses rarely improve downstream capability, but instead vary in how they trade safety gains against usability and efficiency. In particular, rule-based defenses best preserve task performance, highly conservative self-reflective defenses often increase over-refusal, and multi-round defenses incur the largest runtime overhead. These results provide both a benchmark for evaluating defense side effects and practical guidance for selecting defenses under deployment constraints.

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2607.24392 [cs.CR]

(or arXiv:2607.24392v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.24392 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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