Not All Tokens Are Created Equal: Query-Efficient Jailbreak Fuzzing for LLMs
Large Language Models(LLMs) are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, they typically treat all tokens as equally important during prompt mutation, overlooking the varying contributions of individual tokens to triggering model refusals. Consequently, these attacks introduce substantial redundant searching under query-constrained scenarios, reducing attack efficiency and hindering comprehensive vulnerability assessment. In this work, we conduct a token-level analysis of refusal behavior and observe that token contributions are highly skewed rather than uniform. Moreover, we find stron...
| Authors | Wenyu Chen, Xiangtao Meng, Chuanchao Zang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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