Adversarial Prompts for Acceptance Collapse in Speculative Decoding

Abstract:Lossless acceleration schemes, such as speculative decoding, promise significant inference speedups by relying on dynamic token-level alignment between a draft and a target model. However, this guarantee of semantic equivalence masks a severe operational vulnerability: draft-target alignment can be systematically attacked. In this paper, we introduce ADSD, which, to the best of our knowledge, is the first prompt-suffix attack that collapses verifier acceptance by pushing draft probability mass toward tokens the target is unlikely to accept. ADSD uses Soft-Collapse, a verifier-aligned surrogate derived from the asymmetric speculative acceptance rule, together with a target-preservation objective that discourages obvious task corruption. ADSD successfully generates highly effective adversarial suffixes. On the GSM8K dataset, our attack increases the mean sample time by 62.3% while preserving the task quality. We further show that this vulnerability exists across different domains, speculative decoding strategies, and model architectures.

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

arXiv:2607.21804 [cs.CR]

(or arXiv:2607.21804v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.21804 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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