Secure Speculative Decoding for Large Language Models
[Kryptografia i bezpieczeństwo] Autorzy: Yichi Zhang, Zhiqi Wang, Neil Gong i 1 więcej
Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model, referred to as the \emph{draft model}, to generate candidate tokens and then verifying them with the target model for acceptance or rejection. Prior studies primarily focused on the efficiency-utility trade-off of speculative decoding, e.g., lossy speculative decoding, leaving its security implications largely unexplored. In this work, we bridge this gap by providing the \emph{first} systematic study of the security implications of speculative decoding. Through a large-scale measurement study, we reveal a pronounced security-utility asymmetry: across a wide range of lossy speculative decoding methods, improvements in inference efficiency come at a disproportionately high cost to security, with attack success rates for jailbreak and prompt i…
- Kategoria arxiv
- cs.CR
- Kategoria
- Kryptografia i bezpieczeństwo