Secure Speculative Decoding for Large Language Models
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SpecGuard is an inference‑time backdoor detector that leverages speculative decoding—using a small draft model to propose tokens and a target model to verify them—without adding extra model computation. By monitoring the draft‑token acceptance rate, SpecGuard detects when a target model shifts toward attacker‑controlled behavior while the draft model does not, signaling a backdoor trigger. The method reliably identifies a range of backdoor types, including stealthy attacks that bypass input‑level filters, across multiple model families.
arXiv:2606. 19755v1 Announce Type: cross Abstract: Speculative inference accelerates large language model (LLM) decoding but provides no inherent safety guarantees.
arXiv:2610.07822v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft model to propose multiple tokens that are verified by a target...
SpecGuard is an inference‑time backdoor detector that leverages speculative decoding—using a draft model to propose tokens and a target model to verify them—without adding extra model computation. By monitoring the draft‑token acceptance rate, SpecGuard identifies when a target model’s behavior shifts toward attacker‑controlled outputs, a signal that appears whenever a backdoor is triggered. The method works across various backdoor types and model families, reliably detecting stealthy attacks that bypass input‑level filters while avoiding the extra generation cost of existing runtime detectors.
arXiv:2606. 01019v1 Announce Type: cross Abstract: Large Language Model (LLM) generation remains expensive because autoregressive decoding calls the model once for each new token.
arXiv:2607. 08690v1 Announce Type: cross Abstract: Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM.