SpecGuard: Inference-Time Backdoor Detection For Free
SpecGuard detects backdoored LLM behavior at inference time using speculative decoding acceptance rates, adding no extra model computation.
Researchers propose SpecGuard, an inference-time backdoor detector that repurposes draft-token acceptance rates from speculative decoding as a detection signal at zero added model-computation cost. When a trigger shifts the target model toward attacker-controlled behavior, the clean draft model's acceptance rate changes, exposing the backdoor; the paper formalizes when this signal appears and shows suppressing it weakens the backdoor. Experiments across diverse backdoor types and model families show reliable detection, including stealthy cases invisible to input-level filters. Speculative decoding is positioned as a free, always-on monitor for frequently updated deployed models.
- Uses draft-token acceptance rate as a zero-cost backdoor signal
- Detects stealthy backdoors that bypass input-level filters
- Suppressing the acceptance-rate signal necessarily weakens the backdoor
- Targets runtime monitoring for frequently updated LLM deployments
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From the original article
1 min read at arxiv.orgLarge language models are often fine-tuned, shared, or downloaded from third parties, so a deployed model may carry a hidden backdoor that behaves normally on benign inputs but switches to attacker-controlled behavior when a secret trigger appears. While backdoors can be audited before deployment, runtime monitoring remains important for models that are frequently updated.
By Rui Wen · arxiv.org
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