This work proposes ActivationBackdoor, an inference-time backdoor attack that composes two activation-level components for trigger detection and backdoor behavior injection, and shows that ActivationBackdoor attains attack success comparable to training-time backdoor baselines while preserving high clean-task accuracy and utility.
Zichun Su, Mi Zhang, Xiaohan Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Collaborative inference enables cost-effective deployment of large language models by partitioning layers across multiple participants and forwarding intermediate activations between participants in a pipeline, but these transmitted activations also create a new attack surface: a malicious participant can manipulate intermediate activations during inference. Prior work on collaborative inference attacks has largely focused on privacy leakage, leaving the backdoor threat insufficiently explored. Inspired by recent advances in representation engineering, we propose ActivationBackdoor, an inference-time backdoor attack that composes two activation-level components for trigger detection and backdoor behavior injection. This design achieves the same ''clean inputs behave normally, triggered inputs induce attacker-specified behavior'' property as traditional backdoor attacks, while requiring no access to training data and no model parameter updates. Experiments across classification and open-ended generation tasks show that ActivationBackdoor attains attack success comparable to training-time backdoor baselines while preserving high clean-task accuracy and utility. Overall, our results expose a new and practical backdoor risk in collaborative inference arising from intermediate activation exposure.
Zichun Su, Mi Zhang, Xiaohan Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
