The Blind Spots of Detection: Adversarial Perturbation Attacks against Watchdogs in Distributed LLM Inference

Summary

Hush-Up is a stealthy availability attack against distributed LLM inference in which one malicious worker halts generation while evading attribution. The attacker profiles its local hardware error bound, then optimizes an adversarial perturbation on its output activations to maximize the end-of-sequence (EOS) token probability at the final layer. The authors report that the attack forces premature termination across diverse models and benchmarks and can evade or mislead existing verification defenses.

Topics

• Adversarial machine learning

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