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Adversarial Entropy Inflation Against Gumbel-Based Inference Verification

Research LLM Security

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TL;DR - This paper shows that Gumbel-based defenses against LLM weight exfiltration weaken when attackers control prompts and deliberately increase output entropy. The attack roughly doubles leaked bits per token, indicating that verification thresholds should adapt to local token entropy rather than rely on benign-traffic calibration.

  • Character- and script-level prompt disruptions break grammatical and subword structure, expanding the verifier’s admissible token set and covert-channel capacity.
  • Across six instruction-tuned models from 1B to 32B parameters and three random seeds, the strongest attack leaked roughly twice as many bits per token as benign prompts.
  • The reported slowdown for steganographic exfiltration fell from more than 200Ă— under benign traffic to 60×–118Ă— under adversarial prompts.
  • The authors recommend dynamically calibrating jitter-forgiveness thresholds against local token entropy.

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Adversarial Entropy Inflation Against Gumbel-Based Inference Verification

arXiv cs.CR Nikita Kezins 2026-08-24 arXiv:2608.23375
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-12 14:21:15.012082 UTC

TL;DR - This paper shows that Gumbel-based defenses against LLM weight exfiltration weaken when attackers control prompts and deliberately increase output entropy. The attack roughly doubles leaked bits per token, indicating that verification thresholds should adapt to local token entropy rather than rely on benign-traffic calibration.

  • Character- and script-level prompt disruptions break grammatical and subword structure, expanding the verifier’s admissible token set and covert-channel capacity.
  • Across six instruction-tuned models from 1B to 32B parameters and three random seeds, the strongest attack leaked roughly twice as many bits per token as benign prompts.
  • The reported slowdown for steganographic exfiltration fell from more than 200Ă— under benign traffic to 60×–118Ă— under adversarial prompts.
  • The authors recommend dynamically calibrating jitter-forgiveness thresholds against local token entropy.
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