Activation Approximations Can Incur Safety Vulnerabilities Even in
  Aligned LLMs: Comprehensive Analysis and Defense
Large Language Models (LLMs) have showcased remarkable capabilities across
various domains. Accompanying the evolving capabilities and expanding
deployment scenarios of LLMs, their deployment challenges escalate due to their
sheer scale and the advanced yet complex activation designs prevalent in
notable model series, such as Llama, Gemma, and Mistral. These challenges have
become particularly pronounced in resource-constrained deployment scenarios,
where mitigating inference efficiency bottlenecks is imperative. Among various
recent efforts, activation approximation has emerged as a promising avenue for
pursuing inference efficiency, sometimes considered indispensable in
applications such as private inference. Despite achieving substantial speedups
with minimal impact on utility, even appearing sound and practical for
real-world deployment, the safety implications of activation approximations
remain unclear. In this work, we fill this critical gap in LLM safety by
conducting the first systematic safety evaluation of activation approximations.
Our safety vetting spans seven sota techniques across three popular categories,
revealing consistent safety degradation across ten safety-aligned LLMs.
67a3e9fc2955dee2f54fb307
Jiawen Zhang
67a3e9fc2955dee2f54fb308
Kejia Chen
67a3e9fc2955dee2f54fb309
Lipeng He
extracted_confirmed
2025-05-27T18:47:40.398Z
6433707307bad11484af1d2a
https://cdn-avatars.huggingface.co/v1/production/uploads/6433707307bad11484af1d2a/w5zB-zstJzY561n6q7m4D.jpeg
Lipeng (Tony) He
user
ttttonyhe
67a3e9fc2955dee2f54fb30a
Jian Lou
67a3e9fc2955dee2f54fb30b
Dan Li
67a3e9fc2955dee2f54fb30c
Zunlei Feng
67a3e9fc2955dee2f54fb30d
Mingli Song
67a3e9fc2955dee2f54fb30e
Jian Liu
67a3e9fc2955dee2f54fb30f
Kui Ren
67a3e9fc2955dee2f54fb310
Xiaohu Yang
Activation Approximations Can Incur Safety Vulnerabilities Even in
  Aligned LLMs: Comprehensive Analysis and Defense
Large Language Models (LLMs) have showcased remarkable capabilities across
various domains. Accompanying the evolving capabilities and expanding
deployment scenarios of LLMs, their deployment challenges escalate due to their
sheer scale and the advanced yet complex activation designs prevalent in
notable model series, such as Llama, Gemma, and Mistral. These challenges have
become particularly pronounced in resource-constrained deployment scenarios,
where mitigating inference efficiency bottlenecks is imperative. Among various
recent efforts, activation approximation has emerged as a promising avenue for
pursuing inference efficiency, sometimes considered indispensable in
applications such as private inference. Despite achieving substantial speedups
with minimal impact on utility, even appearing sound and practical for
real-world deployment, the safety implications of activation approximations
remain unclear. In this work, we fill this critical gap in LLM safety by
conducting the first systematic safety evaluation of activation approximations.
Our safety vetting spans seven sota techniques across three popular categories,
revealing consistent safety degradation across ten safety-aligned LLMs.
A systematic study reveals widespread safety concerns with activation approximations in Large Language Models, despite their efficiency benefits.
Large Language Models (LLMs) activation approximation resource-constrained deployment inference efficiency safety evaluation safety-aligned LLMs