title: Activation Approximations Can Incur Safety Vulnerabilities Even in
    Aligned LLMs: Comprehensive Analysis and Defense
summary: 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.
paper_authors._id: 67a3e9fc2955dee2f54fb307
paper_authors.name: Jiawen Zhang
paper_authors._id: 67a3e9fc2955dee2f54fb308
paper_authors.name: Kejia Chen
paper_authors._id: 67a3e9fc2955dee2f54fb309
paper_authors.name: Lipeng He
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-27T18:47:40.398Z
paper_authors._id: 6433707307bad11484af1d2a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6433707307bad11484af1d2a/w5zB-zstJzY561n6q7m4D.jpeg
paper_authors.fullname: Lipeng (Tony) He
paper_authors.type: user
paper_authors.user: ttttonyhe
paper_authors._id: 67a3e9fc2955dee2f54fb30a
paper_authors.name: Jian Lou
paper_authors._id: 67a3e9fc2955dee2f54fb30b
paper_authors.name: Dan Li
paper_authors._id: 67a3e9fc2955dee2f54fb30c
paper_authors.name: Zunlei Feng
paper_authors._id: 67a3e9fc2955dee2f54fb30d
paper_authors.name: Mingli Song
paper_authors._id: 67a3e9fc2955dee2f54fb30e
paper_authors.name: Jian Liu
paper_authors._id: 67a3e9fc2955dee2f54fb30f
paper_authors.name: Kui Ren
paper_authors._id: 67a3e9fc2955dee2f54fb310
paper_authors.name: Xiaohu Yang
paper_title: Activation Approximations Can Incur Safety Vulnerabilities Even in
    Aligned LLMs: Comprehensive Analysis and Defense
paper_summary: 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.
paper_ai_summary: A systematic study reveals widespread safety concerns with activation approximations in Large Language Models, despite their efficiency benefits.
paper_ai_keywords: Large Language Models (LLMs) activation approximation resource-constrained deployment inference efficiency safety evaluation safety-aligned LLMs