New paper links autoregressive consistency to LLM safety alignment failures

Researchers have identified autoregressive consistency as a key factor in the fragility of safety alignment in large language models. This phenomenon, where next-token prediction reinforces existing response trajectories, can lead to alignment updates being concentrated on early tokens. The paper proposes that this mechanism explains shallow safety alignment and can be exploited by attacks that introduce harmful continuations at arbitrary points. To address this, the authors introduce adversarial safety alignment, a framework designed to break harmful autoregressive consistency throughout the output trajectory. AI

IMPACT Identifies a core mechanism that can undermine LLM safety, potentially leading to new alignment techniques and attack vectors.

RANK_REASON Academic paper detailing a new mechanism and proposed solution for LLM safety alignment. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Autoregressive Consistency
• Large Language Models

• paper
• safety

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