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How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States

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arxiv 2406.05644 v2 pith:JYM7UZ4A submitted 2024-06-09 cs.CL cs.AIcs.CRcs.CY

Zhenhong Zhou , Haiyang Yu , Xinghua Zhang , Rongwu Xu , Fei Huang , Yongbin Li This is my paper
classification cs.CLcs.AIcs.CRcs.CY
keywords safetyalignmentjailbreakearlyllmsmodelscircumventconcepts
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) rely on safety alignment to avoid responding to malicious user inputs. Unfortunately, jailbreak can circumvent safety guardrails, resulting in LLMs generating harmful content and raising concerns about LLM safety. Due to language models with intensive parameters often regarded as black boxes, the mechanisms of alignment and jailbreak are challenging to elucidate. In this paper, we employ weak classifiers to explain LLM safety through the intermediate hidden states. We first confirm that LLMs learn ethical concepts during pre-training rather than alignment and can identify malicious and normal inputs in the early layers. Alignment actually associates the early concepts with emotion guesses in the middle layers and then refines them to the specific reject tokens for safe generations. Jailbreak disturbs the transformation of early unethical classification into negative emotions. We conduct experiments on models from 7B to 70B across various model families to prove our conclusion. Overall, our paper indicates the intrinsical mechanism of LLM safety and how jailbreaks circumvent safety guardrails, offering a new perspective on LLM safety and reducing concerns. Our code is available at https://github.com/ydyjya/LLM-IHS-Explanation.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Verbalizable Representations Form a Global Workspace in Language Models

    cs.CL 2026-07 conditional novelty 7.0 of 10

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  2. OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

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    VLMs show a Semantic-Physical Gap on food images: strong dish naming but high MAPE on mass/nutrients and frequent unsafe advice for high-risk disease profiles.

  3. Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Summaries of LLM safety papers, paired with a completion-style payload containing a harmful query, jailbreak aligned LLMs at high reported success rates and expose a defense paper versus attack paper bias.

  4. MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security

    cs.CL 2025-09 conditional novelty 5.0 of 10

    MoGUv2 embeds small routers in the deeper layers of LLMs to dynamically blend a helpful variant and a refusal variant, improving safety against jailbreak and fine-tuning attacks while preserving usability.

  5. Evaluating Multi-Agent Defences Against Jailbreaking Attacks on Large Language Models

    cs.AI 2025-06 conditional novelty 4.0 of 10

    Multi-agent defenses reduce false negatives but increase false positives, and optimal agent count varies by jailbreak attack type.

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