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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 · ORCID
classification cs.CLcs.AIcs.CRcs.CY
keywords safetyalignmentjailbreakearlyllmsmodelscircumventconcepts
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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 13 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

    Language models represent their current reasoning in a small, readable set of verbalizable vectors (the J-space) that functions like a global workspace.

  2. Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models

    cs.CL 2024-12 conditional novelty 7.0 of 10

    Jailbreak attacks push LLM activations outside a safety boundary, mostly in low and middle layers, and a tanh-based penalty that pulls activations back inside this boundary blocks most tested attacks with under 2% uti...

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

    cs.AI 2026-07 conditional novelty 6.0 of 10

    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.

  4. 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.

  5. GeneBreaker: Jailbreak Attacks against DNA Language Models with Pathogenicity Guidance

    cs.CR 2025-05 conditional novelty 6.0 of 10

    GeneBreaker, a new attack framework, steers DNA language models to generate sequences with over 90% identity to human pathogens, with success rates up to 60% on the largest Evo2 model.

  6. JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation

    cs.CR 2025-02 conditional novelty 6.0 of 10

    JBShield detects jailbreaks by checking whether a prompt activates both a toxic concept and a jailbreak concept inside an LLM, then steers those concepts to produce a safe refusal.

  7. 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.

  8. Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement

    cs.CL 2025-05 conditional novelty 5.0 of 10

    SAGE is a training-free, prompt-based defense that routes every request through a two-stage safety judgment before answering, reaching near-zero attack success on tested jailbreaks.

  9. Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning

    cs.LG 2025-05 reject novelty 5.0 of 10

    Latte transfers LLM latent-state knowledge via a knowledge adapter and unsupervised meta-learning, claiming SOTA few-shot tabular performance, though its own evaluation contradicts that claim on Diabetes.

  10. AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security

    cs.LG 2025-04 conditional novelty 5.0 of 10

    A multi-agent inference-time guardrail with DSPy-optimized prompts lowers StrongREJECT jailbreak success from 0.078 to 0.038 and WMDP accuracy to near the 25% random floor, without retraining the underlying model.

  11. LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models

    cs.CR 2024-12 conditional novelty 5.0 of 10

    LLM-Virus uses an evolutionary algorithm with an LLM as crossover, mutation, and fitness operator to evolve jailbreak templates, reporting state-of-the-art attack success on HarmBench and AdvBench.

  12. JailbreakLens: Interpreting Jailbreak Mechanism in the Lens of Representation and Circuit

    cs.CR 2024-11 conditional novelty 5.0 of 10

    JailbreakLens shows jailbreaks work by pushing LLM internal representations toward 'safe' clusters while suppressing refusal circuits and boosting affirmation circuits, with a consistent correlation across five models...

  13. 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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