mlsanityData to 7 Oct 2026

Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models

arXiv 2406.09289 ↗First posted 13 Jun 2024cs.CL cs.AI cs.LGcited by 34

On the map: LLM safety and security → Jailbreaking

Abstract (arXiv)

Conversational large language models are trained to refuse to answer harmful questions. However, emergent jailbreaking techniques can still elicit unsafe outputs, presenting an ongoing challenge for model alignment. To better understand how different jailbreak types circumvent safeguards, this paper analyses model activations on different jailbreak inputs. We find that it is possible to extract a jailbreak vector from a single class of jailbreaks that works to mitigate jailbreak effectiveness from other semantically-dissimilar classes. This may indicate that different kinds of effective jailbreaks operate via a similar internal mechanism. We investigate a potential common mechanism of harmfulness feature suppression, and find evidence that effective jailbreaks noticeably reduce a model's perception of prompt harmfulness. These findings offer actionable insights for developing more robust jailbreak countermeasures and lay the groundwork for a deeper, mechanistic understanding of jailbreak dynamics in language models.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 18 of its 34 references that are in the map. First posted on arXiv 13 Jun 2024.

Builds on

18 of 34 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models28 Mar
  2. 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
  3. 2023Multilingual Jailbreak Challenges in Large Language Models10 Oct
  4. 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
  5. 2023Jailbroken: How Does LLM Safety Training Fail?5 Jul

Other references · 13

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  4. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  5. 2023Qwen Technical Reportcited by 3,273
  6. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  7. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  8. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  9. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
  10. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,106
  11. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,009
  12. 2023Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attackscited by 268
1 more
  1. 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 179

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 15; 2026: 9.

34 papers.

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  1. 2024Programming Refusal with Conditional Activation Steeringcited by 191
  2. 2024A Practical Review of Mechanistic Interpretability for Transformer-Based Language Modelscited by 113
  3. 2024Steering Language Model Refusal with Sparse Autoencoderscited by 89
  4. 2025LLMs Encode Harmfulness and Refusal Separatelycited by 75
  5. 2024SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selectioncited by 64
  6. 2024Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreakscited by 44
  7. 2025The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Analysis of Orthogonal Safety Directionscited by 38
  8. 2024Failures to Find Transferable Image Jailbreaks Between Vision-Language Modelscited by 27
  9. 2024What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attackscited by 24
  10. 2025RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?cited by 19
  11. 2026AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agentscited by 16
  12. 2024Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinioncited by 13
22 more
  1. 2025Chain-of-Thought Hijackingcited by 13
  2. 2024FLRT: Fluent Student-Teacher Redteamingcited by 9
  3. 2025Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Modelscited by 9
  4. 2025DAVSP: Safety Alignment for Large Vision-Language Models via Deep Aligned Visual Safety Promptcited by 7
  5. 2024Feint and Attack: Attention-Based Strategies for Jailbreaking and Protecting LLMscited by 5
  6. 2025AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Researchcited by 5
  7. 2025Misaligned Roles, Misplaced Images: Structural Input Perturbations Expose Multimodal Alignment Blind Spotscited by 3
  8. 2025ConceptGuard: Neuro-Symbolic Safety Guardrails via Sparse Interpretable Jailbreak Conceptscited by 2
  9. 2025LatentBreak: Jailbreaking Large Language Models through Latent Space Feedbackcited by 2
  10. 2026Principled Steering via Null-space Projection for Jailbreak Defense in Vision-Language Modelscited by 2
  11. 2025NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Modelscited by 1
  12. 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1
  13. 2026Knowing without Acting: The Disentangled Geometry of Safety Mechanisms in Large Language Modelscited by 1
  14. 2025Don't Walk the Line: Boundary Guidance for Filtered Generationcited by 0
  15. 2025Toward Understanding the Transferability of Adversarial Suffixes in Large Language Modelscited by 0
  16. 2025Exploiting Latent Space Discontinuities for Building Universal LLM Jailbreaks and Data Extraction Attackscited by 0
  17. 2026Towards Unsupervised Adversarial Document Detection in Retrieval Augmented Generation Systemscited by 0
  18. 2026Why Do Large Language Models Generate Harmful Content?cited by 0
  19. 2026Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Modelscited by 0
  20. 2026Why Do Aligned LLMs Remain Jailbreakable: Refusal-Escape Directions, Operator-Level Sources, and Safety-Utility Trade-offcited by 0
  21. 2026Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreakscited by 0
  22. 2026A Polyphonic Conception of AI Understandingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models
  2. 2025Do Internal Layers of LLMs Reveal Patterns for Jailbreak Detection?
  3. 2026Defending Large Language Models Against Jailbreak Attacks via In-Decoding Safety-Awareness Probing
  4. 2024Jailbreak Attacks and Defenses Against Large Language Models: A Survey
  5. 2025Jailbreaking in the Haystack
  6. 2025Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions
  7. 2026SoK: Rethinking Jailbreaking in the Era of Agentic AI: Attacks, Defenses, and Practical Consideration
  8. 2025How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation