mlsanityData to 7 Oct 2026

Superficial Safety Alignment Hypothesis

arXiv 2410.10862 ↗First posted 7 Oct 2024cs.CL cs.AI cs.CRcited by 14

On the map: LLM alignment / RLHF / preference optimisation → Safety alignment and jailbreak attacks

also in 1 more
Abstract (arXiv)

As large language models (LLMs) are overwhelmingly more and more integrated into various applications, ensuring they generate safe responses is a pressing need. Previous studies on alignment have largely focused on general instruction-following but have often overlooked the distinct properties of safety alignment, such as the brittleness of safety mechanisms. To bridge the gap, we propose the Superficial Safety Alignment Hypothesis (SSAH), which posits that safety alignment teaches an otherwise unsafe model to choose the correct reasoning direction-fulfill or refuse users' requests-interpreted as an implicit binary classification task. Through SSAH, we hypothesize that only a few essential components can establish safety guardrails in LLMs. We successfully identify four types of attribute-critical components: Safety Critical Unit (SCU), Utility Critical Unit (UCU), Complex Unit (CU), and Redundant Unit (RU). Our findings show that freezing certain safety-critical components during fine-tuning allows the model to retain its safety attributes while adapting to new tasks. Similarly, we show that leveraging redundant units in the pre-trained model as an "alignment budget" can effectively minimize the alignment tax while achieving the alignment goal. All considered, this paper concludes that the atomic functional unit for safety in LLMs is at the neuron level and underscores that safety alignment should not be complicated. We have code implementation and other information on the project website: https://ssa-h.github.io/.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in Safety alignment and jailbreak attacks, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 80 of its 111 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

80 of 111 references.

Closest ancestors · 12

Same direction, newest first

  1. 2024Safety Layers in Aligned Large Language Models: The Key to LLM Security30 Aug
  2. 2024SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal20 Jun
  3. 2024Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons20 Jun
  4. 2024Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models12 Jun
  5. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deep10 Jun
  6. 2024Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack28 May
  7. 2024Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic19 Feb
  8. 2024Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications7 Feb
  9. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!5 Oct
  10. 2023Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models4 Oct
  11. 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
  12. 2023Are aligned neural networks adversarially aligned?26 Jun

Other references · 68

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  7. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  8. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  9. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  10. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  11. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  12. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
38 more
  1. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  2. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  3. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  4. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  7. 2023Mistral 7Bcited by 3,065
  8. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
  9. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  10. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,140
  11. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,114
  12. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  13. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,565
  14. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  15. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
  16. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  17. 2022Red Teaming Language Models with Language Modelscited by 983
  18. 2018SNIP: Single-shot Network Pruning based on Connection Sensitivitycited by 802
  19. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  20. 2023A Simple and Effective Pruning Approach for Large Language Modelscited by 776
  21. 2023LLM-Pruner: On the Structural Pruning of Large Language Modelscited by 643
  22. 2019Linear Mode Connectivity and the Lottery Ticket Hypothesiscited by 627
  23. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 615
  24. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 533
  25. 2019Importance Estimation for Neural Network Pruningcited by 531
  26. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 457
  27. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
  28. 2023Catastrophic Jailbreak of Open-source LLMs via Exploiting Generationcited by 439
  29. 2023Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruningcited by 408
  30. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translationcited by 390
  31. 2024SliceGPT: Compress Large Language Models by Deleting Rows and Columnscited by 378
  32. 2022Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruningcited by 317
  33. 2024Improving Alignment and Robustness with Circuit Breakerscited by 302
  34. 2023Pretraining Language Models with Human Preferencescited by 245
  35. 2020Comparing Rewinding and Fine-tuning in Neural Network Pruningcited by 238
  36. 2023DepGraph: Towards Any Structural Pruningcited by 202
  37. 2023Mitigating the Alignment Tax of RLHFcited by 181
  38. 2023Self-Alignment with Instruction Backtranslationcited by 149

50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 10.

14 papers.

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  1. 2025Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbationcited by 20
  2. 2025Safety Alignment Can Be Not Superficial With Explicit Safety Signalscited by 9
  3. 2025What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decodingcited by 7
  4. 2026Purifying Generative LLMs from Backdoors without Prior Knowledge or Clean Referencecited by 6
  5. 2025Trustworthy AI: Safety, Bias, and Privacy -- A Surveycited by 5
  6. 2026Operationalising the Superficial Alignment Hypothesis via Task Complexitycited by 4
  7. 2026Alignment-Weighted DPO: A principled reasoning approach to improve safety alignmentcited by 2
  8. 2026Alignment Dynamics in LLM Fine-Tuningcited by 2
  9. 2026Crafting Reversible SFT Behaviors in Large Language Modelscited by 0
  10. 2026Position: Retire the "Positive Backdoor" Label -- Secret Alignment Requires Strict and Systematic Evaluationcited by 0
  11. 2026The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMscited by 0
  12. 2026Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflectioncited by 0
2 more
  1. 2026CausalMix: Data Mixture as Causal Inference for Language Model Trainingcited by 0
  2. 2026MASCRDM: Multi-Agent System for Compliance Risk Detection and Mitigation in Training Process of Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
  2. 2025SaRO: Enhancing LLM Safety through Reasoning-based Alignment
  3. 2026Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization
  4. 2024Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching
  5. 2026Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning
  6. 2026Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance
  7. 2026MOSAIC: Composable Safety Alignment with Modular Control Tokens
  8. 2026Can Safety Emerge from Weak Supervision? A Systematic Analysis of Small Language Models