mlsanityData to 14 Oct 2026

Locating and Steering Refusal Beyond Attention

arXiv 2609.04721 ↗First posted 4 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

Where inside a language model does refusal live, and does that place change when the architecture does? In a transformer, refusal is governed by a single direction in the residual stream, a finding that safety and interpretability tooling now depend on. State-space models (SSMs) route information through a recurrent update instead of attention, sharing no token-mixing mechanism with a transformer. Does the same safety representation survive this shift, or must it be rediscovered per architecture? It survives. A single rigid rotation, which can only reorient a space and not reshape it, aligns one model's representation space with another's, so the two genuinely share the representation. A harm probe trained on a transformer then flags an SSM's harmful inputs, and removing the aligned direction makes a model answer attacks it would otherwise refuse, while a random direction of the same size does far less. What is architecture-specific is not where the direction is steered but where it must be read. Each layer computes a fresh output that is then added into the residual stream, and harm is cleanly readable at this output, the write site, before the addition. A control that holds the intervention's strength fixed shows that what matters is where the direction is estimated, not where it is applied. Applied through a detector-triggered gate, this direction lowers jailbreak success in all four architecture families we test (SSM, transformer, recurrent, hybrid), and on the SSM it holds against an attacker that tunes its prompt against the defense. The gate only matches a trivial rule that returns a fixed refusal whenever the same detector fires, so what transfers across architectures is the direction itself, not defense strength. Safety tooling built on refusal therefore ports to a new architecture by re-estimating the direction at that architecture's write site, not by rebuilding it.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 51 references that are in the map. First posted on arXiv 4 Sep 2026.

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35 of 51 references.

References · 35

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  4. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  5. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,429
  6. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  7. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  8. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  9. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  10. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 860
  11. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 712
  12. 2023SmoothLLM: Defending Large Language Models Against Jailbreaking Attackscited by 469
23 more
  1. 2024Improving Alignment and Robustness with Circuit Breakerscited by 322
  2. 2023LEACE: Perfect linear concept erasure in closed formcited by 267
  3. 2024Not All Language Model Features Are One-Dimensionally Linearcited by 212
  4. 2024Programming Refusal with Conditional Activation Steeringcited by 206
  5. 2024Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrencecited by 159
  6. 2023Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMscited by 91
  7. 2024On the Role of Attention Heads in Large Language Model Safetycited by 72
  8. 2025Detecting Strategic Deception Using Linear Probescited by 68
  9. 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignmentcited by 62
  10. 2026Building Production-Ready Probes For Geminicited by 47
  11. 2024Falcon Mamba: The First Competitive Attention-free 7B Language Modelcited by 46
  12. 2024Towards Universality: Studying Mechanistic Similarity Across Language Model Architecturescited by 31
  13. 2025SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signalscited by 29
  14. 2025The Rogue Scalpel: Activation Steering Compromises LLM Safetycited by 21
  15. 2025Beyond Linear Probes: Dynamic Safety Monitoring for Language Modelscited by 16
  16. 2026Cross-Architecture Model Diffing with Crosscoders: Unsupervised Discovery of Differences Between LLMscited by 12
  17. 2025DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modificationcited by 10
  18. 2026Analysing the Safety Pitfalls of Steering Vectorscited by 10
  19. 2024Does Transformer Interpretability Transfer to RNNs?cited by 9
  20. 2025SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Modelscited by 9
  21. 2025Comparative Analysis of LLM Abliteration Methods: A Cross-Architecture Evaluationcited by 9
  22. 2026Perfect Detection, Failed Control: The Geometry of Knowing vs. Steering in Language Modelscited by 5
  23. 2026Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generationcited by 2

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  1. 2026Component and Dimension Sparsity in Transformer Refusal Mechanisms
  2. 2026Over-Refusal and Representation Subspaces: A Mechanistic Analysis of Task-Conditioned Refusal in Aligned LLMs
  3. 2026On the Steering Dimensionality of Refusal in Language Models
  4. 2026The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs
  5. 2026Refusal Localizes, the Damage Relocates: Safety Layers Under Few-Sample Fine-Tuning
  6. 2025The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
  7. 2026Beyond Shallow Alignment: How Post-Training Methods Determine Refusal Circuits And Steering Robustness
  8. 2026Decomposing Refusal Steering in Mixture-of-Experts Models