mlsanityData to 13 Oct 2026

Causal Localization of the Refusal Direction in Audio Language Models

arXiv 2609.22260 ↗First posted 6 Sep 2026cs.MM cs.CL cs.LGcited by 0

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Abstract (arXiv)

A large audio language model (LALM) attaches a speech front end to a text language model (LM) that is already safety-aligned. When such a model refuses a harmful spoken request, is the refusal carried by the front end, or inherited from the text LM? We test this with causal interventions. At each model's audio-to-LM interface and at tested LM residual layers, we fit a direction separating harmful from benign prompts, ablate its component, and measure the resulting change in the model's first-token refusal margin. Four of the five models are evaluated under held-out category shift. Across five LALMs spanning three backbone families, with three models passing a baseline safety gate, the largest tested effects occur in a mid-to-late LM band, while ablations of the tested interface directions have little effect. On Qwen2.5-Omni, ablating the L16 direction changes the margin by -7.10, versus -0.013 at the projector. The audio pathway is still in use: zeroing the encoder output changes the margin by -4.7. In the same model, the contrast is linearly decodable at an early layer where single-layer ablation has little effect, and a direction fitted on the LM backbone alone transfers to the full audio model. Because harmful and benign prompts also differ in form, we interpret the direction as refusal-linked rather than harmfulness-specific. These interventions localize dependence of the refusal margin, not where refusal is computed. Moving the margin also does not always change what the model writes. Safety audits of these models should use interventions rather than rely on probes alone, and should examine the inherited text LM alongside the audio interface.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 31 of its 34 references that are in the map. First posted on arXiv 6 Sep 2026.

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References · 31

  1. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,064
  2. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,405
  3. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,009
  4. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 850
  5. 2025Qwen2.5-Omni Technical Reportcited by 714
  6. 2024JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Modelscited by 531
  7. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 493
  8. 2025Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Modelscited by 213
  9. 2024LLaMA-Omni: Seamless Speech Interaction with Large Language Modelscited by 172
  10. 2023The Hydra Effect: Emergent Self-repair in Language Model Computationscited by 152
  11. 2020Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?cited by 146
  12. 2024On Prompt-Driven Safeguarding for Large Language Modelscited by 115
19 more
  1. 2025The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independencecited by 114
  2. 2025LLMs Encode Harmfulness and Refusal Separatelycited by 84
  3. 2025DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignmentcited by 48
  4. 2025Voxtralcited by 43
  5. 2024Explorations of Self-Repair in Language Modelscited by 38
  6. 2024Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Modelscited by 36
  7. 2025Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Modelscited by 20
  8. 2025Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusalcited by 19
  9. 2025JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Modelscited by 14
  10. 2025When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Modelscited by 11
  11. 2025SPIRIT: Patching Speech Language Models against Jailbreak Attackscited by 8
  12. 2025SARSteer: Safeguarding Large Audio-Language Models via Safe-Ablated Refusal Steeringcited by 7
  13. 2025Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Modelscited by 6
  14. 2026Now You Hear Me: Audio Narrative Attacks Against Large Audio-Language Modelscited by 6
  15. 2026Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamics Mechanisms and Efficient Alignmentcited by 5
  16. 2026All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluationcited by 5
  17. 2026Acoustic Interference: A New Paradigm Weaponizing Acoustic Latent Semantic for Universal Jailbreak against Large Audio Language Modelscited by 2
  18. 2026Benign Fine-Tuning Breaks Safety Alignment in Audio LLMscited by 1
  19. 2026Safety Geometry Collapse in Multimodal LLMs and Adaptive Drift Correctioncited by 1

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