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Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs

Jaechul Roh, Amir Houmansadr

arXiv:2604.16659v1cs.CRcs.SD

TL;DR

Benign fine-tuning can degrade safety alignment in Audio LLMs, but prior single-space analyses leave unclear whether semantic and acoustic proximity drive risk differently. The paper evaluates proximity-filtered benign audio across three models, finding JSR as high as 87.12% with architecture-conditioned vulnerability patterns, while distant filtering and system prompts reduce JSR to near-zero.

  • Problem

    Prior analyses show benign fine-tuning degrades safety in text and vision but do not distinguish which input properties drive vulnerability in Audio LLMs.

  • Method

    The paper evaluates three Audio LLMs using model-internal and reference-based filtering across semantic, acoustic, and mixed embedding axes.

  • Results

    87.12% JSR is reached after proximity-filtered benign fine-tuning, with vulnerability axes and audio-versus-text risk conditioned by architecture.

  • Takeaways & Limitations

    Distant training-data filtering and a textual system prompt reduce JSR to near-zero without architectural modification.

  • Takeaways & Limitations

    Evaluation is limited to four benign speech datasets, two noise types, frozen encoders, and English single-turn interactions.

Abstract

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Prior work shows that fine-tuning aligned models on benign data degrades safety in text and vision modalities, and that proximity to harmful content in representation space predicts which samples cause the most damage. However, existing analyses operate within a single, undifferentiated embedding space -- leaving open whether distinct input properties drive the vulnerability differently. Audio introduces a structurally richer problem: a benign sample can neighbor harmful content not only through what is said but through how it sounds, even when its words are entirely innocuous. We present the first systematic study of benign fine-tuning safety in Audio LLMs, evaluating three state-of-the-art models with a proximity-based filtering framework that selects benign audio by embedding-space distance to harmful content. By decomposing proximity into semantic, acoustic, and mixed axes using external reference encoders alongside each model's own internal encoder, we show that benign fine-tuning elevates Jailbreak Success Rate (JSR) from single digits to as high as 87.12%. Crucially, the dominant vulnerability axis and the relative risk of audio versus text fine-tuning are both architecture-conditioned -- determined by how each model's encoder and projector transform audio into the LLM's input space. We propose two defenses: filtering training data to maximize distance from harmful embeddings, and a textual system prompt at inference, both reducing JSR to near-zero without architectural modification. Our mechanistic analysis on two architectures reveals that fine-tuning selectively suppresses the late-layer refusal circuit while the frozen encoder preserves representations, and that even the suppression pattern is architecture-conditioned, mirroring the behavioral asymmetries across modalities. Safety degradation from benign fine-tuning is a qualitatively distinct risk in Audio LLMs.

1 Introduction

This section asks whether benign audio fine-tuning compromises Audio LLM safety and shows that the vulnerability depends on representational properties and model architecture.

  • Benign audio fine-tuning can compromise safety alignment in Audio LLMs, extending prior findings from text and vision modalities.
  • The study evaluates three state-of-the-art Audio LLMs with filtering that selects benign audio by embedding-space distance to harmful content.
  • 87.12% JSR is reached after proximity-filtered benign fine-tuning, compared with a single-digit baseline.
  • The dominant vulnerability axis depends on architecture, with semantic filtering most predictive for Kimi-Audio and mixed filtering dominant for AF3.
  • Distant filtering and a safety system prompt reduce JSR to near-zero without architectural modification.

2 Related Work

Related work shows that benign fine-tuning can weaken safety alignment across text and vision-language models, while studies have investigated mechanisms and harmful-sample characteristics.

  • Benign fine-tuning has been shown to reduce refusal and induce broader misaligned behavior in text models.
  • Prior studies identify damaging samples through gradient matching, outlier detection, representation similarity, and optimization conflict.
  • Safety degradation also affects vision-language models, where reported defenses can over-refuse or fail on complex multi-image scenarios.

3 Problem Setting

The problem setting considers unintended safety degradation when users fine-tune aligned Audio LLMs on entirely benign audio whose representations may neighbor harmful content.

  • The setting assumes a well-intentioned user fine-tunes a safety-aligned Audio LLM on entirely benign audio without an adversary or harmful data.
  • The filtering function selects the top-k% benign samples with the smallest minimum embedding distance to harmful samples.
  • The central hypothesis is that smaller k, corresponding to closer benign data, produces greater safety degradation despite benign training data.
  • Frozen audio encoders preserve representations during fine-tuning while the LLM decision boundary can shift because audio safety alignment is not fully transferred from text representations.

4 Methodology

The methodology filters benign audio by proximity to harmful content using model-internal and shared reference embeddings, then measures safety degradation under controlled fine-tuning conditions.

  • The framework selects benign audio by distance to harmful content and evaluates the resulting safety degradation experimentally.
  • Model-internal filtering tests whether each target model’s own encoder pipeline predicts its vulnerability, while reference filtering uses shared external encoders.
  • For each benign sample, the method computes minimum cosine distance to any harmful sample and selects the closest top-k% subset.
  • The dose-response analysis varies k from 10% through 90% to study how proximity relates to safety degradation.
  • Reference encoders separate semantic, acoustic, and mixed proximity, testing whether safety risk depends on what audio says, how it sounds, or both.
  • A text-modality control fine-tunes on transcriptions while holding model, samples, LoRA configuration, and training steps constant.

5 Experiments

Across three Audio LLMs, proximity-filtered benign audio produces larger safety degradation than random sampling, while the most predictive proximity axis and cross-modal risk vary by architecture. Reasoning-oriented data partially mitigates degradation, and distant filtering or textual system prompts provide defenses under model-dependent conditions.

  • Experimental Setup: Three Audio LLMs were fine-tuned on four benign audio datasets and evaluated across safety benchmarks using proximity-filtering strategies.The experiments covered SD-QA, GC Accents, MMSU, and MELD, with MELD used for AF3 and Qwen2.5-Omni.
  • Main Empirical Results: 58.08% AdvBench JSR occurred for Kimi-Audio under internal filtering at 25% data, versus 5.38% with random sampling.Qwen2.5-Omni similarly reached 30.09% versus 5.19% under the same comparison.
  • Reference Encoder Decomposition: 87.12% JSR was reached by Kimi-Audio with Sentence-BERT semantic filtering at 25%, while AF3 was most vulnerable under Whisper-V3 mixed filtering.For Qwen2.5-Omni, internal and mixed filtering are equivalent because its internal encoder is the same Whisper-Large-V3 encoder used as the mixed reference.
  • Reference Encoder Decomposition: Acoustic filtering diverged by architecture: it reduced AF3’s JSR, sustained Qwen2.5-Omni degradation at 23.46% AdvBench JSR, and helped Kimi-Audio only at 25%.These patterns connect acoustic vulnerability to each model’s encoder and bottleneck design.
  • Audio vs. Text Fine-Tuning: AF3’s audio fine-tuning increased AdvBench JSR to 24.42% at 50% data, whereas text fine-tuning decreased it to 2.12% at 25%; Qwen2.5-Omni showed the opposite pattern.The contrast is attributed to how each architecture maps audio and text into the refusal-relevant representational pathway.
  • Dataset Effects: Reasoning-oriented MELD fine-tuning produced smaller AdvBench JSR increases and decreased SafetyBench JSR across filtering methods.The paper attributes this partial mitigation to reasoning-based self-correction after harmful intent is recognized.
  • Defense: Distant filtering improved safety for AF3 and under semantic filtering for Kimi-Audio, but Qwen2.5-Omni remained vulnerable to fine-tuning perturbations.A textual system prompt reduced JSR to near-zero across the tested high-risk checkpoints without architectural modification.

6 Discussion

Safety degradation is architecture-conditioned: fine-tuning most harms the representational pathway least covered by alignment, with distinct cross-modal patterns and selective late-layer refusal suppression. The evaluation remains bounded by speech-focused, English, single-turn settings and frozen encoders.

  • Cross-Modal Asymmetry: For AF3, audio fine-tuning increases JSR while text fine-tuning decreases it; for Qwen2.5-Omni, text fine-tuning is more disruptive than audio.AF3’s projector creates a modality gap, whereas Qwen2.5-Omni preserves closer audio-text alignment.
  • Recognition Without Refusal: Qwen2.5-Omni shows deeper refusal suppression under text fine-tuning than audio fine-tuning, mirroring its higher text-fine-tuning JSR.At 25% filtering, the caption reports JSR values of 16.4% for text and 9.4% for audio.
  • How Encoder Architecture Affects Safety Degradation: The dominant vulnerability axis depends on encoder architecture: semantic filtering dominates Kimi-Audio, mixed filtering AF3, and acoustic filtering remains predictive for Qwen2.5-Omni.AF3’s projector discards acoustic features, while Qwen2.5-Omni’s transparent architecture preserves enough structure for acoustic filtering.
  • Recognition Without Refusal: Late-layer refusal signals collapse after fine-tuning while the frozen encoder preserves representations, linking refusal suppression to observed JSR without eliminating downstream task performance.In AF3, text fine-tuning preserves the refusal signal; in Qwen2.5-Omni, both modalities suppress it, with text producing deeper suppression.
  • Limitations and Future Work: The study evaluates four benign speech datasets and English single-turn interactions, while leaving non-speech tasks, adversarial perturbations, unfreezing, and broader language settings open.Only two noise types are considered, and all encoders are frozen due to computational constraints.

7 Conclusion

The study shows that benign fine-tuning can degrade Audio LLM safety, with vulnerability patterns conditioned by architecture and modality. It also reports that training-time distance filtering and inference-time textual prompts reduce JSR to near-zero.

  • 7 Conclusion: Across three models, four benign datasets, and two safety benchmarks, proximity-filtered benign audio raises JSR from single digits to as high as 87.12%.The dominant vulnerability axis and cross-modal asymmetries depend on architecture.
  • 7 Conclusion: Distant filtering during training and a textual system prompt during inference both reduce JSR to near-zero.These defenses require no architectural modification.
  • 7 Conclusion: The findings motivate modality-aware safety evaluations and data-screening procedures for Audio LLMs exposed to user customization.This consequence is stated within the paper’s scope as Audio LLM deployment expands.

Ethics Statement

The work studies Audio LLM safety vulnerabilities using only benign fine-tuning data and existing harmful-prompt benchmarks. It withholds fine-tuned weights and does not create new harmful content.

  • Ethics Statement: Evaluation uses harmful prompts from AdvBench and SafetyBench, and the study creates no new harmful content.Fine-tuning is performed exclusively on benign data.
  • Ethics Statement: The authors do not release fine-tuned model weights to reduce potential misuse.The statement frames weight non-release as a misuse-prevention measure.

A Mechanistic Analysis

The mechanistic analysis measures refusal-direction projections across LLM layers in AF3 and Qwen2.5-Omni to connect fine-tuning with late-layer refusal suppression. It finds architecture-dependent, modality-specific changes while the audio encoders remain frozen.

  • Setup: The analysis examines AF3 and Qwen2.5-Omni, each with 28 LLM layers, while treating the audio encoder as preceding the analyzed LLM backbone.AF3 uses a Whisper encoder and 2-layer MLP projector; Qwen2.5-Omni uses Whisper-Large-V3 pass-through.
  • Refusal Direction Extraction: The refusal direction is computed from mean activation differences between refused and complied pretrained responses on 520 AdvBench prompts.The direction is normalized and held fixed when evaluating fine-tuned checkpoints.
  • Refusal Direction Extraction: High refusal-direction projections indicate activation of the refusal mechanism, which normally rises sharply across late layers 20–26.Low or near-zero projections indicate that the refusal mechanism is inactive.
  • Cross-Modal Divergence in AF3: In AF3, audio fine-tuning progressively suppresses the late-layer refusal projection, reaching JSR 32.12% and a layer-26 projection near 8 at 75% filtering.At 25% filtering, JSR is 20.19% and the layer-26 projection falls near 34 from approximately 186 pretrained.
  • Cross-Modal Divergence in AF3: AF3 text fine-tuning preserves the refusal signal: at 25% filtering, JSR is 2.12%, below the 7.69% pretrained baseline.Higher filtering conditions retain substantially more refusal signal than audio conditions.
  • Cross-Modal Divergence in AF3: AF3’s compressive projector exposes audio fine-tuning to an under-covered region while text fine-tuning operates where refusal alignment was established.The encoder remains frozen, so the distinction arises from the input pathway into the LLM.
  • Contrasting Pattern in Qwen2.5-Omni: For Qwen2.5-Omni, both modalities suppress late-layer refusal, with audio reaching JSR 9.42% and text reaching 16.35% at 25% filtering.Text reduces the layer-26 projection to approximately 42, while 50% filtering nearly eliminates the signal.
  • Contrasting Pattern in Qwen2.5-Omni: Qwen2.5-Omni’s pass-through architecture places audio and text representations in overlapping input-space regions, making text comparatively more disruptive.Both modalities therefore perturb the same refusal boundary.

B.1 Embedding-based Proximity Method

The method ranks benign audio by its minimum cosine distance to harmful samples, then compares proximity across encoder spaces. These spaces reveal different degrees of overlap and separation between benign and harmful audio.

  • Embedding-based Proximity Method: For each benign sample, the method computes its minimum cosine distance to harmful samples and ranks samples by that value.The closest samples are selected as the proximate subset, while the farthest samples form the distant subset for safe filtering.
  • Embedding Space Visualization: Whisper-V3 embeddings heavily intermingle benign and harmful samples, whereas WavLM acoustic embeddings provide the clearest separation.Sentence-BERT provides moderate separation because lexical semantics distinguish harmful requests from factual questions.
  • Model Architectures: The three evaluated Audio LLMs use distinct encoder architectures, including projected, dual-encoder, and pass-through designs.These architectural differences motivate comparing model-internal and external representation spaces.
  • Evaluation Setup: The filtering framework evaluates benign audio against harmful prompts across multiple benign datasets and two harmful-prompt benchmarks.The benchmarks include AdvBench and SafetyBench, while benign data include spoken and synthesized factual QA corpora.

E Additional Results

Additional results examine safety, utility, defenses, and self-correction after benign fine-tuning. They compare pretrained and fine-tuned models across vulnerable conditions and downstream reasoning tasks.

  • Safety Results: The additional evaluations report pretrained and fine-tuned Jailbreak Success Rate across benign datasets and proximity-filtering conditions.Table 5 compares generalization across datasets, while Table 6 evaluates Audio-Reasoner-CoTA fine-tuning.
  • Self-Correction: Qwen2.5-Omni can initially begin complying with a harmful request and then self-correct during reasoning after fine-tuning.The correction occurs when the model recognizes the harmful intent.
  • Defense: A textual system prompt is evaluated as an inference-time defense on each model’s most vulnerable fine-tuning condition.The same safety-oriented prompt is used across all three models.
  • Utility Evaluation: BBH accuracy is used to test whether safety degradation reflects targeted vulnerability rather than broad capability loss.The evaluation uses unseen reasoning tasks from VoiceBench, and the reported fine-tuning utility change is slight.

H Robustness to Audio Perturbations

Robustness analyses show that proximity-based safety degradation can involve acoustically distinct perturbations while remaining tied to latent encoder representations. Human inspection does not reliably reveal which benign samples are most proximate.

  • Audio Perturbations: Kimi-Audio is fine-tuned on semantically proximate SD-QA audio with café or urban-traffic noise while preserving the original linguistic content.The perturbations shift acoustic embeddings by mean cosine distances of 0.12 and 0.14, respectively.
  • Fine-Tuning Configuration: All audio encoders remain frozen during fine-tuning, while LoRA adapts the LLM backbone and is merged for inference.For AF3 and Kimi-Audio, adapters target attention and FFN projections; Qwen2.5-Omni applies them to Thinker-module linear layers.
  • Interpretability of Proximity: Audio-encoder proximity pairs entirely benign questions with thematically unrelated harmful prompts, whereas text-semantic proximity produces topical pairings.Examples include a polio-vaccine question near hacking instructions and a gochujang question near insider-trading advice.
  • TTS Effects: GC Accent samples can have zero acoustic distance to harmful prompts because TTS voice and prosody patterns produce identical Whisper features.The benign and harmful content can remain entirely unrelated despite this acoustic match.
  • Robustness and Detection: Both proximity-filtered and random benign samples appear innocuous to humans, while their safety risk differs only in encoder space.This makes proximity-based attacks difficult to detect through ordinary content moderation.
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