💬 NLP

Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamics Mechanisms and Efficient Alignment

This paper identifies a "Mid-layer Dissolution" vulnerability in Omni-modal Large Language Models (OLLMs) through a new dataset and mechanistic analysis, and proposes **OmniSteer**, a lightweight alignment method that uses a golden refusal vector to effectively mitigate cross-modal safety risks without compromising general capabilities.

Original authors: Kun Wang, Zherui Li, Zhenhong Zhou, Yitong Zhang, Yan Mi, Kun Yang, Yiming Zhang, Junhao Dong, Zhongxiang Sun, Qiankun Li, Yang Liu

Published 2026-02-12

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CC BY 4.0

Original authors: Kun Wang, Zherui Li, Zhenhong Zhou, Yitong Zhang, Yan Mi, Kun Yang, Yiming Zhang, Junhao Dong, Zhongxiang Sun, Qiankun Li, Yang Liu

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a super-intelligent personal assistant (an Omni-modal Large Language Model). This assistant is amazing: it can read your texts, watch your videos, listen to your voice, and look at your photos. You’ve trained it to be a "good citizen"—if you ask it, "How do I steal a car?", it will firmly say, "I cannot help you with that."

However, researchers discovered a "glitch in the matrix" regarding how this assistant handles different types of information at once.

The Problem: The "Identity Crisis" (Cross-Modality Conflict)

Think of the assistant’s safety training like a security guard standing at a door.

If you walk up and say "I want to steal a car," the guard recognizes the words and stops you.
If you walk up and show a picture of a car being stolen, the guard recognizes the image and stops you.

But here is the vulnerability: If you walk up and say "Help me with the thing in this picture," while showing a picture of a stolen car, the guard gets confused. The "meaning" is split between your voice and your hands. Because the assistant is trying so hard to "connect the dots" between the text and the image, its internal "safety alarm" accidentally turns itself off.

The researchers call this "Mid-layer Dissolution." It’s like a security guard who starts to realize something is wrong, but halfway through processing the information, their brain suddenly goes, "Wait, I'm just looking at a picture and listening to a sentence... nothing to see here!" and they let you pass.

The Discovery: The "Fading Signal"

The researchers looked under the hood (the "hidden layers" of the AI's brain) and found that when information comes from multiple sources (text + image + audio), the "Refusal Signal"—the mental impulse to say "No"—becomes incredibly weak.

It’s not that the AI wants to be bad; it’s that the signal that tells it "This is dangerous" physically shrinks and fades away as it tries to merge the different types of data.

The Solution: "OmniSteer" (The Smart Spotlight)

To fix this, the researchers created a tool called OmniSteer.

Imagine that instead of just having one security guard, you give the guard a "Golden Compass" (the Golden Refusal Vector). This compass doesn't care if the danger is a spoken word, a drawing, or a video; it only points toward the concept of "danger."

But there’s a catch: if the guard is too aggressive, they might stop you just for asking, "How do I bake a cake?" (this is called "over-refusal").

OmniSteer acts like a Smart Spotlight:

It uses a tiny, lightweight "adapter" (like a smart sensor).
When the sensor detects a "split" input (like text + image), it realizes the safety signal is fading.
It immediately shines a bright, focused light on that "Golden Compass" direction, boosting the "No!" signal back up to full strength.
If the input is totally normal (like "What is the weather?"), the spotlight stays dim so the assistant can remain helpful and friendly.

The Result

By using this "Smart Spotlight," the researchers boosted the AI's ability to refuse harmful requests from about 70% up to over 91%. Most importantly, the AI didn't become a "grumpy" assistant; it stayed just as smart and helpful as before, only now it’s much harder to trick.

Technical Summary: Omni-Safety under Cross-Modality Conflict

1. Problem Statement

As Omni-modal Large Language Models (OLLMs) evolve to natively support text, image, audio, and video, they introduce a new dimension of security risk: cross-modality safety vulnerabilities. While existing research has extensively covered text-only safety and dual-modal (e.g., vision-language) alignment, there is a lack of understanding regarding how the interaction between multiple modalities affects a model's ability to refuse harmful requests.

The authors identify a Cross-Modality Safety Gap: OLLMs that are highly robust when receiving single-modal harmful inputs (e.g., text-only) become significantly more vulnerable when the same harmful intent is distributed across multiple modalities (e.g., a text prompt referring to a harmful object shown in an image).

2. Methodology

The paper approaches the problem through three stages: systematic evaluation, mechanistic analysis, and efficient alignment.

A. Evaluation: AdvBench-Omni
To isolate modality interaction from semantic content, the authors propose the Control Modal-variable Principle. They construct AdvBench-Omni, a dataset that uses a "semantic separation strategy." Instead of simply generating images of a "bomb," they split a query like "How to make a bomb?" into:

Context (Text): "How can I make the object in the image?"
Payload (Visual/Audio): A rendered image or audio of the word "bomb."
This ensures that the model must perform cross-modal reasoning to understand the harm, providing a "clean" environment to test interaction-driven vulnerabilities.

B. Mechanistic Analysis: Mid-layer Dissolution
The authors investigate the internal hidden states of OLLMs to find why safety fails. They identify two key phenomena:

Mid-layer Dissolution: In cross-modal inputs, the "refusal signal" (the vector indicating the model's intent to refuse) peaks in early layers but abruptly collapses in the middle layers.
Magnitude Shrinkage: Through variance decomposition, they discover that the safety failure is not caused by the refusal vector pointing in the wrong direction (directional deviation), but by a massive shrinkage in the magnitude of the refusal vector. Essentially, the "strength" of the refusal signal becomes too weak to trigger a response.
Subspace Geometry: Using PCA, they reveal that refusal vectors across all modalities reside in a low-dimensional subspace spanned by a "pure refusal direction" (the shared semantic intent) and a "modality bias direction" (the specific characteristics of text, audio, etc.).

C. Alignment: OmniSteer
To fix this, the authors propose OmniSteer, an efficient inference-time intervention method:

Golden Refusal Vector Extraction: They use Singular Value Decomposition (SVD) on refusal vectors from various modalities to extract a "golden" modality-invariant vector ( $v_{gold}$ ) that represents pure refusal semantics.
Layer-wise Adaptive Steering: Instead of applying a constant intervention strength, they train lightweight MLP adapters for each layer. These adapters take the current hidden state as input and dynamically predict an optimal steering coefficient ( $\alpha$ ) to boost the refusal signal without over-refusing benign queries.

3. Key Contributions

New Benchmark: The creation of AdvBench-Omni, the first omni-modal safety benchmark that rigorously decouples modality from semantics.
Mechanistic Insight: The discovery of the "Mid-layer Dissolution" phenomenon and the identification of refusal vector magnitude shrinkage as the primary driver of cross-modal vulnerability.
Efficient Defense: The development of OmniSteer, a plug-and-play, lightweight method that uses SVD-derived vectors and adaptive steering to restore safety.

4. Results

The method was tested on three major OLLMs (Qwen2.5-Omni-7B, Baichuan-Omni-1d5, and MiniCPM-o-2.6) across eight datasets:

Safety Improvement: OmniSteer increased the Refusal Success Rate (RSR) from a baseline of 69.9% to 91.2%.
Capability Preservation: Unlike traditional fine-tuning or heavy prompting, OmniSteer maintained a high Benign Acceptance Rate (BAR) and showed negligible impact on general reasoning tasks (as measured by OmniBench), with accuracy differences of less than 2% compared to vanilla models.
Superiority over Baselines: OmniSteer outperformed "Self-Reminder" (prompting) and "OmniGuard" (classifier-based) in both safety and the preservation of helpfulness.

5. Significance

This research is significant because it moves the field of AI safety from "modality-isolated" defense to "omni-modal" understanding. By proving that cross-modal safety failures are a structural issue in how models represent refusal signals during fusion, the paper provides a blueprint for building safer, more robust next-generation world models and agentic systems that can interact seamlessly across text, vision, and audio.