USD adversarial images induce false positives in multimodal guard models, blocking legitimate requests

Auto-published from arXiv:2608.01373 by the mine-arxiv pipeline. Reviewed by an LLM judge (Sonnet) against the archive bar — see CONTRIBUTING. Notes: cleared review (confidence 0.85, flags: [possible-zero-day]) ⚠ needsDisclosureReview = true — review for undisclosed vulnerability before any public submission.

Category

over-refusal

Model

Llama-Guard-3-Vision, LLaVA-Guard-v1.2, Qwen-Guard-v1.2, ShieldVLM

Surface

VLM input (image + text prompt submitted to a multimodal guard/safety-filter API)

Setup

An adversary crafts ℓ∞-bounded (ε=16/255) perturbations on benign images using the Unsafe Semantic Distillation (USD) method, which aligns perturbation embeddings with a distributional representation of multiple unsafe reference images (from HolisticSafeBench / VHD11K) via a temperature-scaled LogSumExp loss plus decision-level cross-entropy. The perturbed image is then submitted by an unsuspecting user alongside ordinary prompts such as 'What does this image depict?' or 'Can you edit the image to make the car look like it's painted bright cherry red?' (from Appendix C). No prompt text is controlled by the attacker.

Observed behavior

Guard models reject the benign image+prompt pair as unsafe at 75.6–85.9% attack success rate (ASRr) across all four tested models under realistic diverse-prompt simulation. The models produce explicit refusal responses with flagged policy categories (e.g., Llama-Guard-3-Vision outputs 'unsafe, S1: Violent Crimes') even though the underlying image is an innocuous scene such as a parked van.

Expected behavior

The guard model should classify the benign image+prompt pair as safe and pass the request to the downstream VLM for normal processing.

Reproducibility

medium

Threat model

A malicious actor distributes imperceptibly perturbed images via social media or file-sharing platforms; unsuspecting users incorporate these images into legitimate queries on platforms (image editors, VQA services, content generators) that use multimodal guard models as upstream safety filters. The result is systematic service denial—an availability attack that also erodes user trust in the platform's safety infrastructure ('Boy Who Cried Wolf' effect).

Novelty

First demonstration that multimodal guard models are vulnerable to a prompt-agnostic false-positive (availability) attack, exposing an entirely unexplored inverse threat to the classic jailbreak false-negative paradigm.

Source

• arXiv: 2608.01373
• PDF: https://arxiv.org/pdf/2608.01373
• Categories: cs.CR
• Authors: Shuo Shi, Rui Yin, Naen Xu, Jiahao Chen, Chunyi Zhou, Tianyu Du, Zhihui Fu, Jun Wang, Zhaoxiang Wang, Shouling Ji

Triage notes (auto)

• paperType: red-team-vuln
• estimatedCaseCount: 3
• triage reason: Novel adversarial attack (Unsafe Semantic Distillation) that induces false positives on benign inputs across four guard models with 84% success rate. Demonstrates reproducible model-level safety failures in deployed multimodal safety filters.

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