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Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities

Authors: Yuan Xiong, Ziqi Miao, Lijun Li, Chen Qian, Jie Li, Jing Shao

Published: 2025-12-02

arXiv ID: 2512.02973v1

Added to Library: 2025-12-03 03:01 UTC

Red Teaming

📄 Abstract

While Multimodal Large Language Models (MLLMs) show remarkable capabilities, their safety alignments are susceptible to jailbreak attacks. Existing attack methods typically focus on text-image interplay, treating the visual modality as a secondary prompt. This approach underutilizes the unique potential of images to carry complex, contextual information. To address this gap, we propose a new image-centric attack method, Contextual Image Attack (CIA), which employs a multi-agent system to subtly embeds harmful queries into seemingly benign visual contexts using four distinct visualization strategies. To further enhance the attack's efficacy, the system incorporate contextual element enhancement and automatic toxicity obfuscation techniques. Experimental results on the MMSafetyBench-tiny dataset show that CIA achieves high toxicity scores of 4.73 and 4.83 against the GPT-4o and Qwen2.5-VL-72B models, respectively, with Attack Success Rates (ASR) reaching 86.31\% and 91.07\%. Our method significantly outperforms prior work, demonstrating that the visual modality itself is a potent vector for jailbreaking advanced MLLMs.

🔍 Key Points

  • Introduction of Contextual Image Attack (CIA) as a novel jailbreak method for Multimodal Large Language Models (MLLMs), highlighting the significance of visual context in evading safety mechanisms.
  • Development of a multi-agent system composed of a Parser, an Image Generator, and a dual-path Refiner, enabling the systematic generation of contextual images that embed harmful queries.
  • Demonstration of CIA's superior attack effectiveness, achieving high toxicity scores and attack success rates over baseline methods, particularly on models like GPT-4o and Qwen2.5-VL-72B.
  • Identification of critical vulnerabilities in MLLM safety alignments when visual context is utilized in adversarial attacks, emphasizing the underexplored risks associated with visual inputs.
  • Emphasis on the need for robust defenses against CIA-like attacks, highlighting the implications for future research directions in the area of AI safety.

💡 Why This Paper Matters

This paper offers significant contributions to the field of AI safety by elucidating how visual contexts can be manipulated to expose vulnerabilities in advanced MLLMs. The proposed Contextual Image Attack not only showcases an innovative technique for mounting effective jailbreaking attacks but also underscores the necessity for enhanced safety measures in multimodal models to address such risks. Given the growing reliance on MLLMs in sensitive applications, the implications of this research are critical for ensuring responsible AI deployment.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers will find this paper especially relevant as it exposes new vulnerabilities in MLLMs, providing insights into the interaction between text and visual modalities in adversarial contexts. The detailed methodology and experimental results highlight crucial aspects of model design that could be exploited, thus serving as a foundation for developing more robust safety mechanisms. Moreover, understanding the implications of visual context in adversarial attacks will guide future research on enhancing the resilience of AI systems against such threats.

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