Adversarial Prompt Injection Attack on Multimodal Large Language Models

📰 ArXiv cs.AI

Researchers introduce an adversarial prompt injection attack on multimodal large language models using imperceptible visual prompts

advanced Published 1 Apr 2026

Action Steps

Identify potential vulnerabilities in multimodal large language models
Design imperceptible visual prompts to inject malicious instructions
Evaluate the effectiveness of the attack on closed-source MLLMs
Develop countermeasures to mitigate the attack, such as input validation and filtering

Who Needs to Know This

AI researchers and engineers working on multimodal large language models can benefit from understanding this attack to improve model robustness, while security teams can use this knowledge to develop countermeasures

Key Insight

💡 Multimodal large language models are vulnerable to adversarial prompt injection attacks using imperceptible visual prompts

Key Takeaways

Researchers introduce an adversarial prompt injection attack on multimodal large language models using imperceptible visual prompts

Full Article

Title: Adversarial Prompt Injection Attack on Multimodal Large Language Models

Abstract:
arXiv:2603.29418v1 Announce Type: cross Abstract: Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior leaves them vulnerable to prompt injection attacks. Existing prompt injection methods predominantly rely on textual prompts or perceptible visual prompts that are observable by human users. In this work, we study imperceptible visual prompt injection against powerful closed-source MLLMs, where adversarial in

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