Adversarial Prompt Injection Attack on Multimodal Large Language Models

1. 1Identify potential vulnerabilities in multimodal large language models
2. 2Design imperceptible visual prompts to inject malicious instructions
3. 3Evaluate the effectiveness of the attack on closed-source MLLMs
4. 4Develop countermeasures to mitigate the attack, such as input validation and filtering

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

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

🚨 New attack on multimodal LLMs: imperceptible visual prompt injection 🚨