Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

1. 1Identify potential attack vectors in MLLMs, such as text, image, and audio inputs
2. 2Analyze the impact of adversarial manipulation on MLLM performance and security
3. 3Develop and evaluate countermeasures to mitigate adversarial threats, such as adversarial training and input validation
4. 4Investigate the transferability of adversarial attacks across different MLLM architectures and modalities

AI researchers and engineers working on multimodal large language models can benefit from this survey to understand potential vulnerabilities and develop countermeasures, while security teams can use this knowledge to protect against adversarial attacks

💡 Multimodal large language models are vulnerable to adversarial manipulation, which can have significant consequences for their security and performance

🚨 Adversarial attacks on multimodal large language models can compromise security and performance! 🤖