Security Analysis: Are GPT-4V Models Safe Against Jailbreak Attacks? delivers an in-depth examination of large language models, particularly focusing on GPT-4V, to understand their susceptibility to jailbreak attacks. Jailbreak attacks, which seek to bypass model restrictions, pose significant risks for the deployment and trustworthiness of AI services.

This paper’s contributions include:

• Creating a comprehensive dataset for evaluating the robustness of LLMs in the context of 11 different safety policies.
• Conducting rigorous red-teaming experiments on various state-of-the-art models, including both proprietary and open-source LLMs.
• Presenting deep analyses of findings, uncovering the relative robustness levels among different LLMs and MLLMs.

The analysis reveals that GPT-4V models are relatively robust against such jailbreak attempts, offering insights into the security measures necessary for future AI development. Understanding the safety mechanisms of these models is critical in ensuring they can be used responsibly without unintended consequences.

The implications of such studies are vast for developers, regulators, and users of AI technologies, as they shed light on potential vulnerabilities and the effectiveness of current safeguards.