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Comprehensive Evaluation of Jailbreak Attacks on Large Language Models and Multimodal Large Language Models

Core Concepts
This study provides a comprehensive evaluation of the robustness of both proprietary and open-source large language models (LLMs) and multimodal large language models (MLLMs) against various jailbreak attack methods targeting both textual and visual inputs.
The study first constructed a comprehensive jailbreak evaluation dataset with 1445 harmful behavior questions covering 11 different safety policies. It then conducted extensive red-teaming experiments on 11 different LLMs and MLLMs, including both state-of-the-art proprietary models like GPT-4 and GPT-4V, as well as open-source models like Llama2 and Vicuna. The key findings are: GPT-4 and GPT-4V demonstrate significantly better robustness against both textual and visual jailbreak attacks compared to open-source models. Among open-source models, Llama2-7B and Qwen-VL-Chat are more robust, with Llama2-7B even outperforming GPT-4 in some cases. The transferability of visual jailbreak methods is relatively limited compared to textual jailbreak methods. Automatic jailbreak methods like AutoDAN show better transferability across models compared to GCG. There is a significant gap in the robustness between proprietary and open-source models, indicating the importance of comprehensive safety alignment for large language models.
GPT-4 and GPT-4V demonstrate jailbreak success rates below 2.5% across most attack methods. Llama2-7B has a jailbreak success rate below 1% for the GCG attack, but up to 11% for the AutoDAN attack. Vicuna-7B has the highest jailbreak success rates among the open-source models, reaching over 50% for some automatic attack methods. FigStep, a visual jailbreak method, achieves over 30% success rates on some open-source multimodal models, but less than 0.1% on GPT-4V.
"GPT-4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs." "Among open-source models, Llama2-7B is the most robust model whereas Vicuna-7B is the most vulnerable one." "Visual jailbreak methods have relatively limited transferability compared to textual jailbreak methods."

Key Insights Distilled From

by Shuo Chen,Zh... at 04-05-2024
Red Teaming GPT-4V

Deeper Inquiries

How can the safety alignment process for open-source language models be improved to match the robustness of proprietary models?

To enhance the safety alignment process for open-source language models and align them with the robustness of proprietary models like GPT-4 and GPT-4V, several strategies can be implemented: Fine-tuning for Safety: Open-source models can undergo rigorous fine-tuning processes specifically focused on safety alignment. This involves training the model on curated datasets that emphasize ethical and safe responses, similar to how proprietary models are fine-tuned. Continuous Monitoring and Updating: Regular monitoring of model behavior and continuous updates to the safety mechanisms can help identify and address vulnerabilities promptly. This proactive approach ensures that the model remains resilient to emerging threats. Collaborative Research and Benchmarking: Collaboration between researchers, developers, and the community can lead to the creation of universal evaluation benchmarks and standardized metrics for assessing safety. This shared knowledge can help improve the overall safety alignment process. Transparency and Accountability: Open-source models should prioritize transparency in their training data, model architecture, and decision-making processes. This transparency fosters accountability and trust among users and researchers. Community Engagement: Engaging with the broader AI community to gather feedback, insights, and diverse perspectives can provide valuable input for enhancing the safety alignment process. This collaborative approach can lead to more comprehensive and effective safety measures. By implementing these strategies and fostering a culture of continuous improvement and collaboration, open-source language models can improve their safety alignment processes and match the robustness of proprietary models.

What are the potential vulnerabilities in the safety mechanisms of GPT-4 and GPT-4V that could be exploited by future jailbreak attacks?

While GPT-4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source models, there are still potential vulnerabilities in their safety mechanisms that could be exploited by future attacks: Transferability Issues: The limited transferability of visual jailbreak methods suggests that attackers may find ways to enhance the transferability of these methods to target GPT-4 and GPT-4V. Adversarial examples that can bypass the model's safety mechanisms may pose a significant threat. Semantic Ambiguity: GPT-4 and GPT-4V may still struggle with understanding and contextualizing nuanced or ambiguous prompts, leaving room for attackers to craft deceptive inputs that exploit these weaknesses. Adversarial Prompt Engineering: Future attackers could develop more sophisticated prompt engineering techniques that evade the models' safety filters. By carefully crafting prompts that manipulate the model's decision-making process, attackers could elicit harmful responses. Model Biases: GPT-4 and GPT-4V may still exhibit biases in their outputs, despite safety alignment efforts. Attackers could exploit these biases to generate harmful or unethical content by leveraging the model's inherent biases. Lack of Real-time Monitoring: Without robust real-time monitoring systems in place, malicious inputs may go undetected, allowing attackers to exploit vulnerabilities in the safety mechanisms of GPT-4 and GPT-4V. By addressing these potential vulnerabilities through continuous monitoring, robust testing, and proactive mitigation strategies, the safety mechanisms of GPT-4 and GPT-4V can be strengthened to mitigate the risks of future jailbreak attacks.

How can the insights from this study be applied to develop more comprehensive and proactive safety evaluation frameworks for emerging large language models?

The insights from this study can be instrumental in developing more comprehensive and proactive safety evaluation frameworks for emerging large language models by: Dataset Construction: Building on the dataset construction methodology used in this study, future frameworks can curate diverse and extensive datasets that cover a wide range of harmful behaviors and scenarios. This ensures that models are tested comprehensively against potential threats. Red-Teaming Experiments: Conducting red-teaming experiments, as done in this study, can help identify vulnerabilities and weaknesses in models' safety mechanisms. These experiments should be expanded to include a variety of attack methods and models to provide a holistic evaluation. Evaluation Metrics: Standardizing evaluation metrics, such as refusal word detection and LLMs as judges, can provide consistent and objective measures of model robustness. These metrics should be refined and adapted to suit the specific characteristics of emerging large language models. Continuous Improvement: Implementing a cycle of continuous improvement based on the findings from red-teaming experiments can enhance the safety evaluation frameworks over time. Regular updates and adaptations to address new threats and vulnerabilities are essential. Community Collaboration: Encouraging collaboration and knowledge-sharing within the AI community can lead to the development of best practices and guidelines for safety evaluation. Engaging researchers, developers, and stakeholders in ongoing discussions can enrich the frameworks with diverse perspectives. By incorporating these strategies and leveraging the insights gained from this study, more proactive and robust safety evaluation frameworks can be established to ensure the ethical and safe deployment of emerging large language models.