WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models (2406.18510v1)

Abstract: We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics, and then composes multiple tactics for systematic exploration of novel jailbreaks. Compared to prior work that performed red-teaming via recruited human workers, gradient-based optimization, or iterative revision with LLMs, our work investigates jailbreaks from chatbot users who were not specifically instructed to break the system. WildTeaming reveals previously unidentified vulnerabilities of frontier LLMs, resulting in up to 4.6x more diverse and successful adversarial attacks compared to state-of-the-art jailbreak methods. While many datasets exist for jailbreak evaluation, very few open-source datasets exist for jailbreak training, as safety training data has been closed even when model weights are open. With WildTeaming we create WildJailbreak, a large-scale open-source synthetic safety dataset with 262K vanilla (direct request) and adversarial (complex jailbreak) prompt-response pairs. To mitigate exaggerated safety behaviors, WildJailbreak provides two contrastive types of queries: 1) harmful queries (vanilla & adversarial) and 2) benign queries that resemble harmful queries in form but contain no harm. As WildJailbreak considerably upgrades the quality and scale of existing safety resources, it uniquely enables us to examine the scaling effects of data and the interplay of data properties and model capabilities during safety training. Through extensive experiments, we identify the training properties that enable an ideal balance of safety behaviors: appropriate safeguarding without over-refusal, effective handling of vanilla and adversarial queries, and minimal, if any, decrease in general capabilities. All components of WildJailbeak contribute to achieving balanced safety behaviors of models.

Overview of "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer LLMs"

The paper "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer LLMs" introduces WildTeaming, a novel red-teaming framework designed to enhance the safety of LLMs by systematically identifying and mitigating vulnerabilities. The paper highlights the unique aspects of WildTeaming, including the collection of in-the-wild user-chatbot interactions to discover a wide array of novel jailbreak tactics and the creation of a synthetic safety dataset, WildJailbreak, for safety training.

Key Contributions

1. WildTeaming Framework:
   • Mining Real-World Jailbreak Tactics: WildTeaming mines 105K human-devised jailbreak tactics from real-world user-chatbot interactions, identifying 5.7K unique clusters. This is a significant improvement over previous methods that relied on recruited human workers, gradient-based optimization, or iterative revision with LLMs.
   • Composing Diverse Adversarial Attacks: By combining different selections of mined tactics using LLMs such as Mixtral-8$ and GPT-4, WildTeaming generates a diverse set of adversarial attack candidates.
2. WildJailbreak Dataset:
   • Creation of a Large-Scale Safety Dataset: WildJailbreak contains 262K prompt-response pairs, including both vanilla and adversarial prompts. The dataset is uniquely designed to tackle exaggerated safety behaviors by providing contrastive types of queries: harmful and benign.
   • Systematic Safety Training: The dataset allows for the examination of data scaling effects and the interplay of data properties and model capabilities, leading to the identification of training properties that balance safety behaviors without over-refusal.
3. Evaluation and Results:
   • Effectiveness and Diversity Metrics: WildTeaming shows significant improvements in diversity and success rate of adversarial attacks compared to state-of-the-art methods such as PAIR and GCG. The results are validated using HarmBench, a unified jailbreaking evaluation benchmark.
   • Safety Training Insights: Training with WildJailbreak considerably enhances model robustness against both vanilla and adversarial queries, demonstrating the importance of comprehensive safety datasets.

Implications and Future Developments

The research has significant implications for both practical applications and theoretical advancements in AI safety:

1. Enhanced Model Safety: By systematically identifying and mitigating vulnerabilities in LLMs, WildTeaming contributes to building safer AI systems that are robust against a wide range of adversarial attacks.
2. Open-Source Safety Resources: The release of WildJailbreak as an open-source dataset can facilitate further research and development in AI safety, promoting transparency and collaboration within the research community.
3. Evolving Safety Evaluation: The paper underscores the need for dynamic and scalable safety evaluation methods that can keep pace with the evolving capabilities of LLMs.
4. Comprehensive Safety Alignment: The insights gained from this research pave the way for future studies aimed at understanding the best practices for safety alignment, including the trade-offs between supervised fine-tuning, DPO, PPO, and the use of plug-in safety filters.

Conclusion

The paper "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer LLMs" makes a substantial contribution to the field of AI safety by introducing a scalable and systematic approach to uncover and mitigate vulnerabilities in LLMs. The development and release of the WildJailbreak dataset offer a valuable resource for enhancing model safety, and the empirical insights from the research provide a solid foundation for future advancements in safety training and evaluation methods.

markdown ## Overview of "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer LLMs" The paper "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer LLMs" introduces WildTeaming, a novel red-teaming framework designed to enhance the safety of LLMs by systematically identifying and mitigating vulnerabilities. The paper highlights the unique aspects of WildTeaming, including the collection of in-the-wild user-chatbot interactions to discover a wide array of novel jailbreak tactics and the creation of a synthetic safety dataset, WildJailbreak, for safety training. ### Key Contributions 1. **WildTeaming Framework**: - **Mining Real-World Jailbreak Tactics**: WildTeaming mines 105K human-devised jailbreak tactics from real-world user-chatbot interactions, identifying 5.7K unique clusters. This is a significant improvement over previous methods that relied on recruited human workers, gradient-based optimization, or iterative revision with LLMs. - **Composing Diverse Adversarial Attacks**: By combining different selections of mined tactics using LLMs such as Mixtral-8$ and GPT-4, WildTeaming generates a diverse set of adversarial attack candidates. 2. **WildJailbreak Dataset**: - **Creation of a Large-Scale Safety Dataset**: WildJailbreak contains 262K prompt-response pairs, including both vanilla and adversarial prompts. The dataset is uniquely designed to tackle exaggerated safety behaviors by providing contrastive types of queries: harmful and benign. - **Systematic Safety Training**: The dataset allows for the examination of data scaling effects and the interplay of data properties and model capabilities, leading to the identification of training properties that balance safety behaviors without over-refusal. 3. **Evaluation and Results**: - **Effectiveness and Diversity Metrics**: WildTeaming shows significant improvements in diversity and success rate of adversarial attacks compared to state-of-the-art methods such as PAIR and GCG. The results are validated using HarmBench, a unified jailbreaking evaluation benchmark. - **Safety Training Insights**: Training with WildJailbreak considerably enhances model robustness against both vanilla and adversarial queries, demonstrating the importance of comprehensive safety datasets. ### Implications and Future Developments The research has significant implications for both practical applications and theoretical advancements in AI safety: 1. **Enhanced Model Safety**: By systematically identifying and mitigating vulnerabilities in LLMs, WildTeaming contributes to building safer AI systems that are robust against a wide range of adversarial attacks. 2. **Open-Source Safety Resources**: The release of WildJailbreak as an open-source dataset can facilitate further research and development in AI safety, promoting transparency and collaboration within the research community. 3. **Evolving Safety Evaluation**: The paper underscores the need for dynamic and scalable safety evaluation methods that can keep pace with the evolving capabilities of LLMs. 4. **Comprehensive Safety Alignment**: The insights gained from this research pave the way for future studies aimed at understanding the best practices for safety alignment, including the trade-offs between supervised fine-tuning, DPO, PPO, and the use of plug-in safety filters. ### Conclusion The paper "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer LLMs" makes a substantial contribution to the field of AI safety by introducing a scalable and systematic approach to uncover and mitigate vulnerabilities in LLMs. The development and release of the WildJailbreak dataset offer a valuable resource for enhancing model safety, and the empirical insights from the research provide a solid foundation for future advancements in safety training and evaluation methods.