Automated Red Teaming with GOAT: the Generative Offensive Agent Tester (2410.01606v1)

Abstract: Red teaming assesses how LLMs can produce content that violates norms, policies, and rules set during their safety training. However, most existing automated methods in the literature are not representative of the way humans tend to interact with AI models. Common users of AI models may not have advanced knowledge of adversarial machine learning methods or access to model internals, and they do not spend a lot of time crafting a single highly effective adversarial prompt. Instead, they are likely to make use of techniques commonly shared online and exploit the multiturn conversational nature of LLMs. While manual testing addresses this gap, it is an inefficient and often expensive process. To address these limitations, we introduce the Generative Offensive Agent Tester (GOAT), an automated agentic red teaming system that simulates plain language adversarial conversations while leveraging multiple adversarial prompting techniques to identify vulnerabilities in LLMs. We instantiate GOAT with 7 red teaming attacks by prompting a general-purpose model in a way that encourages reasoning through the choices of methods available, the current target model's response, and the next steps. Our approach is designed to be extensible and efficient, allowing human testers to focus on exploring new areas of risk while automation covers the scaled adversarial stress-testing of known risk territory. We present the design and evaluation of GOAT, demonstrating its effectiveness in identifying vulnerabilities in state-of-the-art LLMs, with an ASR@10 of 97% against Llama 3.1 and 88% against GPT-4 on the JailbreakBench dataset.

Automated Red Teaming with GOAT: the Generative Offensive Agent Tester

The paper "Automated Red Teaming with GOAT: the Generative Offensive Agent Tester" addresses the increasingly pertinent concern of ensuring the robustness and reliability of LLMs against adversarial manipulation. Red teaming serves as a crucial practice for evaluating model vulnerabilities in terms of ethical and policy adherence. However, existing automated methodologies do not adequately reflect the interaction patterns typical of human users, who tend to employ readily available techniques over multiple conversational turns rather than crafting singular, highly effective adversarial prompts.

Introduction and Motivation

In acknowledging the evolving nature of LLM use, where users engage in multi-turn dialogues with chatbots, the authors introduce GOAT, the Generative Offensive Agent Tester. GOAT is designed to simulate adversarial conversations akin to those conducted by human testers, leveraging a diverse set of adversarial prompting techniques within realistic conversational settings. By doing so, GOAT aims to identify and address vulnerabilities in LLMs more efficiently and at scale.

Methodology

GOAT operates through a structured yet adaptable approach, anchored by three primary system components: Red Teaming Attacks, Attacker LLM Reasoning, and Multi-Turn Conversation Chaining.

1. Red Teaming Attacks:
   • The attack strategies in GOAT are categorized and embedded within the system prompt of the attacker LLM. This includes methods like Response Priming, Refusal Suppression, and Persona Modification, among others.
   • These attacks are constructed to be easily extended and combined, allowing for dynamic and layered application through multi-turn interactions.
2. Attacker LLM Reasoning:
   • The attacker LLM employs Chain-of-Thought prompting, enhancing its ability to reason through the conversation, adjusting strategies based on the model's responses.
   • This reasoning framework insists on formulating observations, thoughts, strategies, and responses for each conversational turn.
3. Multi-Turn Conversation Chaining:
   • The attacker and target LLMs are paired to engage in an adversarial conversation. The attacker LLM dynamically generates prompts based on ongoing conversation trends and prior responses, significantly improving the simulation of real-world adversarial probing.

Experimental Setup and Results

The researchers tested GOAT against leading models such as Llama 3.1, GPT-4-Turbo, among others, using the JailbreakBench dataset for evaluation.

• Success Metrics and Dataset:
  • Attack success rate (ASR) was evaluated within a maximum of 5 conversation turns. GOAT demonstrated superior ASR@10 (97% against Llama 3.1 and 88% against GPT-4-Turbo), outperforming existing methods like Crescendo within equivalent query budgets.
• Numerical Results:
  • The effectiveness of GOAT in identifying vulnerabilities was quantified by ASR metrics, notably achieving higher rates within fewer conversational turns, thereby validating the efficiency and scalability of the proposed methodology.

Implications and Future Developments

The introduction of GOAT has significant implications for both practical security and theoretical advancements in AI safety:

1. Practical Implications:
   • GOAT facilitates scalable adversarial testing, reducing reliance on cost-intensive manual testing while expanding coverage of potential vulnerabilities in LLMs. Its ability to simulate multi-turn adversarial conversations provides a more realistic and comprehensive assessment of model robustness.
2. Theoretical Implications:
   • The dynamic reasoning incorporated within GOAT's framework underscores the importance of adaptive strategies in adversarial testing. Future developments could explore deeper integrations of memory retention and long-context reasoning to augment the capabilities of such automated systems.
3. Speculative Future Developments in AI:
   • As LLMs continue to evolve, integrating more sophisticated adversarial probing methodologies like GOAT will become crucial. Future research may focus on expanding these techniques to cover a broader array of adversarial behaviors, further refining the automation in identifying and mitigating potential vulnerabilities.

Conclusion

The paper delineates a structured yet extensible approach to adversarial testing with GOAT, showcasing its efficacy in identifying vulnerabilities in state-of-the-art LLMs. By mimicking human-like adversarial interactions, GOAT presents a pragmatic advancement in the field of AI safety, promoting the development of more resilient and ethically sound AI systems. Such advancements not only fortify the operational safety of AI but also set a precedent for future research in automated adversarial testing methodologies.