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All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks (2401.09798v3)

Published 18 Jan 2024 in cs.CL, cs.AI, and cs.CY

Abstract: LLMs, such as ChatGPT, encounter `jailbreak' challenges, wherein safeguards are circumvented to generate ethically harmful prompts. This study introduces a straightforward black-box method for efficiently crafting jailbreak prompts, addressing the significant complexity and computational costs associated with conventional methods. Our technique iteratively transforms harmful prompts into benign expressions directly utilizing the target LLM, predicated on the hypothesis that LLMs can autonomously generate expressions that evade safeguards. Through experiments conducted with ChatGPT (GPT-3.5 and GPT-4) and Gemini-Pro, our method consistently achieved an attack success rate exceeding 80% within an average of five iterations for forbidden questions and proved robust against model updates. The jailbreak prompts generated were not only naturally-worded and succinct but also challenging to defend against. These findings suggest that the creation of effective jailbreak prompts is less complex than previously believed, underscoring the heightened risk posed by black-box jailbreak attacks.

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References (25)
  1. OpenAI. Introducing chatgpt. OpenAI Blog, 11 2022.
  2. A review of chatgpt applications in education, marketing, software engineering, and healthcare: Benefits, drawbacks, and research directions. arXiv preprint arXiv:2305.00237, 2023.
  3. Malik Sallam. Chatgpt utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 2023.
  4. Large language models in medicine. Nature medicine, 29(8):1930–1940, 2023.
  5. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  6. Toxicity in chatgpt: Analyzing persona-assigned language models. arXiv preprint arXiv:2304.05335, 2023.
  7. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022.
  8. A holistic approach to undesired content detection in the real world. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 15009–15018, 2023.
  9. Are aligned neural networks adversarially aligned? arXiv preprint arXiv:2306.15447, 2023.
  10. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
  11. "do anything now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models. arXiv preprint arXiv:2308.03825, 2023.
  12. coolaj86. Chat gpt “dan”’ (and other “jailbreaks”). GitHub Gist, 10 2023.
  13. Autodan: Automatic and interpretable adversarial attacks on large language models. arXiv preprint arXiv:2310.15140, 2023.
  14. Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043, 2023.
  15. Smoothllm: Defending large language models against jailbreaking attacks. arXiv preprint arXiv:2310.03684, 2023.
  16. Defending chatgpt against jailbreak attack via self-reminders. Nature Machine Intelligence, pages 1–11, 2023.
  17. Detecting language model attacks with perplexity. arXiv preprint arXiv:2308.14132, 2023.
  18. Baseline defenses for adversarial attacks against aligned language models. arXiv preprint arXiv:2309.00614, 2023.
  19. Open sesame! universal black box jailbreaking of large language models. arXiv preprint arXiv:2309.01446, 2023.
  20. Jailbreaking black box large language models in twenty queries. arXiv preprint arXiv:2310.08419, 2023.
  21. Ignore previous prompt: Attack techniques for language models. arXiv preprint arXiv:2211.09527, 2022.
  22. Jailbreaking chatgpt via prompt engineering: An empirical study. arXiv preprint arXiv:2305.13860, 2023.
  23. Tricking llms into disobedience: Understanding, analyzing, and preventing jailbreaks. arXiv preprint arXiv:2305.14965, 2023.
  24. Survey of vulnerabilities in large language models revealed by adversarial attacks. arXiv preprint arXiv:2310.10844, 2023.
  25. Adversarial attacks on deep-learning models in natural language processing: A survey. ACM Transactions on Intelligent Systems and Technology (TIST), 11(3):1–41, 2020.
Citations (13)
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Summary

  • The paper proposes a simplified black-box jailbreak approach using iterative LLM rewriting to evade safeguards with over 80% success.
  • It demonstrates method efficiency by reducing computational complexity and eliminating the need for white-box access.
  • The findings expose significant vulnerabilities in current LLM defenses and urge the development of more robust countermeasures.

Overview of the Jailbreak Challenge in LLMs

LLMs are increasingly permeating various sectors, offering a suite of capabilities that promise to transform industries such as education and healthcare. Given their extensive training on diverse textual data, these models sometimes generate content that can be ethically problematic, challenging their broader application. Providers of these models have put in place various safeguarding measures aimed at aligning LLM outputs with ethical standards by blocking prompts that could lead to undesirable content. Nonetheless, these defenses are not impregnable; there are ways to bypass them, termed 'jailbreak attacks.' These attacks are areas of active research, as they pose significant security implications for the application of LLMs.

Simplifying Jailbreak Attacks

Historically, jailbreak attacks have either been manually engineered or generated through more labor-intensive, computationally expensive means such as gradient-based optimization in open-source models. Now, this paper proposes a more straightforward black-box method to create jailbreak prompts. This method cleverly uses the target LLM to rephrase potentially harmful prompts into less detectable versions that evade safeguards. Through iterative rewrites of the harmful texts by the LLM itself, the researchers showcased the somewhat unsettling ease with which robust jailbreak prompts can be crafted. Remarkably, this simplified approach yielded an over 80% success rate in penetrating defenses within an average of five iterations.

Utility and Efficacy of the New Method

The paper underscores the high success rate and efficiency of the proposed method across several LLMs and updates. It emphasizes the natural language composition of the generated jailbreak prompts, highlighting their potential to go undetected by current safeguard mechanisms due to their conciseness. Contrary to previous jailbreak methods that may necessitate a white-box environment for LLMs, this approach requires no such complex infrastructure and can be executed using standard user computing resources and the LLM's API. Such simplicity indicates a more significant potential threat to the current black-box models.

Implications and the Path Forward

The significance of this paper extends beyond the technical achievement of yielding effective jailbreak prompts. It casts a spotlight on the existing vulnerabilities within leading-edge LLMs, urging a reassessment of the robustness of current defense strategies. At the same time, it opens up avenues for future research to refine these defenses against evolving attack methods, ensuring LLM operation remains within ethical bounds. Moreover, regularly reassessing these defense mechanisms against new datasets of potentially harmful content may further fortify LLMs against jailbreak attempts. The paper serves as a wake-up call for model providers to anticipate and prepare for more sophisticated attacks that leverage the model's own capabilities to undermine established safeguards.

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  1. GitHub - kztakemoto/simbaja: All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks (17 stars)