Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

• URL: http://arxiv.org/abs/2312.02119v3
• Date: Thu, 31 Oct 2024 15:57:42 GMT
• Title: Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
• Authors: Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, Amin Karbasi,
• Abstract summary: We present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks. TAP generates prompts that jailbreak state-of-the-art LLMs for more than 80% of the prompts. TAP is also capable of jailbreaking LLMs protected by state-of-the-art guardrails, e.g., LlamaGuard.
• Score: 34.36053833900958
• License:
• Abstract: While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks. In this work, we present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM. TAP utilizes an attacker LLM to iteratively refine candidate (attack) prompts until one of the refined prompts jailbreaks the target. In addition, before sending prompts to the target, TAP assesses them and prunes the ones unlikely to result in jailbreaks, reducing the number of queries sent to the target LLM. In empirical evaluations, we observe that TAP generates prompts that jailbreak state-of-the-art LLMs (including GPT4-Turbo and GPT4o) for more than 80% of the prompts. This significantly improves upon the previous state-of-the-art black-box methods for generating jailbreaks while using a smaller number of queries than them. Furthermore, TAP is also capable of jailbreaking LLMs protected by state-of-the-art guardrails, e.g., LlamaGuard.

Related papers

• SQL Injection Jailbreak: a structural disaster of large language models [71.55108680517422]
  We propose a novel jailbreak method, which utilizes the construction of input prompts by LLMs to inject jailbreak information into user prompts. Our SIJ method achieves nearly 100% attack success rates on five well-known open-source LLMs in the context of AdvBench.
  arXiv  Detail & Related papers  (2024-11-03T13:36:34Z)
• EnJa: Ensemble Jailbreak on Large Language Models [69.13666224876408]
  Large Language Models (LLMs) are increasingly being deployed in safety-critical applications. LLMs can still be jailbroken by carefully crafted malicious prompts, producing content that violates policy regulations. We propose a novel EnJa attack to hide harmful instructions using prompt-level jailbreak, boost the attack success rate using a gradient-based attack, and connect the two types of jailbreak attacks via a template-based connector.
  arXiv  Detail & Related papers  (2024-08-07T07:46:08Z)
• LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper [16.078682415975337]
  Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs) This paper proposes a lightweight yet practical defense called SELFDEFEND. It can defend against all existing jailbreak attacks with minimal delay for jailbreak prompts and negligible delay for normal user prompts.
  arXiv  Detail & Related papers  (2024-02-24T05:34:43Z)
• Comprehensive Assessment of Jailbreak Attacks Against LLMs [28.58973312098698]
  We study 13 cutting-edge jailbreak methods from four categories, 160 questions from 16 violation categories, and six popular LLMs. Our experimental results demonstrate that the optimized jailbreak prompts consistently achieve the highest attack success rates. We discuss the trade-off between the attack performance and efficiency, as well as show that the transferability of the jailbreak prompts is still viable.
  arXiv  Detail & Related papers  (2024-02-08T13:42:50Z)
• Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts [64.60375604495883]
  We discover a system prompt leakage vulnerability in GPT-4V. By employing GPT-4 as a red teaming tool against itself, we aim to search for potential jailbreak prompts leveraging stolen system prompts. We also evaluate the effect of modifying system prompts to defend against jailbreaking attacks.
  arXiv  Detail & Related papers  (2023-11-15T17:17:39Z)
• A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily [51.63085197162279]
  Large Language Models (LLMs) are designed to provide useful and safe responses. adversarial prompts known as 'jailbreaks' can circumvent safeguards. We propose ReNeLLM, an automatic framework that leverages LLMs themselves to generate effective jailbreak prompts.
  arXiv  Detail & Related papers  (2023-11-14T16:02:16Z)
• Jailbreaking Black Box Large Language Models in Twenty Queries [97.29563503097995]
  Large language models (LLMs) are vulnerable to adversarial jailbreaks. We propose an algorithm that generates semantic jailbreaks with only black-box access to an LLM.
  arXiv  Detail & Related papers  (2023-10-12T15:38:28Z)
• "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models [50.22128133926407]
  We conduct a comprehensive analysis of 1,405 jailbreak prompts spanning from December 2022 to December 2023. We identify 131 jailbreak communities and discover unique characteristics of jailbreak prompts and their major attack strategies. We identify five highly effective jailbreak prompts that achieve 0.95 attack success rates on ChatGPT (GPT-3.5) and GPT-4.
  arXiv  Detail & Related papers  (2023-08-07T16:55:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.