Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models

• URL: http://arxiv.org/abs/2410.11459v1
• Date: Tue, 15 Oct 2024 10:07:15 GMT
• Title: Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models
• Authors: Hao Yang, Lizhen Qu, Ehsan Shareghi, Gholamreza Haffari,
• Abstract summary: Large language models (LLMs) have exhibited outstanding performance in engaging with humans. LLMs are vulnerable to jailbreak attacks, leading to the generation of harmful responses. We propose Jigsaw Puzzles (JSP), a straightforward yet effective multi-turn jailbreak strategy against the advanced LLMs.
• Score: 50.89022445197919
• License:
• Abstract: Large language models (LLMs) have exhibited outstanding performance in engaging with humans and addressing complex questions by leveraging their vast implicit knowledge and robust reasoning capabilities. However, such models are vulnerable to jailbreak attacks, leading to the generation of harmful responses. Despite recent research on single-turn jailbreak strategies to facilitate the development of defence mechanisms, the challenge of revealing vulnerabilities under multi-turn setting remains relatively under-explored. In this work, we propose Jigsaw Puzzles (JSP), a straightforward yet effective multi-turn jailbreak strategy against the advanced LLMs. JSP splits questions into harmless fractions as the input of each turn, and requests LLMs to reconstruct and respond to questions under multi-turn interaction. Our experimental results demonstrate that the proposed JSP jailbreak bypasses original safeguards against explicitly harmful content, achieving an average attack success rate of 93.76% on 189 harmful queries across 5 advanced LLMs (Gemini-1.5-Pro, Llama-3.1-70B, GPT-4, GPT-4o, GPT-4o-mini). Moreover, JSP achieves a state-of-the-art attack success rate of 92% on GPT-4 on the harmful query benchmark, and exhibits strong resistant to defence strategies. Warning: this paper contains offensive examples.

Related papers

• Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities [63.603861880022954]
  We introduce ADV-LLM, an iterative self-tuning process that crafts adversarial LLMs with enhanced jailbreak ability. Our framework significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. It exhibits strong attack transferability to closed-source models, achieving 99% ASR on GPT-3.5 and 49% ASR on GPT-4, despite being optimized solely on Llama3.
  arXiv  Detail & Related papers  (2024-10-24T06:36:12Z)
• Deciphering the Chaos: Enhancing Jailbreak Attacks via Adversarial Prompt Translation [71.92055093709924]
  We propose a novel method that "translates" garbled adversarial prompts into coherent and human-readable natural language adversarial prompts. It also offers a new approach to discovering effective designs for jailbreak prompts, advancing the understanding of jailbreak attacks. Our method achieves over 90% attack success rates against Llama-2-Chat models on AdvBench, despite their outstanding resistance to jailbreak attacks.
  arXiv  Detail & Related papers  (2024-10-15T06:31:04Z)
• RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking [30.67803190789498]
  We propose a new jailbreak approach, RED QUEEN ATTACK, that constructs a multi-turn scenario, concealing the malicious intent under the guise of preventing harm. Our experiments reveal that all LLMs are vulnerable to RED QUEEN ATTACK, reaching 87.62% attack success rate on GPT-4o and 75.4% on Llama3-70B. To prioritize safety, we introduce a straightforward mitigation strategy called RED QUEEN GUARD, which aligns LLMs to effectively counter adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-26T01:24:17Z)
• Effective and Evasive Fuzz Testing-Driven Jailbreaking Attacks against LLMs [33.87649859430635]
  Large Language Models (LLMs) have excelled in various tasks but are still vulnerable to jailbreaking attacks. We introduce a novel jailbreaking attack framework that adapts the black-box fuzz testing approach with a series of customized designs. Our method achieves attack success rates of over 90%,80% and 74%, respectively, exceeding existing baselines by more than 60%.
  arXiv  Detail & Related papers  (2024-09-23T10:03:09Z)
• Figure it Out: Analyzing-based Jailbreak Attack on Large Language Models [21.252514293436437]
  We propose Analyzing-based Jailbreak (ABJ) to combat jailbreak attacks on Large Language Models (LLMs) ABJ achieves 94.8% attack success rate (ASR) and 1.06 attack efficiency (AE) on GPT-4-turbo-0409, demonstrating state-of-the-art attack effectiveness and efficiency.
  arXiv  Detail & Related papers  (2024-07-23T06:14:41Z)
• Safe Unlearning: A Surprisingly Effective and Generalizable Solution to Defend Against Jailbreak Attacks [89.54736699767315]
  We conjecture that directly unlearn the harmful knowledge in the LLM can be a more effective way to defend against jailbreak attacks. Our solution reduced the Attack Success Rate (ASR) in Vicuna-7B from 82.6% to 7.7% on out-of-distribution (OOD) harmful questions. This significantly outperforms Llama2-7B-Chat, which is fine-tuned on about 0.1M safety alignment samples but still has an ASR of 21.9% even under the help of an additional safety system prompt.
  arXiv  Detail & Related papers  (2024-07-03T07:14:05Z)
• Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs [13.317364896194903]
  Large Language Models (LLMs) have demonstrated significant capabilities in executing complex tasks in a zero-shot manner. LLMs are susceptible to jailbreak attacks and can be manipulated to produce harmful outputs.
  arXiv  Detail & Related papers  (2024-06-13T17:01:40Z)
• Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks [59.46556573924901]
  This paper introduces Defensive Prompt Patch (DPP), a novel prompt-based defense mechanism for large language models (LLMs) Unlike previous approaches, DPP is designed to achieve a minimal Attack Success Rate (ASR) while preserving the high utility of LLMs. Empirical results conducted on LLAMA-2-7B-Chat and Mistral-7B-Instruct-v0.2 models demonstrate the robustness and adaptability of DPP.
  arXiv  Detail & Related papers  (2024-05-30T14:40:35Z)
• EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models [53.87416566981008]
  This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against Large Language Models (LLMs) It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator. Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks.
  arXiv  Detail & Related papers  (2024-03-18T18:39:53Z)
• 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)

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.