Diversity Helps Jailbreak Large Language Models

• URL: http://arxiv.org/abs/2411.04223v1
• Date: Wed, 06 Nov 2024 19:39:48 GMT
• Title: Diversity Helps Jailbreak Large Language Models
• Authors: Weiliang Zhao, Daniel Ben-Levi, Junfeng Yang, Chengzhi Mao,
• Abstract summary: We have uncovered a powerful jailbreak technique that leverages large language models' ability to diverge from prior context. By simply instructing the LLM to deviate and obfuscate previous attacks, our method dramatically outperforms existing approaches. This revelation exposes a critical flaw in current LLM safety training, suggesting that existing methods may merely mask vulnerabilities rather than eliminate them.
• Score: 16.34618038553998
• License:
• Abstract: We have uncovered a powerful jailbreak technique that leverages large language models' ability to diverge from prior context, enabling them to bypass safety constraints and generate harmful outputs. By simply instructing the LLM to deviate and obfuscate previous attacks, our method dramatically outperforms existing approaches, achieving up to a 62% higher success rate in compromising nine leading chatbots, including GPT-4, Gemini, and Llama, while using only 13% of the queries. This revelation exposes a critical flaw in current LLM safety training, suggesting that existing methods may merely mask vulnerabilities rather than eliminate them. Our findings sound an urgent alarm for the need to revolutionize testing methodologies to ensure robust and reliable LLM security.

Related papers

• Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models [59.25318174362368]
  Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text. We conduct a detailed analysis of seven different jailbreak methods and find that disagreements stem from insufficient observation samples. We propose a novel defense called textbfActivation Boundary Defense (ABD), which adaptively constrains the activations within the safety boundary.
  arXiv  Detail & Related papers  (2024-12-22T14:18:39Z)
• Multi-round jailbreak attack on large language models [2.540971544359496]
  We introduce a multi-round jailbreak approach to better understand "jailbreak" attacks. This method can rewrite the dangerous prompts, decomposing them into a series of less harmful sub-questions. Our experimental results show a 94% success rate on the llama2-7B.
  arXiv  Detail & Related papers  (2024-10-15T12:08:14Z)
• 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)
• PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach [25.31933913962953]
  Large Language Models (LLMs) have gained widespread use, raising concerns about their security. We introduce PathSeeker, a novel black-box jailbreak method, which is inspired by the game of rats escaping a maze. Our method outperforms five state-of-the-art attack techniques when tested across 13 commercial and open-source LLMs.
  arXiv  Detail & Related papers  (2024-09-21T15:36:26Z)
• Tamper-Resistant Safeguards for Open-Weight LLMs [57.90526233549399]
  We develop a method for building tamper-resistant safeguards into open-weight LLMs. We find that our method greatly improves tamper-resistance while preserving benign capabilities.
  arXiv  Detail & Related papers  (2024-08-01T17:59:12Z)
• The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models [8.423787598133972]
  This paper uncovers a critical vulnerability in the function calling process of large language models (LLMs) We introduce a novel "jailbreak function" attack method that exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters. Our findings highlight the urgent need for enhanced security measures in the function calling capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-07-25T10:09:21Z)
• LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models [21.02295266675853]
  Existing jailbreak methods suffer from two main limitations: reliance on complicated prompt engineering and iterative optimization. We propose an efficient jailbreak attack method, Analyzing-based Jailbreak (ABJ), which leverages the advanced reasoning capability of LLMs to autonomously generate harmful content.
  arXiv  Detail & Related papers  (2024-07-23T06:14:41Z)
• Jailbreaking Large Language Models Through Alignment Vulnerabilities in Out-of-Distribution Settings [57.136748215262884]
  We introduce ObscurePrompt for jailbreaking LLMs, inspired by the observed fragile alignments in Out-of-Distribution (OOD) data. We first formulate the decision boundary in the jailbreaking process and then explore how obscure text affects LLM's ethical decision boundary. Our approach substantially improves upon previous methods in terms of attack effectiveness, maintaining efficacy against two prevalent defense mechanisms.
  arXiv  Detail & Related papers  (2024-06-19T16:09:58Z)
• Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation [15.928341917085467]
  JailMine employs an automated "mining" process to elicit malicious responses from large language models. We demonstrate JailMine's effectiveness and efficiency, achieving a significant average reduction of 86% in time consumed.
  arXiv  Detail & Related papers  (2024-05-20T17:17:55Z)
• Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes [61.916827858666906]
  Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer. To reduce harm and misuse, efforts have been made to align these LLMs to human values using advanced training techniques such as Reinforcement Learning from Human Feedback. Recent studies have highlighted the vulnerability of LLMs to adversarial jailbreak attempts aiming at subverting the embedded safety guardrails. This paper proposes a method called Gradient Cuff to detect jailbreak attempts.
  arXiv  Detail & Related papers  (2024-03-01T03:29:54Z)
• ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings [58.82536530615557]
  We propose an Adversarial Suffix Embedding Translation Framework (ASETF) to transform continuous adversarial suffix embeddings into coherent and understandable text. Our method significantly reduces the computation time of adversarial suffixes and achieves a much better attack success rate to existing techniques.
  arXiv  Detail & Related papers  (2024-02-25T06:46:27Z)

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.