Jailbreaking? One Step Is Enough!

• URL: http://arxiv.org/abs/2412.12621v1
• Date: Tue, 17 Dec 2024 07:33:41 GMT
• Title: Jailbreaking? One Step Is Enough!
• Authors: Weixiong Zheng, Peijian Zeng, Yiwei Li, Hongyan Wu, Nankai Lin, Junhao Chen, Aimin Yang, Yongmei Zhou,
• Abstract summary: Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. We propose a Reverse Embedded Defense Attack (REDA) mechanism that disguises the attack intention as the "defense" intention. To enhance the model's confidence and guidance in "defensive" intentions, we adopt in-context learning (ICL) with a small number of attack examples.
• Score: 6.142918017301964
• License:
• Abstract: Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However, current jailbreak methods and the target model's defenses are engaged in an independent and adversarial process, resulting in the need for frequent attack iterations and redesigning attacks for different models. To address these gaps, we propose a Reverse Embedded Defense Attack (REDA) mechanism that disguises the attack intention as the "defense". intention against harmful content. Specifically, REDA starts from the target response, guiding the model to embed harmful content within its defensive measures, thereby relegating harmful content to a secondary role and making the model believe it is performing a defensive task. The attacking model considers that it is guiding the target model to deal with harmful content, while the target model thinks it is performing a defensive task, creating an illusion of cooperation between the two. Additionally, to enhance the model's confidence and guidance in "defensive" intentions, we adopt in-context learning (ICL) with a small number of attack examples and construct a corresponding dataset of attack examples. Extensive evaluations demonstrate that the REDA method enables cross-model attacks without the need to redesign attack strategies for different models, enables successful jailbreak in one iteration, and outperforms existing methods on both open-source and closed-source models.

Related papers

• DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing [62.43110639295449]
  Large Language Models (LLMs) are widely applied in decision making, but their deployment is threatened by jailbreak attacks. Delman is a novel approach leveraging direct model editing for precise, dynamic protection against jailbreak attacks. Delman directly updates a minimal set of relevant parameters to neutralize harmful behaviors while preserving the model's utility.
  arXiv  Detail & Related papers  (2025-02-17T10:39:21Z)
• Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense [55.77152277982117]
  We introduce Layer-AdvPatcher, a methodology designed to defend against jailbreak attacks. We use an unlearning strategy to patch specific layers within large language models through self-augmented datasets. Our framework reduces the harmfulness and attack success rate of jailbreak attacks.
  arXiv  Detail & Related papers  (2025-01-05T19:06:03Z)
• Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions [51.51850981481236]
  We introduce POATE, a novel jailbreak technique that harnesses contrastive reasoning to provoke unethical responses. PoATE crafts semantically opposing intents and integrates them with adversarial templates, steering models toward harmful outputs with remarkable subtlety. To counter this, we propose Intent-Aware CoT and Reverse Thinking CoT, which decompose queries to detect malicious intent and reason in reverse to evaluate and reject harmful responses.
  arXiv  Detail & Related papers  (2025-01-03T15:40:03Z)
• FlexLLM: Exploring LLM Customization for Moving Target Defense on Black-Box LLMs Against Jailbreak Attacks [7.31505609352525]
  Defense in large language models (LLMs) is crucial to counter the numerous attackers exploiting these systems to generate harmful content. We propose a moving target defense approach that alters decoding hyper parameters to enhance model robustness. Our results demonstrate that our defense is the most effective against jailbreak attacks in three of the models tested.
  arXiv  Detail & Related papers  (2024-12-10T17:02:28Z)
• A Realistic Threat Model for Large Language Model Jailbreaks [87.64278063236847]
  In this work, we propose a unified threat model for the principled comparison of jailbreak attacks. Our threat model combines constraints in perplexity, measuring how far a jailbreak deviates from natural text. We adapt popular attacks to this new, realistic threat model, with which we, for the first time, benchmark these attacks on equal footing.
  arXiv  Detail & Related papers  (2024-10-21T17:27:01Z)
• You Know What I'm Saying: Jailbreak Attack via Implicit Reference [22.520950422702757]
  This study identifies a previously overlooked vulnerability, which we term Attack via Implicit Reference (AIR) AIR decomposes a malicious objective into permissible objectives and links them through implicit references within the context. Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding 90% on most models.
  arXiv  Detail & Related papers  (2024-10-04T18:42:57Z)
• Prefix Guidance: A Steering Wheel for Large Language Models to Defend Against Jailbreak Attacks [27.11523234556414]
  We propose a plug-and-play and easy-to-deploy jailbreak defense framework, namely Prefix Guidance (PG) PG guides the model to identify harmful prompts by directly setting the first few tokens of the model's output. We demonstrate the effectiveness of PG across three models and five attack methods.
  arXiv  Detail & Related papers  (2024-08-15T14:51:32Z)
• Jailbreak Attacks and Defenses Against Large Language Models: A Survey [22.392989536664288]
  Large Language Models (LLMs) have performed exceptionally in various text-generative tasks. "jailbreaking" induces the model to generate malicious responses against the usage policy and society. We propose a comprehensive and detailed taxonomy of jailbreak attack and defense methods.
  arXiv  Detail & Related papers  (2024-07-05T06:57:30Z)
• Weak-to-Strong Jailbreaking on Large Language Models [96.50953637783581]
  Large language models (LLMs) are vulnerable to jailbreak attacks. Existing jailbreaking methods are computationally costly. We propose the weak-to-strong jailbreaking attack.
  arXiv  Detail & Related papers  (2024-01-30T18:48:37Z)
• Jailbroken: How Does LLM Safety Training Fail? [92.8748773632051]
  "jailbreak" attacks on early releases of ChatGPT elicit undesired behavior. We investigate why such attacks succeed and how they can be created. New attacks utilizing our failure modes succeed on every prompt in a collection of unsafe requests.
  arXiv  Detail & Related papers  (2023-07-05T17:58:10Z)

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.