RePD: Defending Jailbreak Attack through a Retrieval-based Prompt Decomposition Process

• URL: http://arxiv.org/abs/2410.08660v2
• Date: Tue, 22 Oct 2024 04:22:45 GMT
• Title: RePD: Defending Jailbreak Attack through a Retrieval-based Prompt Decomposition Process
• Authors: Peiran Wang, Xiaogeng Liu, Chaowei Xiao,
• Abstract summary: We introduce RePD, an innovative attack framework designed to mitigate the risk of jailbreak attacks on large language models (LLMs) RePD operates on a one-shot learning model, wherein it accesses a database of jailbreak prompt templates to identify and decompose harmful inquiries embedded within user prompts. We have demonstrated the efficacy of our proposed RePD in enhancing the resilience of LLMs against jailbreak attacks, without compromising their performance in responding to typical user requests.
• Score: 23.66988994636578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we introduce RePD, an innovative attack Retrieval-based Prompt Decomposition framework designed to mitigate the risk of jailbreak attacks on large language models (LLMs). Despite rigorous pretraining and finetuning focused on ethical alignment, LLMs are still susceptible to jailbreak exploits. RePD operates on a one-shot learning model, wherein it accesses a database of pre-collected jailbreak prompt templates to identify and decompose harmful inquiries embedded within user prompts. This process involves integrating the decomposition of the jailbreak prompt into the user's original query into a one-shot learning example to effectively teach the LLM to discern and separate malicious components. Consequently, the LLM is equipped to first neutralize any potentially harmful elements before addressing the user's prompt in a manner that aligns with its ethical guidelines. RePD is versatile and compatible with a variety of open-source LLMs acting as agents. Through comprehensive experimentation with both harmful and benign prompts, we have demonstrated the efficacy of our proposed RePD in enhancing the resilience of LLMs against jailbreak attacks, without compromising their performance in responding to typical user requests.

Related papers

• DROJ: A Prompt-Driven Attack against Large Language Models [0.0]
  Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Despite massive alignment efforts, LLMs remain susceptible to adversarial jailbreak attacks. We introduce a novel approach, Directed Rrepresentation Optimization Jailbreak (DROJ)
  arXiv  Detail & Related papers  (2024-11-14T01:48:08Z)
• Buckle Up: Robustifying LLMs at Every Customization Stage via Data Curation [20.176424063726277]
  Large language models (LLMs) are extensively adapted for downstream applications through a process known as "customization" Recent studies have revealed a vulnerability that tuning LLMs with malicious samples can compromise their robustness and amplify harmful content, an attack known as "jailbreaking"
  arXiv  Detail & Related papers  (2024-10-03T05:24:38Z)
• 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)
• Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing [14.094372002702476]
  Large language models (LLMs) are increasingly being adopted in a wide range of real-world applications. Recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts. We propose a novel defense method termed textbfLayer-specific textbfEditing (LED) to enhance the resilience of LLMs against jailbreak attacks.
  arXiv  Detail & Related papers  (2024-05-28T13:26:12Z)
• AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting [54.931241667414184]
  We propose textbfAdaptive textbfShield Prompting, which prepends inputs with defense prompts to defend MLLMs against structure-based jailbreak attacks. Our methods can consistently improve MLLMs' robustness against structure-based jailbreak attacks.
  arXiv  Detail & Related papers  (2024-03-14T15:57:13Z)
• 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)
• Defending LLMs against Jailbreaking Attacks via Backtranslation [61.878363293735624]
  We propose a new method for defending LLMs against jailbreaking attacks by backtranslation'' The inferred prompt is called the backtranslated prompt which tends to reveal the actual intent of the original prompt. We empirically demonstrate that our defense significantly outperforms the baselines.
  arXiv  Detail & Related papers  (2024-02-26T10:03:33Z)
• DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers [74.7446827091938]
  We introduce an automatic prompt textbfDecomposition and textbfReconstruction framework for jailbreak textbfAttack (DrAttack) DrAttack includes three key components: (a) Decomposition' of the original prompt into sub-prompts, (b) Reconstruction' of these sub-prompts implicitly by in-context learning with semantically similar but harmless reassembling demo, and (c) a Synonym Search' of sub-prompts, aiming to find sub-prompts' synonyms that maintain the original intent while
  arXiv  Detail & Related papers  (2024-02-25T17:43:29Z)
• Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak [26.741029482196534]
  "Jailbreak Attack" is phenomenon where Large Language Models (LLMs) generate harmful responses when faced with malicious instructions. We introduce a novel automatic jailbreak method RADIAL, which bypasses the security mechanism by amplifying the potential of LLMs to generate affirmation responses. Our method achieves excellent attack performance on English malicious instructions with five open-source advanced LLMs while maintaining robust attack performance in executing cross-language attacks against Chinese malicious instructions.
  arXiv  Detail & Related papers  (2023-12-07T08:29:58Z)
• 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)

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.