SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner

• URL: http://arxiv.org/abs/2406.05498v2
• Date: Thu, 5 Sep 2024 17:33:33 GMT
• Title: SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner
• Authors: Xunguang Wang, Daoyuan Wu, Zhenlan Ji, Zongjie Li, Pingchuan Ma, Shuai Wang, Yingjiu Li, Yang Liu, Ning Liu, Juergen Rahmel,
• Abstract summary: This paper introduces a generic LLM jailbreak defense framework called SelfDefend. We empirically validate using the commonly used GPT-3.5/4 models across all major jailbreak attacks. These models outperform six state-of-the-art defenses and match the performance of GPT-4-based SelfDefend.
• Score: 21.414701448926614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs) and has evolved into multiple categories: human-based, optimization-based, generation-based, and the recent indirect and multilingual jailbreaks. However, delivering a practical jailbreak defense is challenging because it needs to not only handle all the above jailbreak attacks but also incur negligible delays to user prompts, as well as be compatible with both open-source and closed-source LLMs. Inspired by how the traditional security concept of shadow stacks defends against memory overflow attacks, this paper introduces a generic LLM jailbreak defense framework called SelfDefend, which establishes a shadow LLM as a defense instance to concurrently protect the target LLM instance in the normal stack and collaborate with it for checkpoint-based access control. The effectiveness of SelfDefend builds upon our observation that existing LLMs (both target and defense LLMs) have the capability to identify harmful prompts or intentions in user queries, which we empirically validate using the commonly used GPT-3.5/4 models across all major jailbreak attacks. To further improve the defense's robustness and minimize costs, we employ a data distillation approach to tune dedicated open-source defense models. These models outperform six state-of-the-art defenses and match the performance of GPT-4-based SelfDefend, with significantly lower extra delays. We also empirically show that the tuned models are robust to adaptive jailbreaks and prompt injections.

Related papers

• HSF: Defending against Jailbreak Attacks with Hidden State Filtering [14.031010511732008]
  We propose a jailbreak attack defense strategy based on a Hidden State Filter (HSF) HSF enables the model to preemptively identify and reject adversarial inputs before the inference process begins. It significantly reduces the success rate of jailbreak attacks while minimally impacting responses to benign user queries.
  arXiv  Detail & Related papers  (2024-08-31T06:50:07Z)
• 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)
• AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens [83.08119913279488]
  We present a systematic analysis of the dependency relationships in jailbreak attack and defense techniques. We propose three comprehensive, automated, and logical frameworks. We show that the proposed ensemble jailbreak attack and defense framework significantly outperforms existing research.
  arXiv  Detail & Related papers  (2024-06-06T07:24:41Z)
• 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)
• 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)
• Distract Large Language Models for Automatic Jailbreak Attack [8.364590541640482]
  We propose a novel black-box jailbreak framework for automated red teaming of Large language models. We designed malicious content concealing and memory reframing with an iterative optimization algorithm to jailbreak LLMs.
  arXiv  Detail & Related papers  (2024-03-13T11:16:43Z)
• AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks [20.5016054418053]
  AutoDefense is a multi-agent defense framework that filters harmful responses from large language models. Our framework is robust against different jailbreak attack prompts, and can be used to defend different victim models.
  arXiv  Detail & Related papers  (2024-03-02T16:52:22Z)
• 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)
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks [99.23352758320945]
  We propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks on large language models (LLMs) Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense first randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs.
  arXiv  Detail & Related papers  (2023-10-05T17:01:53Z)

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.