Jailbreak Attacks and Defenses Against Large Language Models: A Survey

• URL: http://arxiv.org/abs/2407.04295v2
• Date: Fri, 30 Aug 2024 11:57:47 GMT
• Title: Jailbreak Attacks and Defenses Against Large Language Models: A Survey
• Authors: Sibo Yi, Yule Liu, Zhen Sun, Tianshuo Cong, Xinlei He, Jiaxing Song, Ke Xu, Qi Li,
• Abstract summary: 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.
• Score: 22.392989536664288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have performed exceptionally in various text-generative tasks, including question answering, translation, code completion, etc. However, the over-assistance of LLMs has raised the challenge of "jailbreaking", which induces the model to generate malicious responses against the usage policy and society by designing adversarial prompts. With the emergence of jailbreak attack methods exploiting different vulnerabilities in LLMs, the corresponding safety alignment measures are also evolving. In this paper, we propose a comprehensive and detailed taxonomy of jailbreak attack and defense methods. For instance, the attack methods are divided into black-box and white-box attacks based on the transparency of the target model. Meanwhile, we classify defense methods into prompt-level and model-level defenses. Additionally, we further subdivide these attack and defense methods into distinct sub-classes and present a coherent diagram illustrating their relationships. We also conduct an investigation into the current evaluation methods and compare them from different perspectives. Our findings aim to inspire future research and practical implementations in safeguarding LLMs against adversarial attacks. Above all, although jailbreak remains a significant concern within the community, we believe that our work enhances the understanding of this domain and provides a foundation for developing more secure LLMs.

Related papers

• The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense [56.32083100401117]
  We investigate why Vision Large Language Models (VLLMs) are prone to jailbreak attacks. We then make a key observation: existing defense mechanisms suffer from an textbfover-prudence problem. We find that the two representative evaluation methods for jailbreak often exhibit chance agreement.
  arXiv  Detail & Related papers  (2024-11-13T07:57:19Z)
• Transferable Ensemble Black-box Jailbreak Attacks on Large Language Models [0.0]
  We propose a novel black-box jailbreak attacking framework that incorporates various LLM-as-Attacker methods. Our method is designed based on three key observations from existing jailbreaking studies and practices.
  arXiv  Detail & Related papers  (2024-10-31T01:55:33Z)
• 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)
• 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)
• Purple-teaming LLMs with Adversarial Defender Training [57.535241000787416]
  We present Purple-teaming LLMs with Adversarial Defender training (PAD) PAD is a pipeline designed to safeguard LLMs by novelly incorporating the red-teaming (attack) and blue-teaming (safety training) techniques. PAD significantly outperforms existing baselines in both finding effective attacks and establishing a robust safe guardrail.
  arXiv  Detail & Related papers  (2024-07-01T23:25:30Z)
• 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)
• 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)
• Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks [55.603893267803265]
  Large Language Models (LLMs) are susceptible to Jailbreaking attacks. Jailbreaking attacks aim to extract harmful information by subtly modifying the attack query. We focus on a new attack form, called Contextual Interaction Attack.
  arXiv  Detail & Related papers  (2024-02-14T13:45:19Z)

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.