GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs

• URL: http://arxiv.org/abs/2411.14133v1
• Date: Thu, 21 Nov 2024 14:00:01 GMT
• Title: GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
• Authors: Advik Raj Basani, Xiao Zhang,
• Abstract summary: We introduce Generative Adversarial Suffix Prompter (GASP) to improve adversarial suffix creation in a fully black-box setting. Our experiments show that GASP can generate natural jailbreak prompts, significantly improving attack success rates, reducing training times, and accelerating inference speed.
• Score: 3.096869664709865
• License:
• Abstract: Large Language Models (LLMs) have shown impressive proficiency across a range of natural language processing tasks yet remain vulnerable to adversarial prompts, known as jailbreak attacks, carefully designed to elicit harmful responses from LLMs. Traditional methods rely on manual heuristics, which suffer from limited generalizability. While being automatic, optimization-based attacks often produce unnatural jailbreak prompts that are easy to detect by safety filters or require high computational overhead due to discrete token optimization. Witnessing the limitations of existing jailbreak methods, we introduce Generative Adversarial Suffix Prompter (GASP), a novel framework that combines human-readable prompt generation with Latent Bayesian Optimization (LBO) to improve adversarial suffix creation in a fully black-box setting. GASP leverages LBO to craft adversarial suffixes by efficiently exploring continuous embedding spaces, gradually optimizing the model to improve attack efficacy while balancing prompt coherence through a targeted iterative refinement procedure. Our experiments show that GASP can generate natural jailbreak prompts, significantly improving attack success rates, reducing training times, and accelerating inference speed, thus making it an efficient and scalable solution for red-teaming LLMs.

Related papers

• xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking [32.89084809038529]
  Black-box jailbreak is an attack where crafted prompts bypass safety mechanisms in large language models. We propose a novel black-box jailbreak method leveraging reinforcement learning (RL) We introduce a comprehensive jailbreak evaluation framework incorporating keywords, intent matching, and answer validation to provide a more rigorous and holistic assessment of jailbreak success.
  arXiv  Detail & Related papers  (2025-01-28T06:07:58Z)
• LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models [59.29840790102413]
  Existing jailbreak attacks are primarily based on opaque optimization techniques and gradient search methods. We propose LLM-Virus, a jailbreak attack method based on evolutionary algorithm, termed evolutionary jailbreak. Our results show that LLM-Virus achieves competitive or even superior performance compared to existing attack methods.
  arXiv  Detail & Related papers  (2024-12-28T07:48:57Z)
• JailPO: A Novel Black-box Jailbreak Framework via Preference Optimization against Aligned LLMs [11.924542310342282]
  We present JailPO, a novel black-box jailbreak framework to examine Large Language Models (LLMs) alignment. For scalability and universality, JailPO meticulously trains attack models to automatically generate covert jailbreak prompts. We also introduce a preference optimization-based attack method to enhance the jailbreak effectiveness.
  arXiv  Detail & Related papers  (2024-12-20T07:29:10Z)
• LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds [98.20826635707341]
  LIAR (Leveraging Inference time Alignment to jailbReak) is a fast and efficient best-of-N approach tailored for jailbreak attacks. Our results demonstrate that a best-of-N approach is a simple yet highly effective strategy for evaluating the robustness of aligned LLMs.
  arXiv  Detail & Related papers  (2024-12-06T18:02:59Z)
• Unlocking Adversarial Suffix Optimization Without Affirmative Phrases: Efficient Black-box Jailbreaking via LLM as Optimizer [33.67942887761857]
  We present ECLIPSE, a novel and efficient black-box jailbreaking method utilizing optimizable suffixes. We employ task prompts to translate jailbreaking goals into natural language instructions, which guides the LLM to generate adversarial suffixes for malicious queries. ECLIPSE achieves an average attack success rate (ASR) of 0.92 across three open-source LLMs and GPT-3.5-Turbo, significantly surpassing GCG in 2.4 times.
  arXiv  Detail & Related papers  (2024-08-21T03:35:24Z)
• 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)
• 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)
• 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)
• 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)
• AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models [55.748851471119906]
  Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks. Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but unreadable gibberish prompts, detectable by perplexity-based filters. We introduce AutoDAN, an interpretable, gradient-based adversarial attack that merges the strengths of both attack types.
  arXiv  Detail & Related papers  (2023-10-23T17:46:07Z)

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.