Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities

• URL: http://arxiv.org/abs/2410.18469v2
• Date: Fri, 25 Oct 2024 23:05:59 GMT
• Title: Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities
• Authors: Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng, Hao Cheng, Aidan San, Michel Galley, Jianfeng Gao,
• Abstract summary: We introduce ADV-LLM, an iterative self-tuning process that crafts adversarial LLMs with enhanced jailbreak ability. Our framework significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. It exhibits strong attack transferability to closed-source models, achieving 99% ASR on GPT-3.5 and 49% ASR on GPT-4, despite being optimized solely on Llama3.
• Score: 63.603861880022954
• License:
• Abstract: Recent research has shown that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks, where adversarial suffixes crafted by algorithms appended to harmful queries bypass safety alignment and trigger unintended responses. Current methods for generating these suffixes are computationally expensive and have low Attack Success Rates (ASR), especially against well-aligned models like Llama2 and Llama3. To overcome these limitations, we introduce ADV-LLM, an iterative self-tuning process that crafts adversarial LLMs with enhanced jailbreak ability. Our framework significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100\% ASR on various open-source LLMs. Moreover, it exhibits strong attack transferability to closed-source models, achieving 99% ASR on GPT-3.5 and 49% ASR on GPT-4, despite being optimized solely on Llama3. Beyond improving jailbreak ability, ADV-LLM provides valuable insights for future safety alignment research through its ability to generate large datasets for studying LLM safety.

Related papers

• LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs [13.36946005380889]
  We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Our method significantly outperforms existing red-teaming approaches, achieving a +57.2% improvement in Attack Success Rate (ASR) on LLaMA2-7B-chat and a +50.3% increase on Claude 2.
  arXiv  Detail & Related papers  (2024-11-13T18:44:30Z)
• Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models [50.89022445197919]
  Large language models (LLMs) have exhibited outstanding performance in engaging with humans. LLMs are vulnerable to jailbreak attacks, leading to the generation of harmful responses. We propose Jigsaw Puzzles (JSP), a straightforward yet effective multi-turn jailbreak strategy against the advanced LLMs.
  arXiv  Detail & Related papers  (2024-10-15T10:07:15Z)
• 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)
• 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)
• h4rm3l: A Dynamic Benchmark of Composable Jailbreak Attacks for LLM Safety Assessment [48.5611060845958]
  We propose a novel benchmark of composable jailbreak attacks to move beyond static datasets and of attacks and harms. We use h4rm3l to generate a dataset of 2656 successful novel jailbreak attacks targeting 6 state-of-the-art (SOTA) open-source and proprietary LLMs. Several of our synthesized attacks are more effective than previously reported ones, with Attack Success rates exceeding 90% on SOTA closed language models.
  arXiv  Detail & Related papers  (2024-08-09T01:45:39Z)
• Automated Progressive Red Teaming [38.723546092060666]
  Manual red teaming is time-consuming, costly and lacks scalability. We propose Automated Progressive Red Teaming (APRT) as an effectively learnable framework. APRT leverages three core modules: an Intention Expanding LLM that generates diverse initial attack samples, an Intention Hiding LLM that crafts adversarial prompts, and an Evil Maker to manage prompt diversity and filter ineffective samples.
  arXiv  Detail & Related papers  (2024-07-04T12:14:27Z)
• 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)
• 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)
• Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! [88.90694413503614]
  We find that the safety alignment of LLMs can be compromised by fine-tuning. We jailbreak GPT-3.5 Turbo's safety guardrails by fine-tuning it on only 10 such examples. We advocate for further research efforts toward reinforcing safety protocols for the custom fine-tuning of aligned LLMs.
  arXiv  Detail & Related papers  (2023-10-05T17:12:17Z)

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.