Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment

• URL: http://arxiv.org/abs/2411.02785v1
• Date: Tue, 05 Nov 2024 03:51:13 GMT
• Title: Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment
• Authors: Jason Vega, Junsheng Huang, Gaokai Zhang, Hangoo Kang, Minjia Zhang, Gagandeep Singh,
• Abstract summary: In this paper, we study how simple random augmentations to the input prompt affect safety alignment effectiveness in state-of-the-art LLMs. We show that low-resource and unsophisticated attackers can significantly improve their chances of bypassing alignment with just 25 random augmentations per prompt.
• Score: 16.5939079098358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety alignment of Large Language Models (LLMs) has recently become a critical objective of model developers. In response, a growing body of work has been investigating how safety alignment can be bypassed through various jailbreaking methods, such as adversarial attacks. However, these jailbreak methods can be rather costly or involve a non-trivial amount of creativity and effort, introducing the assumption that malicious users are high-resource or sophisticated. In this paper, we study how simple random augmentations to the input prompt affect safety alignment effectiveness in state-of-the-art LLMs, such as Llama 3 and Qwen 2. We perform an in-depth evaluation of 17 different models and investigate the intersection of safety under random augmentations with multiple dimensions: augmentation type, model size, quantization, fine-tuning-based defenses, and decoding strategies (e.g., sampling temperature). We show that low-resource and unsophisticated attackers, i.e. $\textit{stochastic monkeys}$, can significantly improve their chances of bypassing alignment with just 25 random augmentations per prompt.

Related papers

• Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense [55.77152277982117]
  We introduce Layer-AdvPatcher, a methodology designed to defend against jailbreak attacks. We use an unlearning strategy to patch specific layers within large language models through self-augmented datasets. Our framework reduces the harmfulness and attack success rate of jailbreak attacks.
  arXiv  Detail & Related papers  (2025-01-05T19:06:03Z)
• Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models [59.25318174362368]
  Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text. We conduct a detailed analysis of seven different jailbreak methods and find that disagreements stem from insufficient observation samples. We propose a novel defense called textbfActivation Boundary Defense (ABD), which adaptively constrains the activations within the safety boundary.
  arXiv  Detail & Related papers  (2024-12-22T14:18:39Z)
• A Realistic Threat Model for Large Language Model Jailbreaks [87.64278063236847]
  In this work, we propose a unified threat model for the principled comparison of jailbreak attacks. Our threat model combines constraints in perplexity, measuring how far a jailbreak deviates from natural text. We adapt popular attacks to this new, realistic threat model, with which we, for the first time, benchmark these attacks on equal footing.
  arXiv  Detail & Related papers  (2024-10-21T17:27:01Z)
• Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models [8.024771725860127]
  Large Language Models (LLMs) remain vulnerable to jailbreak attacks that bypass their safety mechanisms. We introduce a novel scalable jailbreak attack that preempts the activation of an LLM's safety policies by occupying its computational resources.
  arXiv  Detail & Related papers  (2024-10-05T15:10:01Z)
• AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting LLMs [34.221522224051846]
  We propose an adaptive position pre-fill jailbreak attack approach for executing jailbreak attacks on Large Language Models (LLMs) Our method leverages the model's instruction-following capabilities to first output safe content, then exploits its narrative-shifting abilities to generate harmful content. Our method can improve the attack success rate by 47% on the widely recognized secure model (Llama2) compared to existing approaches.
  arXiv  Detail & Related papers  (2024-09-11T00:00:58Z)
• SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance [48.80398992974831]
  SafeAligner is a methodology implemented at the decoding stage to fortify defenses against jailbreak attacks. We develop two specialized models: the Sentinel Model, which is trained to foster safety, and the Intruder Model, designed to generate riskier responses. We show that SafeAligner can increase the likelihood of beneficial tokens, while reducing the occurrence of harmful ones.
  arXiv  Detail & Related papers  (2024-06-26T07:15:44Z)
• RL-JACK: Reinforcement Learning-powered Black-box Jailbreaking Attack against LLMs [14.1985036536366]
  We propose RL-JACK, a novel black-box jailbreaking attack powered by deep reinforcement learning (DRL) Our method includes a series of customized designs to enhance the RL agent's learning efficiency in the jailbreaking context. We demonstrate that RL-JACK is overall much more effective than existing jailbreaking attacks against six SOTA LLMs.
  arXiv  Detail & Related papers  (2024-06-13T01:05:22Z)
• 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)
• Weak-to-Strong Jailbreaking on Large Language Models [96.50953637783581]
  Large language models (LLMs) are vulnerable to jailbreak attacks. Existing jailbreaking methods are computationally costly. We propose the weak-to-strong jailbreaking attack.
  arXiv  Detail & Related papers  (2024-01-30T18:48:37Z)
• Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation [39.829517061574364]
  Even carefully aligned models can be manipulated maliciously, leading to unintended behaviors, known as "jailbreaks" We propose the generation exploitation attack, which disrupts model alignment by only manipulating variations of decoding methods. Our study underscores a major failure in current safety evaluation and alignment procedures for open-source LLMs.
  arXiv  Detail & Related papers  (2023-10-10T20:15:54Z)
• Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models [102.63973600144308]
  Open-source large language models can be easily subverted to generate harmful content. Experiments across 8 models released by 5 different organizations demonstrate the effectiveness of shadow alignment attack. This study serves as a clarion call for a collective effort to overhaul and fortify the safety of open-source LLMs against malicious attackers.
  arXiv  Detail & Related papers  (2023-10-04T16:39:31Z)
• AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models [54.95912006700379]
  We introduce AutoDAN, a novel jailbreak attack against aligned Large Language Models. AutoDAN can automatically generate stealthy jailbreak prompts by the carefully designed hierarchical genetic algorithm.
  arXiv  Detail & Related papers  (2023-10-03T19:44:37Z)
• One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training [54.622474306336635]
  A new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques. We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
  arXiv  Detail & Related papers  (2023-08-12T09:34:43Z)

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.