How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

• URL: http://arxiv.org/abs/2401.06373v2
• Date: Tue, 23 Jan 2024 22:46:12 GMT
• Title: How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
• Authors: Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, Weiyan Shi
• Abstract summary: This paper introduces a new perspective to jailbreak large language models (LLMs) as human-like communicators. We apply a persuasion taxonomy derived from decades of social science research to generate persuasive adversarial prompts (PAP) to jailbreak LLMs. PAP consistently achieves an attack success rate of over $92%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses.
• Score: 66.05593434288625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak performance across all risk categories: PAP consistently achieves an attack success rate of over $92\%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses, and advocate for more fundamental mitigation for highly interactive LLMs

Related papers

• MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue [36.44365630876591]
  Large Language Models (LLMs) demonstrate outstanding performance in their reservoir of knowledge and understanding capabilities. LLMs have been shown to be prone to illegal or unethical reactions when subjected to jailbreak attacks. We propose a novel multi-round dialogue jailbreaking agent, emphasizing the importance of stealthiness in identifying and mitigating potential threats to human values.
  arXiv  Detail & Related papers  (2024-11-06T10:32:09Z)
• 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)
• Figure it Out: Analyzing-based Jailbreak Attack on Large Language Models [21.252514293436437]
  We propose Analyzing-based Jailbreak (ABJ) to combat jailbreak attacks on Large Language Models (LLMs) ABJ achieves 94.8% attack success rate (ASR) and 1.06 attack efficiency (AE) on GPT-4-turbo-0409, demonstrating state-of-the-art attack effectiveness and efficiency.
  arXiv  Detail & Related papers  (2024-07-23T06:14:41Z)
• Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens [22.24239212756129]
  Existing jailbreaking attacks require either human experts or leveraging complicated algorithms to craft prompts. We introduce BOOST, a simple attack that leverages only the eos tokens. Our findings uncover how fragile an LLM is against jailbreak attacks, motivating the development of strong safety alignment approaches.
  arXiv  Detail & Related papers  (2024-05-31T07:41:03Z)
• Comprehensive Assessment of Jailbreak Attacks Against LLMs [28.58973312098698]
  We study 13 cutting-edge jailbreak methods from four categories, 160 questions from 16 violation categories, and six popular LLMs. Our experimental results demonstrate that the optimized jailbreak prompts consistently achieve the highest attack success rates. We discuss the trade-off between the attack performance and efficiency, as well as show that the transferability of the jailbreak prompts is still viable.
  arXiv  Detail & Related papers  (2024-02-08T13:42:50Z)
• Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking [60.78524314357671]
  We investigate a novel category of jailbreak attacks specifically designed to target the cognitive structure and processes of large language models (LLMs) Our proposed cognitive overload is a black-box attack with no need for knowledge of model architecture or access to model weights. Experiments conducted on AdvBench and MasterKey reveal that various LLMs, including both popular open-source model Llama 2 and the proprietary model ChatGPT, can be compromised through cognitive overload.
  arXiv  Detail & Related papers  (2023-11-16T11:52:22Z)
• 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.