Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks

• URL: http://arxiv.org/abs/2407.00869v2
• Date: Mon, 23 Sep 2024 19:48:20 GMT
• Title: Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks
• Authors: Yue Zhou, Henry Peng Zou, Barbara Di Eugenio, Yang Zhang,
• Abstract summary: We find that language models have difficulties generating fallacious and deceptive reasoning. We propose a jailbreak attack method that elicits an aligned language model for malicious output.
• Score: 6.614364170035397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We find that language models have difficulties generating fallacious and deceptive reasoning. When asked to generate deceptive outputs, language models tend to leak honest counterparts but believe them to be false. Exploiting this deficiency, we propose a jailbreak attack method that elicits an aligned language model for malicious output. Specifically, we query the model to generate a fallacious yet deceptively real procedure for the harmful behavior. Since a fallacious procedure is generally considered fake and thus harmless by LLMs, it helps bypass the safeguard mechanism. Yet the output is factually harmful since the LLM cannot fabricate fallacious solutions but proposes truthful ones. We evaluate our approach over five safety-aligned large language models, comparing four previous jailbreak methods, and show that our approach achieves competitive performance with more harmful outputs. We believe the findings could be extended beyond model safety, such as self-verification and hallucination.

Related papers

• Making Them a Malicious Database: Exploiting Query Code to Jailbreak Aligned Large Language Models [44.27350994698781]
  We propose a novel framework to examine the generalizability of safety alignment. By treating LLMs as knowledge databases, we translate malicious queries in natural language into structured non-natural query language. We conduct extensive experiments on mainstream LLMs, and the results show that QueryAttack can achieve high attack success rates.
  arXiv  Detail & Related papers  (2025-02-13T19:13:03Z)
• Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions [51.51850981481236]
  We introduce POATE, a novel jailbreak technique that harnesses contrastive reasoning to provoke unethical responses. PoATE crafts semantically opposing intents and integrates them with adversarial templates, steering models toward harmful outputs with remarkable subtlety. To counter this, we propose Intent-Aware CoT and Reverse Thinking CoT, which decompose queries to detect malicious intent and reason in reverse to evaluate and reject harmful responses.
  arXiv  Detail & Related papers  (2025-01-03T15:40:03Z)
• SQL Injection Jailbreak: A Structural Disaster of Large Language Models [71.55108680517422]
  In this paper, we introduce a novel jailbreak method, which induces large language models (LLMs) to produce harmful content. By injecting jailbreak information into user prompts, SIJ successfully induces the model to output harmful content. We propose a simple defense method called Self-Reminder-Key to counter SIJ and demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2024-11-03T13:36:34Z)
• 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)
• Multi-round jailbreak attack on large language models [2.540971544359496]
  We introduce a multi-round jailbreak approach to better understand "jailbreak" attacks. This method can rewrite the dangerous prompts, decomposing them into a series of less harmful sub-questions. Our experimental results show a 94% success rate on the llama2-7B.
  arXiv  Detail & Related papers  (2024-10-15T12:08:14Z)
• Deciphering the Chaos: Enhancing Jailbreak Attacks via Adversarial Prompt Translation [71.92055093709924]
  We propose a novel method that "translates" garbled adversarial prompts into coherent and human-readable natural language adversarial prompts. It also offers a new approach to discovering effective designs for jailbreak prompts, advancing the understanding of jailbreak attacks. Our method achieves over 90% attack success rates against Llama-2-Chat models on AdvBench, despite their outstanding resistance to jailbreak attacks.
  arXiv  Detail & Related papers  (2024-10-15T06:31:04Z)
• Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Neural Carrier Articles [10.109063166962079]
  This paper proposes a new type of jailbreak attacks which shift the attention of the Language Model Models (LLMs) The proposed attack leverage the knowledge graph and a composer LLM to automatically generating a carrier article that is similar to the topic of a prohibited query. Our experiment results show that the proposed attacking method can successfully jailbreak all the target LLMs which high success rate, except for Claude-3.
  arXiv  Detail & Related papers  (2024-08-20T20:35:04Z)
• 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)
• Hidden You Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Logic Chain Injection [2.235763774591544]
  Existing jailbreak attacks can successfully deceive the Language Model Models (LLMs) This paper proposes a new type of jailbreak attacks which can deceive both the LLMs and human (i.e., security analyst)
  arXiv  Detail & Related papers  (2024-04-07T07:42:12Z)
• Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction [31.171418109420276]
  We pioneer a theoretical foundation in LLMs security by identifying bias vulnerabilities within the safety fine-tuning. We design a black-box jailbreak method named DRA, which conceals harmful instructions through disguise. We evaluate DRA across various open-source and closed-source models, showcasing state-of-the-art jailbreak success rates and attack efficiency.
  arXiv  Detail & Related papers  (2024-02-28T06:50:14Z)
• Coercing LLMs to do and reveal (almost) anything [80.8601180293558]
  It has been shown that adversarial attacks on large language models (LLMs) can "jailbreak" the model into making harmful statements. We argue that the spectrum of adversarial attacks on LLMs is much larger than merely jailbreaking.
  arXiv  Detail & Related papers  (2024-02-21T18:59:13Z)
• 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)
• Multilingual Jailbreak Challenges in Large Language Models [96.74878032417054]
  In this study, we reveal the presence of multilingual jailbreak challenges within large language models (LLMs) We consider two potential risky scenarios: unintentional and intentional. We propose a novel textscSelf-Defense framework that automatically generates multilingual training data for safety fine-tuning.
  arXiv  Detail & Related papers  (2023-10-10T09:44:06Z)
• Universal and Transferable Adversarial Attacks on Aligned Language Models [118.41733208825278]
  We propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Surprisingly, we find that the adversarial prompts generated by our approach are quite transferable.
  arXiv  Detail & Related papers  (2023-07-27T17:49:12Z)

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.