You Know What I'm Saying: Jailbreak Attack via Implicit Reference

• URL: http://arxiv.org/abs/2410.03857v2
• Date: Tue, 8 Oct 2024 08:34:38 GMT
• Title: You Know What I'm Saying: Jailbreak Attack via Implicit Reference
• Authors: Tianyu Wu, Lingrui Mei, Ruibin Yuan, Lujun Li, Wei Xue, Yike Guo,
• Abstract summary: This study identifies a previously overlooked vulnerability, which we term Attack via Implicit Reference (AIR) AIR decomposes a malicious objective into permissible objectives and links them through implicit references within the context. Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding 90% on most models.
• Score: 22.520950422702757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent advancements in large language model (LLM) alignment have enabled the effective identification of malicious objectives involving scene nesting and keyword rewriting, our study reveals that these methods remain inadequate at detecting malicious objectives expressed through context within nested harmless objectives. This study identifies a previously overlooked vulnerability, which we term Attack via Implicit Reference (AIR). AIR decomposes a malicious objective into permissible objectives and links them through implicit references within the context. This method employs multiple related harmless objectives to generate malicious content without triggering refusal responses, thereby effectively bypassing existing detection techniques.Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding 90% on most models, including GPT-4o, Claude-3.5-Sonnet, and Qwen-2-72B. Notably, we observe an inverse scaling phenomenon, where larger models are more vulnerable to this attack method. These findings underscore the urgent need for defense mechanisms capable of understanding and preventing contextual attacks. Furthermore, we introduce a cross-model attack strategy that leverages less secure models to generate malicious contexts, thereby further increasing the ASR when targeting other models.Our code and jailbreak artifacts can be found at https://github.com/Lucas-TY/llm_Implicit_reference.

Related papers

• Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models [53.580928907886324]
  Reasoning-Augmented Conversation is a novel multi-turn jailbreak framework. It reformulates harmful queries into benign reasoning tasks. We show that RACE achieves state-of-the-art attack effectiveness in complex conversational scenarios.
  arXiv  Detail & Related papers  (2025-02-16T09:27:44Z)
• 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)
• Jailbreaking? One Step Is Enough! [6.142918017301964]
  Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. We propose a Reverse Embedded Defense Attack (REDA) mechanism that disguises the attack intention as the "defense" intention. To enhance the model's confidence and guidance in "defensive" intentions, we adopt in-context learning (ICL) with a small number of attack examples.
  arXiv  Detail & Related papers  (2024-12-17T07:33:41Z)
• The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense [56.32083100401117]
  We investigate why Vision Large Language Models (VLLMs) are prone to jailbreak attacks. We then make a key observation: existing defense mechanisms suffer from an textbfover-prudence problem. We find that the two representative evaluation methods for jailbreak often exhibit chance agreement.
  arXiv  Detail & Related papers  (2024-11-13T07:57:19Z)
• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees [16.16333915007336]
  Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking. backdoor attacks involve manipulating a small percentage of training data labels, such as Label Flipping (LF), which changes benign labels to malicious ones and vice versa. We propose an innovative alarm system that detects the presence of poisoned labels and a defense mechanism designed to uncover the original class labels.
  arXiv  Detail & Related papers  (2024-03-05T14:21:57Z)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• Hijacking Large Language Models via Adversarial In-Context Learning [8.15194326639149]
  In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific downstream tasks. Existing attacks are either easy to detect, rely on external models, or lack specificity towards ICL. This work introduces a novel transferable attack against ICL to address these issues.
  arXiv  Detail & Related papers  (2023-11-16T15:01:48Z)
• Object-fabrication Targeted Attack for Object Detection [54.10697546734503]
  adversarial attack for object detection contains targeted attack and untargeted attack. New object-fabrication targeted attack mode can mislead detectors tofabricate extra false objects with specific target labels.
  arXiv  Detail & Related papers  (2022-12-13T08:42:39Z)

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.