XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs

• URL: http://arxiv.org/abs/2504.21700v1
• Date: Wed, 30 Apr 2025 14:44:24 GMT
• Title: XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs
• Authors: Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera, Vinod P,
• Abstract summary: Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions.<n>We propose an Explainable-AI solution that comparatively analyzes the behavior of censored and uncensored models to derive unique exploitable alignment patterns.<n>Then, we propose XBreaking, a novel jailbreak attack that exploits these unique patterns to break the security constraints of LLMs by targeted noise injection.
• Score: 1.6874375111244329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adoption in critical application scenarios such as government organizations and medical institutions. For this reason, commercial LLMs typically undergo a sophisticated censoring mechanism to eliminate any harmful output they could possibly produce. In response to this, LLM Jailbreaking is a significant threat to such protections, and many previous approaches have already demonstrated its effectiveness across diverse domains. Existing jailbreak proposals mostly adopt a generate-and-test strategy to craft malicious input. To improve the comprehension of censoring mechanisms and design a targeted jailbreak attack, we propose an Explainable-AI solution that comparatively analyzes the behavior of censored and uncensored models to derive unique exploitable alignment patterns. Then, we propose XBreaking, a novel jailbreak attack that exploits these unique patterns to break the security constraints of LLMs by targeted noise injection. Our thorough experimental campaign returns important insights about the censoring mechanisms and demonstrates the effectiveness and performance of our attack.

Related papers

• Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval [25.17143802138141]
  Large Language Models (LLMs) are vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses.<n>We propose Safety Context Retrieval (SCR), a scalable and robust safeguarding paradigm for LLMs against jailbreaking.
  arXiv  Detail & Related papers  (2025-05-21T16:58:14Z)
• Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement [48.50995874445193]
  Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks.<n>We propose SAGE (Self-Aware Guard Enhancement), a training-free defense strategy designed to align LLMs' strong safety discrimination performance with their relatively weaker safety generation ability.
  arXiv  Detail & Related papers  (2025-05-17T15:54:52Z)
• Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreaking [13.939357884952154]
  We reveal a vulnerability in large language models (LLMs), which we term Defense Threshold Decay (DTD) As the model generates substantial benign content, its attention weights shift from the input to prior output, making it more susceptible to jailbreak attacks. To mitigate such attacks, we introduce a simple yet effective defense strategy, POSD, which significantly reduces jailbreak success rates.
  arXiv  Detail & Related papers  (2025-04-08T03:57:09Z)
• 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)
• 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.<n>We use an unlearning strategy to patch specific layers within large language models through self-augmented datasets.<n>Our framework reduces the harmfulness and attack success rate of jailbreak attacks.
  arXiv  Detail & Related papers  (2025-01-05T19:06:03Z)
• An Interpretable N-gram Perplexity 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.<n>Our threat model checks if a given jailbreak is likely to occur in the distribution of text.<n>We adapt popular attacks to this threat model, and, for the first time, benchmark these attacks on equal footing with it.
  arXiv  Detail & Related papers  (2024-10-21T17:27:01Z)
• LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models [21.02295266675853]
  We propose a novel black-box jailbreak attack method, Analyzing-based Jailbreak (ABJ)<n>ABJ comprises two independent attack paths, which exploit the model's multimodal reasoning capabilities to bypass safety mechanisms.<n>Our work reveals a new type of safety risk and highlights the urgent need to mitigate implicit vulnerabilities in the model's reasoning process.
  arXiv  Detail & Related papers  (2024-07-23T06:14:41Z)
• 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.<n>We first formulate the decision boundary in the jailbreaking process and then explore how obscure text affects LLM's ethical decision boundary.<n>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)
• Distract Large Language Models for Automatic Jailbreak Attack [8.364590541640482]
  We propose a novel black-box jailbreak framework for automated red teaming of Large language models. We designed malicious content concealing and memory reframing with an iterative optimization algorithm to jailbreak LLMs.
  arXiv  Detail & Related papers  (2024-03-13T11:16:43Z)
• 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)
• Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective [43.94115802328438]
  Recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) when exposed to malicious inputs.<n>We suggest that the self-safeguarding capability of LLMs is linked to specific activity patterns within their representation space.<n>Our findings demonstrate that these patterns can be detected with just a few pairs of contrastive queries.
  arXiv  Detail & Related papers  (2024-01-12T00:50:04Z)
• Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak [26.741029482196534]
  "Jailbreak Attack" is phenomenon where Large Language Models (LLMs) generate harmful responses when faced with malicious instructions. We introduce a novel automatic jailbreak method RADIAL, which bypasses the security mechanism by amplifying the potential of LLMs to generate affirmation responses. Our method achieves excellent attack performance on English malicious instructions with five open-source advanced LLMs while maintaining robust attack performance in executing cross-language attacks against Chinese malicious instructions.
  arXiv  Detail & Related papers  (2023-12-07T08:29:58Z)
• AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models [55.748851471119906]
  Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks. Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but unreadable gibberish prompts, detectable by perplexity-based filters. We introduce AutoDAN, an interpretable, gradient-based adversarial attack that merges the strengths of both attack types.
  arXiv  Detail & Related papers  (2023-10-23T17:46:07Z)
• 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)

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.