Jailbreak Attack Initializations as Extractors of Compliance Directions
- URL: http://arxiv.org/abs/2502.09755v3
- Date: Wed, 08 Oct 2025 07:28:04 GMT
- Title: Jailbreak Attack Initializations as Extractors of Compliance Directions
- Authors: Amit Levi, Rom Himelstein, Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin,
- Abstract summary: Safety-aligned LLMs respond to prompts with either compliance or refusal.<n>Recent works show that initializing attacks via self-transfer from other prompts significantly enhances their performance.<n>We propose CRI, an framework that aims to project unseen prompts further along compliance directions.
- Score: 5.910850302054065
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model's activation space. Recent works show that initializing attacks via self-transfer from other prompts significantly enhances their performance. However, the underlying mechanisms of these initializations remain unclear, and attacks utilize arbitrary or hand-picked initializations. This work presents that each gradient-based jailbreak attack and subsequent initialization gradually converge to a single compliance direction that suppresses refusal, thereby enabling an efficient transition from refusal to compliance. Based on this insight, we propose CRI, an initialization framework that aims to project unseen prompts further along compliance directions. We demonstrate our approach on multiple attacks, models, and datasets, achieving an increased attack success rate (ASR) and reduced computational overhead, highlighting the fragility of safety-aligned LLMs. A reference implementation is available at: https://amit1221levi.github.io/CRI-Jailbreak-Init-LLMs-evaluation.
Related papers
- Jailbreaking LLMs Without Gradients or Priors: Effective and Transferable Attacks [22.52730333160258]
We introduce RAILS, a framework that operates solely on model logits.<n>By eliminating gradient dependency, RAILS enables cross-tokenizer ensemble attacks.<n> Empirically, RAILS achieves near 100% success rates on multiple open-source models and high black-box attack transferability to closed-source systems like GPT and Gemini.
arXiv Detail & Related papers (2026-01-06T21:14:13Z) - Friend or Foe: How LLMs' Safety Mind Gets Fooled by Intent Shift Attack [53.34204977366491]
Large language models (LLMs) remain vulnerable to jailbreaking attacks despite their impressive capabilities.<n>In this paper, we introduce ISA (Intent Shift Attack), which obfuscates LLMs about the intent of the attacks.<n>Our approach only needs minimal edits to the original request, and yields natural, human-readable, and seemingly harmless prompts.
arXiv Detail & Related papers (2025-11-01T13:44:42Z) - Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks [29.465042445657947]
New attacks expose large language models' inability to recognize unseen malicious instructions.<n>We propose IMAGINE, a synthesis framework that leverages embedding space distribution analysis to generate jailbreak-like instructions.<n>We show significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llama3.2 without compromising their utility.
arXiv Detail & Related papers (2025-08-27T16:44:03Z) - Mitigating Jailbreaks with Intent-Aware LLMs [42.48292327349576]
Large language models (LLMs) remain vulnerable to jailbreak attacks via adversarially crafted instructions.<n>We propose Intent-FT, a simple and lightweight fine-tuning approach that explicitly trains LLMs to infer the underlying intent of an instruction before responding.<n> Empirically, Intent-FT consistently mitigates all evaluated attack categories, with no single attack exceeding a 50% success rate.
arXiv Detail & Related papers (2025-08-16T15:03:33Z) - The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems [101.68501850486179]
We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities.<n>This task aims to find imperceptible perturbations that retrieve a target document, originally excluded from the initial top-$k$ candidate set.<n>We propose ReGENT, a reinforcement learning-based framework that tracks interactions between the attacker and the target RAG.
arXiv Detail & Related papers (2025-05-24T08:19:25Z) - Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs [54.90315421117162]
We propose a novel poisoning method via completely harmless data.<n>Inspired by the causal reasoning in auto-regressive LLMs, we aim to establish robust associations between triggers and an affirmative response prefix.<n>We observe an interesting resistance phenomenon where the LLM initially appears to agree but subsequently refuses to answer.
arXiv Detail & Related papers (2025-05-23T08:13:59Z) - Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs [83.11815479874447]
We propose a novel jailbreak attack framework, inspired by cognitive decomposition and biases in human cognition.<n>We employ cognitive decomposition to reduce the complexity of malicious prompts and relevance bias to reorganize prompts.<n>We also introduce a ranking-based harmfulness evaluation metric that surpasses the traditional binary success-or-failure paradigm.
arXiv Detail & Related papers (2025-05-03T05:28:11Z) - Prefill-Based Jailbreak: A Novel Approach of Bypassing LLM Safety Boundary [2.4329261266984346]
Large Language Models (LLMs) are designed to generate helpful and safe content.<n> adversarial attacks, commonly referred to as jailbreak, can bypass their safety protocols.<n>We introduce a novel jailbreak attack method that leverages the prefilling feature of LLMs.
arXiv Detail & Related papers (2025-04-28T07:38:43Z) - Improving LLM Safety Alignment with Dual-Objective Optimization [65.41451412400609]
Existing training-time safety alignment techniques for large language models (LLMs) remain vulnerable to jailbreak attacks.<n>We propose an improved safety alignment that disentangles DPO objectives into two components: (1) robust refusal training, which encourages refusal even when partial unsafe generations are produced, and (2) targeted unlearning of harmful knowledge.
arXiv Detail & Related papers (2025-03-05T18:01:05Z) - REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective [57.57786477441956]
We propose an adaptive and semantic optimization problem over the population of responses.
Our objective doubles the attack success rate (ASR) on Llama3 and increases the ASR from 2% to 50% with circuit breaker defense.
arXiv Detail & Related papers (2025-02-24T15:34:48Z) - CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models [18.06388944779541]
"jailbreaking" is the use of large language models to trigger unintended behaviors.
We propose a novel method to balance the jailbreak attack success rate with semantic coherence.
Our method is superior to state-of-the-art baselines in attack effectiveness.
arXiv Detail & Related papers (2025-02-17T02:49:26Z) - 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) - xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking [32.89084809038529]
Black-box jailbreak is an attack where crafted prompts bypass safety mechanisms in large language models.<n>We propose a novel black-box jailbreak method leveraging reinforcement learning (RL)<n>We introduce a comprehensive jailbreak evaluation framework incorporating keywords, intent matching, and answer validation to provide a more rigorous and holistic assessment of jailbreak success.
arXiv Detail & Related papers (2025-01-28T06:07:58Z) - LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds [98.20826635707341]
LIAR (Leveraging Inference time Alignment to jailbReak) is a fast and efficient best-of-N approach tailored for jailbreak attacks.<n>Our results demonstrate that a best-of-N approach is a simple yet highly effective strategy for evaluating the robustness of aligned LLMs.
arXiv Detail & Related papers (2024-12-06T18:02:59Z) - 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) - 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) - AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens [83.08119913279488]
We present a systematic analysis of the dependency relationships in jailbreak attack and defense techniques.
We propose three comprehensive, automated, and logical frameworks.
We show that the proposed ensemble jailbreak attack and defense framework significantly outperforms existing research.
arXiv Detail & Related papers (2024-06-06T07:24:41Z) - Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation [49.480978190805125]
Transfer attacks generate significant interest for black-box applications.
Existing works essentially directly optimize the single-level objective w.r.t. surrogate model.
We propose a bilevel optimization paradigm, which explicitly reforms the nested relationship between the Upper-Level (UL) pseudo-victim attacker and the Lower-Level (LL) surrogate attacker.
arXiv Detail & Related papers (2024-06-04T07:45:27Z) - Don't Say No: Jailbreaking LLM by Suppressing Refusal [15.350198454170895]
We introduce DSN (Don't Say No) attack, which combines a cosine decay schedule method with refusal suppression to achieve higher success rates.<n>Extensive experiments demonstrate that DSN outperforms baseline attacks and achieves state-of-the-art attack success rates (ASR)
arXiv Detail & Related papers (2024-04-25T07:15:23Z) - AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting [54.931241667414184]
We propose textbfAdaptive textbfShield Prompting, which prepends inputs with defense prompts to defend MLLMs against structure-based jailbreak attacks.
Our methods can consistently improve MLLMs' robustness against structure-based jailbreak attacks.
arXiv Detail & Related papers (2024-03-14T15:57:13Z) - Defending Large Language Models against Jailbreak Attacks via Semantic
Smoothing [107.97160023681184]
Aligned large language models (LLMs) are vulnerable to jailbreaking attacks.
We propose SEMANTICSMOOTH, a smoothing-based defense that aggregates predictions of semantically transformed copies of a given input prompt.
arXiv Detail & Related papers (2024-02-25T20:36:03Z) - 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)
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.