Benign Inputs, Harmful Outputs: Cross-Modal Jailbreaking via Distributed Semantic Recomposition

• URL: http://arxiv.org/abs/2606.01837v1
• Date: Mon, 01 Jun 2026 07:49:12 GMT
• Title: Benign Inputs, Harmful Outputs: Cross-Modal Jailbreaking via Distributed Semantic Recomposition
• Abstract summary: Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable capabilities in content synthesis and autonomous reasoning.<n>Previous safety guardrails are primarily designed for unimodal textual input interception.<n>We propose Distributed Semantic Recomposition (DSR), a novel cross-modal jailbreak framework.
• Score: 10.177453923096754
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable capabilities in content synthesis and autonomous reasoning. Previous safety guardrails are primarily designed for unimodal textual input interception, leaving them vulnerable to cross-modal jailbreak attacks. However, regardless unimodal textual attack or cross-modal jailbreak, typically inclusive part of explicit harmful or sensitive content at the input level, which is called Harm-Bearing. It allow the model's safety filters to detect and block such content easily. To address this limitations, we propose Distributed Semantic Recomposition (DSR), a novel cross-modal jailbreak framework that decomposes harmful intent into a set of benign textual and visual primitives. By exploiting the model's reasoning ability, DSR enables the latent fusion of these seemingly innocent components into harmful outputs during the cross-modal inference phase. Extensive experiments on multiple commercial MLLMs pipelines demonstrate that DSR achieves superior attack success rates while maintaining an extremely low or even negligible input toxicity rate. Our findings uncover a critical Utility-Safety Paradox in MLLMs, where the model's instruction-following proficiency facilitates its own cognitive exploitation. Content Warning: This paper contains harmful model responses.

Related papers

• CSR-Bench: A Benchmark for Evaluating the Cross-modal Safety and Reliability of MLLMs [10.42126976065225]
  Multimodal large language models (MLLMs) enable interaction over both text and images.<n>This paper introduces CSR-Bench, a benchmark for evaluating cross-modal reliability.<n>We evaluate 16 state-of-the-art MLLMs and observe systematic cross-modal alignment gaps.
  arXiv  Detail & Related papers  (2026-02-03T08:49:44Z)
• A Fragile Guardrail: Diffusion LLM's Safety Blessing and Its Failure Mode [51.43498132808724]
  We show that Diffusion large language models (D-LLMs) have intrinsic robustness against jailbreak attacks.<n>We identify a simple yet effective failure mode, termed context nesting, where harmful requests are embedded within structured benign contexts.<n>We show that this simple strategy is sufficient to bypass D-LLMs' safety blessing, achieving state-of-the-art attack success rates.
  arXiv  Detail & Related papers  (2026-01-30T23:08:14Z)
• Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography [77.44136793431893]
  We propose a novel jailbreak paradigm that introduces dual steganography to covertly embed malicious queries into benign-looking images.<n>Our Odysseus successfully jailbreaks several pioneering and realistic MLLM-integrated systems, achieving up to 99% attack success rate.
  arXiv  Detail & Related papers  (2025-12-23T08:53:36Z)
• RL-MTJail: Reinforcement Learning for Automated Black-Box Multi-Turn Jailbreaking of Large Language Models [60.201244463046784]
  Large language models are vulnerable to jailbreak attacks.<n>This paper studies black-box multi-turn jailbreaks, aiming to train attacker LLMs to elicit harmful content from black-box models.
  arXiv  Detail & Related papers  (2025-12-08T17:42:59Z)
• Multimodal Safety Is Asymmetric: Cross-Modal Exploits Unlock Black-Box MLLMs Jailbreaks [33.836587055255954]
  Multimodal large language models (MLLMs) have demonstrated significant utility across diverse real-world applications.<n>But MLLMs remain vulnerable to jailbreaks, where adversarial inputs can collapse their safety constraints and trigger unethical responses.<n>We develop PolyJailbreak, a black-box jailbreak method grounded in reinforcement learning.
  arXiv  Detail & Related papers  (2025-10-20T08:03:39Z)
• When Safe Unimodal Inputs Collide: Optimizing Reasoning Chains for Cross-Modal Safety in Multimodal Large Language Models [50.66979825532277]
  We introduce Safe-Semantics-but-Unsafe-Interpretation (SSUI), the first dataset featuring interpretable reasoning paths tailored for a cross-modal challenge.<n>A novel training framework, Safety-aware Reasoning Path Optimization (SRPO), is also designed based on the SSUI dataset.<n> Experimental results show that our SRPO-trained models achieve state-of-the-art results on key safety benchmarks.
  arXiv  Detail & Related papers  (2025-09-15T15:40:58Z)
• JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering [73.962469626788]
  Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus.<n>We propose JPS, underlineJailbreak MLLMs with collaborative visual underlinePerturbation and textual underlineSteering.
  arXiv  Detail & Related papers  (2025-08-07T07:14:01Z)
• Cross-Modal Obfuscation for Jailbreak Attacks on Large Vision-Language Models [11.867355323884217]
  We present a novel black-box jailbreak attack framework that decomposes malicious prompts into semantically benign visual and textual fragments.<n>Our approach supports adjustable reasoning complexity and requires significantly fewer queries than prior attacks, enabling both stealth and efficiency.
  arXiv  Detail & Related papers  (2025-06-20T05:30:25Z)
• MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks [85.3303135160762]
  MIRAGE is a novel framework that exploits narrative-driven context and role immersion to circumvent safety mechanisms in Multimodal Large Language Models.<n>It achieves state-of-the-art performance, improving attack success rates by up to 17.5% over the best baselines.<n>We demonstrate that role immersion and structured semantic reconstruction can activate inherent model biases, facilitating the model's spontaneous violation of ethical safeguards.
  arXiv  Detail & Related papers  (2025-03-24T20:38:42Z)

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.