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Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models

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arxiv 2408.00523 v3 pith:AFD52LUV submitted 2024-08-01 cs.CR cs.AIcs.LG

Yingkai Dong , Xiangtao Meng , Ning Yu , Zheng Li , Shanqing Guo This is my paper
classification cs.CRcs.AIcs.LG
keywords jailbreakjailfuzzermodelspromptsattacksjailbreakingagentsblack-box
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-image (T2I) generative models have revolutionized content creation by transforming textual descriptions into high-quality images. However, these models are vulnerable to jailbreaking attacks, where carefully crafted prompts bypass safety mechanisms to produce unsafe content. While researchers have developed various jailbreak attacks to expose this risk, these methods face significant limitations, including impractical access requirements, easily detectable unnatural prompts, restricted search spaces, and high query demands on the target system. In this paper, we propose JailFuzzer, a novel fuzzing framework driven by large language model (LLM) agents, designed to efficiently generate natural and semantically meaningful jailbreak prompts in a black-box setting. Specifically, JailFuzzer employs fuzz-testing principles with three components: a seed pool for initial and jailbreak prompts, a guided mutation engine for generating meaningful variations, and an oracle function to evaluate jailbreak success. Furthermore, we construct the guided mutation engine and oracle function by LLM-based agents, which further ensures efficiency and adaptability in black-box settings. Extensive experiments demonstrate that JailFuzzer has significant advantages in jailbreaking T2I models. It generates natural and semantically coherent prompts, reducing the likelihood of detection by traditional defenses. Additionally, it achieves a high success rate in jailbreak attacks with minimal query overhead, outperforming existing methods across all key metrics. This study underscores the need for stronger safety mechanisms in generative models and provides a foundation for future research on defending against sophisticated jailbreaking attacks. JailFuzzer is open-source and available at this repository: https://github.com/YingkaiD/JailFuzzer.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models

    cs.AI 2026-07 conditional novelty 6.0 of 10

    MIND learns a 'defense profile' of a T2I model from fine-grained feedback, then uses it to guide an evolutionary search, achieving 95.62% ASR across six defenses and 91.58% on Wan-2.5.

  2. $PC^2$: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models

    cs.CR 2026-01 conditional novelty 6.0 of 10

    PC2, a multilingual descriptive-rewriting attack, makes GPT-based text-to-image models generate politically controversial images of real public figures despite safety filters.

  3. SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

    cs.CR 2025-10 conditional novelty 6.0 of 10

    A systemization of LLM jailbreak security that adds linked taxonomies, an evaluation platform, and JailbreakDB, while its main attack–defense comparison results remain deferred.

  4. When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling

    math.OC 2026-04 unverdicted novelty 5.0 of 10

    DRL dynamic algorithm configuration trained on small carbon-aware flow-shop instances generalizes and outperforms static tuning as instance complexity grows.

  5. Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation

    cs.CL 2025-08 reject novelty 5.0 of 10

    Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.

  6. Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A new evaluation tool uses (vision-)language model world knowledge to rank nearby concepts and craft adversarial prompts, showing that diffusion unlearning is incomplete and that semantic similarity correlates with co...

  7. Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message

    cs.AI 2025-07 reject novelty 5.0 of 10

    Trojan Horse Prompting injects malicious instructions into a fabricated assistant message in the API chat history, aiming to bypass Gemini's safety filters, but no quantitative evidence is provided.

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