PixJail: Self-Evolving Paper-to-Pipeline Reproduction for Text-to-Image Jailbreak Evaluation

• URL: http://arxiv.org/abs/2606.24081v1
• Date: Tue, 23 Jun 2026 02:49:10 GMT
• Title: PixJail: Self-Evolving Paper-to-Pipeline Reproduction for Text-to-Image Jailbreak Evaluation
• Abstract summary: Text-to-Image jailbreak evaluation is not a single prompt-level test, but a pipeline-level problem shaped by multiple stages.<n>We propose PixJail, a self-evolving paper-to-pipeline agent framework for reproducible T2I jailbreak evaluation.<n>We reproduce eleven representative T2I jailbreak methods, including both code-available and code-unavailable papers.
• Score: 21.837531720036427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Text-to-Image (T2I) jailbreak techniques evolve rapidly, existing benchmarks and reproduction workflows often struggle to keep pace. More importantly, T2I jailbreak evaluation is not a single prompt-level test, but a pipeline-level problem shaped by multiple stages, including prompt transformation, image generation, safety filtering, and multimodal judging. This makes results across papers difficult to reliably reproduce and fairly compare. To bridge this gap, we propose PixJail, a self-evolving paper-to-pipeline agent framework for reproducible T2I jailbreak evaluation. Given a T2I jailbreak paper and optional reference code, PixJail rapidly constructs a paper-specific attack module and a runnable evaluation pipeline under a unified contract, while faithfully reproducing the original experimental results. PixJail further maintains a memory bank that stores paper digests, attack evolution patterns, reusable templates, failure cases, and versioned artifacts, enabling future reproduction efforts to reuse prior experience. We reproduce eleven representative T2I jailbreak methods, including both code-available and code-unavailable papers. Under their original settings, our framework accurately recovers prior results with minimal error (2.1\% average, 0\% median). We hope that PixJail can serve as a unified foundation for future T2I jailbreak reproduction and evaluation, significantly reducing manual effort.

Related papers

• LLM Jailbreak Detection for (Almost) Free! [62.466970731998714]
  Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks.<n>Jailbreak detection methods show promise in mitigating jailbreak attacks through the assistance of other models or multiple model inferences.<n>We propose a Free Jailbreak Detection (FJD) which prepends an affirmative instruction to the input and scales the logits by temperature to further distinguish between jailbreak and benign prompts.
  arXiv  Detail & Related papers  (2025-09-18T02:42:52Z)
• Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models [80.66766532477973]
  Test-time IMmunization (TIM) can adaptively defend against various jailbreak attacks in a self-evolving way.<n>Test-time IMmunization (TIM) can adaptively defend against various jailbreak attacks in a self-evolving way.
  arXiv  Detail & Related papers  (2025-05-28T11:57:46Z)
• IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves [70.43466586161345]
  IDEATOR is a novel jailbreak method that autonomously generates malicious image-text pairs for black-box jailbreak attacks.<n>Our benchmark results on 11 recently releasedVLMs reveal significant gaps in safety alignment.<n>For instance, our challenge set ASRs of 46.31% on GPT-4o and 19.65% on Claude-3.5-Sonnet, underscoring the urgent need for stronger defenses.
  arXiv  Detail & Related papers  (2024-10-29T07:15:56Z)
• Multimodal Pragmatic Jailbreak on Text-to-image Models [42.53834190690214]
  This work introduces a novel type of jailbreak, which triggers T2I models to generate the image with visual text.<n>We benchmark nine representative T2I models, including two closed-source commercial models.<n>Our work provides a foundation for further development towards more secure and reliable T2I models.
  arXiv  Detail & Related papers  (2024-09-27T21:23:46Z)
• Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models [15.582860145268553]
  JailFuzzer is a novel fuzzing framework driven by large language model (LLM) agents.<n>It generates natural and semantically coherent prompts, reducing the likelihood of detection by traditional defenses.<n>It achieves a high success rate in jailbreak attacks with minimal query overhead.
  arXiv  Detail & Related papers  (2024-08-01T12:54:46Z)
• Automatic Jailbreaking of the Text-to-Image Generative AI Systems [76.9697122883554]
  We study the safety of the commercial T2I generation systems, such as ChatGPT, Copilot, and Gemini, on copyright infringement with naive prompts. We propose a stronger automated jailbreaking pipeline for T2I generation systems, which produces prompts that bypass their safety guards. Our framework successfully jailbreaks the ChatGPT with 11.0% block rate, making it generate copyrighted contents in 76% of the time.
  arXiv  Detail & Related papers  (2024-05-26T13:32:24Z)
• JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models [123.66104233291065]
  Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address. JailbreakBench is an open-sourced benchmark with the following components.
  arXiv  Detail & Related papers  (2024-03-28T02:44:02Z)
• A StrongREJECT for Empty Jailbreaks [72.8807309802266]
  StrongREJECT is a high-quality benchmark for evaluating jailbreak performance. It scores the harmfulness of a victim model's responses to forbidden prompts. It achieves state-of-the-art agreement with human judgments of jailbreak effectiveness.
  arXiv  Detail & Related papers  (2024-02-15T18:58:09Z)

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.