Dynamic Optimization and Safety Indicator Injection for Jailbreaking Text-to-Image Models with Multimodal Safety Filters
arXiv:2505.18979v2 Announce Type: replace Abstract: Text-to-image (T2I) models can generate not-safe-for-work (NSFW) content, motivating multi-stage safety pipelines with both text and image filters. Newer LLM-based filters detect latent intent beyond keywords, making token-level perturbation attacks unreliable. Our evaluation further shows that existing jailbreak methods exhibit a sharp trade-off between filter evasion and semantic fidelity, while also requiring excessive queries to succeed. We introduce \textbf{OptJail}, an automated jailbreak framework that combines dynamic prompt optimizat
The proliferation of advanced text-to-image models and the increasing sophistication of safety filters necessitate more robust methods for circumventing these safeguards, leading to continuous research in AI security and red-teaming.
This development highlights the ongoing arms race between AI model developers and those seeking to exploit or jailbreak them, underscoring critical vulnerabilities in AI safety and governance.
The ability to more effectively jailbreak multimodal AI safety filters means that current defensive measures are less reliable, requiring significant re-evaluation and improvement in AI safety strategies.
- · AI red-teaming researchers
- · Cybersecurity firms specializing in AI
- · AI model developers
- · Users relying solely on current AI safety filters
AI developers will be forced to rapidly innovate new, more resilient safety mechanisms for their text-to-image models.
Increased public scrutiny and regulatory pressure surrounding the safety and ethical deployment of powerful AI systems will likely follow.
The perceived trustworthiness of AI systems could erode, impacting their adoption in sensitive applications if safeguards are consistently breached.
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