Exposing Implicit Vulnerabilities in Text-to-Image Models via Adversarial Agentic Probing

Red-teaming Text-to-Image (T2I) models is essential for safedeployment, yet it remains particularly challenging against implicit ad-versarial prompts. Unlike explicit adversarial prompts that can be read-ily identified and blocked, implicit ones are much harder to detect: theprompts appear benign on the text surface yet still lead to inappropri-ate visual content. To address this, we propose Adversarial Probing forImplicit VulnErabilities (AdvPIE), a multimodal agentic framework toexpose implicit vulnerabilities without requiring access to the parametersof target models. AdvPIE adopts a policy agent to generate and refineimplicit adversarial prompts based on the feedback from a judge agent.To construct informative feedback, the judge agent provides modality-specific safety evaluation at both global and relative levels across itera-tions. To effectively leverage the feedback, we propose a novel CumulativeAdversarial Decoding strategy for the policy agent, which dynamicallyreweights token distributions to favor tokens that lead to more harmfulimages while preserving sampling diversity. Extensive experiments onstandard and safety-aligned T2I models show that AdvPIE1 effectivelyuncovers implicit vulnerabilities, outperforming various baseline meth-ods. Warning: This paper includes content that may be disturbing.