Semantic Underspecification as a Safety Risk in Text-to-Image Generation

Existing text-to-image (T2I) safety research primarily focuses on harmful semantics that are explicitly expressed in prompts, deliberately encoded through adversarial manipulation, or already manifested in generated images. However, in everyday T2I use, ordinary users may unintentionally omit contextual information that appears irrelevant to their immediate description but is critical for safety. Such seemingly benign prompts can remain compatible with both benign and unsafe interpretations, allowing a T2I model to instantiate an unsafe scene despite the absence of any explicit harmful request or malicious intent. We identify and formulate this previously underexplored pre-generation safety risk as semantic underspecification, and construct a curated benchmark of 100 prompts across five representative safety risk categories to systematically study it. To mitigate this risk, we propose Risk-aware Semantic Completion (RSC), an iterative prompt repair framework that resolves safety-critical semantic gaps before image generation. RSC first interprets plausible unsafe readings of the current prompt and identifies the missing semantics that enable them, then introduces targeted benign context to disambiguate the scene. A semantic preservation check subsequently verifies whether the repaired prompt retains the user's explicitly specified content; failed candidates are returned for further repair. By combining risk interpretation, targeted completion, and preservation-aware repair, RSC suppresses unsafe interpretations without simply removing risky objects, altering user-specified actions, or broadly rewriting the original scene. On the benchmark, RSC achieves a 92.0% Risk Mitigation Rate, a 92.0% Semantic Preservation Rate.