SemTrace: Source-Grounded Semantic Signatures for Document Exposure Detection and Source-Copy Attribution

Large language models are increasingly used to transform sensitive or controlled documents into downstream text, creating a provenance problem of detecting exposure to a protected copy and attributing the text to its source copy. Existing generation-time watermarks often rely on decoder access, surface-form patterns, or source-irrelevant artifacts. Therefore, we propose SemTrace, a source-grounded semantic signature that encodes provenance through copy-specific choices among alternative source-supported facts. SemTrace verifies and pairs atomic propositions, assigns them through recipient-specific codewords, and uses erasure-aware NLI to recover the semantic pattern for detection and attribution. Experiments under controlled and relaxed generation show strong detection and resilience to paraphrasing, while controlled-generation experiments further demonstrate multi-copy source attribution. Under relaxed generation, SemTrace further outperforms the evaluated ICW baselines in detection and robustness while maintaining competitive text quality. More broadly, SemTrace provides a source-grounded approach to document provenance, demonstrating that semantic content choices can serve as robust and attributable provenance signals in downstream generation.