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Abstract

As large language models (LLMs) are increasingly trained on scraped web corpora without authorisation, content owners require forensic methods to prove that their documents were included in a models training set. We propose SIGIL (Subtle Injection for Ground-truth Inference of LLM training data), a framework that embeds imperceptible canary sequences into protected text and code such that any LLM trained on those documents exhibits statistically detectable behavioural signatures when probed with targeted queries.