SIGIL
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Subtle Injection for Ground-truth Inference of LLM Training Data
Announce Type: new 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 model's training set. We propose \textbf{SIGIL} (\textbf{S}ubtle \textbf{I}njection for \textbf{G}round-truth \textbf{I}nference of \textbf{L}LM training data), a framework that embeds imperceptible \emph{canary sequences} into protected text and code such that any LLM...
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Announce Type: new Abstract: Systems programming languages traditionally struggle with the tension between physical transparency and compile-time memory safety. C++ provides direct, zero-cost hardware access but lacks strict safety boundaries, whereas Rust guarantees safety at the cost of complex lifetime annotations and implicit dereferencing chains. In this paper, we present Toka, a native systems programming language that establishes physical transparency in resource management via...