INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/8/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Subtle Injection for Ground-truth Inference of LLM Training Data arXiv:2606.06502v1 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 t…
https://arxiv.org/abs/2606.06502
sha256:3b8841e68b6bb293e2aaf4271ebade16b158bab15bbe00ee9c4734df0b5b06a2
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Threat actors mentioned
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ATT&CK techniques
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