INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/17/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs arXiv:2606.17110v1 Announce Type: new Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets. Ensuring the privacy of such data against extraction attacks has become a central concern. In this paper, we ask whether an attacker who can poison a portion of the training data can…
https://arxiv.org/abs/2606.17110
sha256:848a7e3f608b16b6f99681435ec63f4ba53cd5978261d9bf600b1cb7b5032e68
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
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Malware families
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Threat actors mentioned
Allowlist mentions — not a formal attribution verdict.
ATT&CK techniques
MITRE IDs referenced in text and present in local technique table.
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