INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/25/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Certification of Machine Learning Models via Directional Sharpness arXiv:2606.25004v1 Announce Type: cross Abstract: In machine learning, model certification has been identified as an important method for gaining assurance about a model's trustworthiness and quality. A model's quality is largely determined by its ability to generalize, i.e., to perform well on data beyond what it was trained on. It is not possible to certify generalization directly, however, as it depends on…
https://arxiv.org/abs/2606.25004
sha256:6de54c316f111801cbab69665df0f4066192f9c8e078ca660c5d28791b57db3c
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
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No indicators linked for this report.
Malware families
Allowlist token matches only.
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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