INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/10/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
SoK: Colluding Adversaries in Machine Learning Pipelines arXiv:2606.10091v1 Announce Type: new Abstract: Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objectives, knowledge, and capabilities) can collude by executing one attack to amplify others. Existing work lacks a systematic framework to explore collusion among adversaries, and to study the implications of the adversaries' …
https://arxiv.org/abs/2606.10091
sha256:6d3bf597494777597134e97f9cb87cae4c6cb38fa806a5c152a882bc0113d3bc
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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