INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/18/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning arXiv:2606.19129v1 Announce Type: new Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors. While enforcing confidentiality calls …
https://arxiv.org/abs/2606.19129
sha256:d490a773df8e4f75c91fb05af5c020a01aa7dbd8dbc7f57143c406dd4b597634
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
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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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