INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 5/14/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? arXiv:2605.12827v1 Announce Type: new Abstract: Graph neural networks (GNNs) deployed as cloud services can be \emph{stolen} through \emph{model-extraction attacks}, which train a surrogate from query responses to reproduce the target's behaviour, and a growing line of ownership defenses tries to prevent or trace such theft. The title of this paper asks two questions: \emph{how hard is it to s…
https://arxiv.org/abs/2605.12827
sha256:2ac038b686a969a8baed6f6b3df906439180a5e2390f84fb13ed1f4f2971a63c
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
Linked with report → mentions → indicator. Values open the indicator workspace.
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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