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).
IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts arXiv:2606.09908v1 Announce Type: new Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation. Prior work focuses mainly on individual-level risks, overlooking \textbf{interdependent privacy (IDP)}--where one person's data may be rev…
https://arxiv.org/abs/2606.09908
sha256:dd8ff47cd1dc8b504bcd38d1e7158bb5acfe21d01072d855f065486f8e49b0db
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
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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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