INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/15/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents arXiv:2606.13757v1 Announce Type: new Abstract: Large language model (LLM) reviewers are increasingly used in pull-request (PR) workflows, where their approvals help decide which code is merged into a repository. This raises a question that benchmarks for static vulnerability detection or code generation do not address: can an automated reviewer reject a malicious contribution when the attacker controls both…
https://arxiv.org/abs/2606.13757
sha256:dbe571c674d48a6c10ac59f9e89c847023677d4cc2d849c207521db5a7d23325
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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