Risk Models as Mediating Artifacts: A Postphenomenological Analysis of the CIIM Framework in Cybersecurity Practice

2026-04-28T07:23:40Z2ba20976fafb05af1dca5854e314de0e4117b9ac2f956fa8eb90bcf5000b54ad
Android malware detectionAutoRISEDeepSignatureLLM securityReconstructive Authority ModelRouteGuardagent securityattention hijackingattestationautomated red‑teamingeFPGAfault injectionformal verification (SVA)hardware securityhardware trojansimage authenticationinternal‑signal detectionjailbreaksmodel extractionself‑supervised learningside‑channel attacksskill poisoningsupply‑chain threatstemporal datasetwatermarking

What happened

A batch of recent arXiv papers surveying and advancing security-relevant research across multiple layers of ML and systems stacks. Key highlights: AutoRISE and other automated red‑teaming work move from prompt search to program/strategy search, substantially increasing jailbreak/attack success; RouteGuard demonstrates that skill‑poisoning of LLM agents produces an internal ‘attention‑hijack’ signature and that internal‑signal detectors outperform lexical filters (F1≈0.8834, recovers ≈90.5% of attacks missed by text screening); the Reconstructive Authority Model (RAM) argues attestation alone (

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_cr
Record identifier
2ba20976fafb05af1dca5854e314de0e4117b9ac2f956fa8eb90bcf5000b54ad
Enrichment time
2026-04-28T07:23:40Z
AI-assisted enrichment
Yes

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