Ecosystem Trust Profiles

2026-03-09T07:23:37Z010d35bf16fb2a42ed0635349f54fa27a84e05a558a38d775a196a5acfa6ba1a
CUDA bugsDSHGPU-native fuzzingHDL securityISS-RegAuthLLM jailbreakLiDAR authenticationRAGRefusal Erasure AttackSAHASecureRAG-RTLTEE attestationThermoCAPTCHAattention-head attackscryptanalysishardware vulnerability detectionheterogeneous systemsmodel alignmentneural distinguisherprivacy-preserving authenticationproof-of-guardrailquantization-aware trainingself-evolving agents','deception','cross-ecosystem trust','data-spoofing resistancethermal imaging

What happened

The document is a collection of recent security- and safety-relevant research across AI, hardware, and authentication domains. Key findings: (1) New LLM-level jailbreak techniques target deep attention heads (SAHA) and exploit a disentanglement between recognition and execution (DSH), culminating in a Refusal Erasure Attack (REA) that surgically disables refusal behavior — demonstrating significant new attack surface in model internals. (2) Proof-of-guardrail proposes TEE-signed attestations to cryptographically prove guardrail execution, but authors note residual deception/jailbreak risks if/

Why it matters

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

Evidence and limitations

Source ID
arxiv_cs_cr
Record identifier
010d35bf16fb2a42ed0635349f54fa27a84e05a558a38d775a196a5acfa6ba1a
Enrichment time
2026-03-09T07:23:37Z
AI-assisted enrichment
Yes

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