Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception
arXiv 2604.20895•45ce0b1884c1dc1310357a16b1d1b30569e7c153587a1b2579b49b2177f263a8
DNN-limitationsISO-26262ISO/SAE-21434LLM-agentsLLM-safetyRAGSDN-intrusion-detection','keystroke-inference','VR-securitySafeRedirectadaptive-defenseagentic-modelsarxiv-source-leakageautonomous-drivingdata-leakagedata-poisoningfunction-callingfunction-hijackinginternal-safety-collapseinterpretability-attacksjailbreakingmembership-inferenceretrieval-augmented-generationrisk-assessmentsecurity-recall-divergencesensitivity-uncertainty-alignmentuncertainty-calibration
Paper metadata
- arXiv ID
- 2604.20895
- Version
- Not specified by this published record
- Category
- Computer Science — Cryptography and Security (cs.CR)
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Evidence and limitations
- Source ID
- arxiv_cs_cr
- Record identifier
- 45ce0b1884c1dc1310357a16b1d1b30569e7c153587a1b2579b49b2177f263a8
- Enrichment time
- 2026-04-24T07:23:36Z
- AI-assisted enrichment
- Yes
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