{"id":"W4393750598","doi":"10.48550/arxiv.2404.00139","title":"Security Risks Concerns of Generative AI in the IoT","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta; Kennesaw State University; Georgia State University; Auburn University; University of Minnesota; Beijing University of Posts and Telecommunications; George Mason University; National Science Foundation","keywords":"Generative grammar; Computer science; Computer security; Internet of Things; Risk analysis (engineering); Business; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01303704,0.0005487854,0.0004606841,0.001286169,0.005148418,0.008897896,0.001065571,0.006445302,0.002204049],"category_scores_gemma":[0.0198605,0.000488847,0.0008060863,0.0007416564,0.03476834,0.01029846,0.007001315,0.00781359,0.0003538644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004645108,"about_ca_system_score_gemma":0.003175352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001491506,"about_ca_topic_score_gemma":0.0008939251,"domain_scores_codex":[0.9877771,0.007481196,0.000325102,0.0007503293,0.002974062,0.0006922655],"domain_scores_gemma":[0.9714456,0.02148654,0.001851527,0.002636611,0.001862173,0.0007176052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006928383,0.00001029715,0.0004295647,0.00001805414,0.00000513465,0.00009742372,0.002329118,0.0009802217,0.0001631718,0.9932659,0.0003604023,0.002333812],"study_design_scores_gemma":[0.000005659244,0.00001223928,0.0002918638,0.00008916288,0.00000684232,0.0001838402,0.001718439,0.002118056,0.0002904278,0.9837362,0.01153261,0.00001463083],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2168566,0.005005486,0.1833961,0.1564745,0.0004595602,0.0001498754,0.00008467107,0.0001581262,0.4374151],"genre_scores_gemma":[0.986348,0.0009293912,0.007278854,0.002354689,0.0001585194,0.00009331146,0.0000126495,0.00003243814,0.002792141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01303704,"threshold_uncertainty_score":0.06894732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2652709628314394,"score_gpt":0.3344822870650601,"score_spread":0.06921132423362075,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}