{"id":"W4412176745","doi":"10.1007/978-3-031-97623-0_9","title":"Red Light for Security: Uncovering Auto Feature Check and Access Control Gaps in AAOS","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Feature (linguistics); Access control; Artificial intelligence; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004621789,0.0003409474,0.000323075,0.001286819,0.0006487042,0.001374975,0.0006489946,0.0007978726,0.002310841],"category_scores_gemma":[0.003778442,0.0002590794,0.0002768504,0.0008701087,0.0006671526,0.002246854,0.0009415689,0.001319277,0.000881959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003885396,"about_ca_system_score_gemma":0.0007384022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002191362,"about_ca_topic_score_gemma":0.00425096,"domain_scores_codex":[0.9995909,0.00004303312,0.00001187888,0.00009489078,0.0001812132,0.00007796819],"domain_scores_gemma":[0.9975351,0.001311201,0.0003568718,0.0003979389,0.0002833491,0.000115507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008362258,0.0005314014,0.09174525,0.0004187094,0.00007083689,0.001641809,0.003783773,0.01107667,0.05041681,0.0384732,0.04089053,0.7601147],"study_design_scores_gemma":[0.00006342639,0.0005232783,0.07458573,0.0004979123,0.0002017717,0.003804083,0.005991276,0.5308174,0.1118903,0.1806866,0.0907487,0.0001894299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8316858,0.003458834,0.1169689,0.001876285,0.0004474568,0.0001044264,0.002313265,0.0084744,0.03467074],"genre_scores_gemma":[0.9423489,0.0005632495,0.04860787,0.0003129797,0.00007321165,0.00003237829,0.001048874,0.0004815331,0.006530871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002310841,"threshold_uncertainty_score":0.007730544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00841651904773657,"score_gpt":0.265112826448567,"score_spread":0.2566963074008305,"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."}}