{"id":"W4387298350","doi":"10.1145/3607199.3607212","title":"MIFP: Selective Fat-Pointer Bounds Compression for Accurate Bounds Checking","year":2023,"lang":"en","type":"article","venue":"","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Padding; Pointer (user interface); Compression (physics); Data compression; Pointer analysis; Parallel computing; Overhead (engineering); Theoretical computer science; Algorithm; Programming language; Computer hardware; Static analysis; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004235448,0.0001422904,0.0001552461,0.0001604537,0.0005230553,0.0005186975,0.0008033749,0.0000761701,0.00002054303],"category_scores_gemma":[0.00009235412,0.0001304532,0.0001001059,0.0008247463,0.00003771799,0.0005303159,0.0003325073,0.0001393249,0.0001296611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006060434,"about_ca_system_score_gemma":0.00005467486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002130673,"about_ca_topic_score_gemma":0.000007207962,"domain_scores_codex":[0.9985946,0.00005194381,0.0002702299,0.0004827367,0.0002084365,0.0003920457],"domain_scores_gemma":[0.9988497,0.0003624911,0.0001114402,0.0004141445,0.0001874086,0.00007479054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009278946,0.0002495589,0.001683473,0.0002101999,0.0001669916,0.00001459985,0.01932749,0.002130733,0.02480128,0.5901403,0.2870921,0.07409043],"study_design_scores_gemma":[0.0006230116,0.00007795359,0.002027039,0.00005547373,0.000004570413,0.000007880546,0.0001838703,0.9058754,0.02445975,0.0173782,0.04902522,0.0002816371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02390608,0.0000315603,0.9661497,0.001755879,0.001139964,0.0002751547,0.00000278086,0.001023466,0.005715404],"genre_scores_gemma":[0.9752274,0.000006505124,0.02226093,0.0006821327,0.0002333227,0.00005225082,0.00001490053,0.00001388619,0.001508687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9513213,"threshold_uncertainty_score":0.5319729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05485408248253466,"score_gpt":0.3278535568381398,"score_spread":0.2729994743556051,"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."}}