{"id":"https://openalex.org/W7165139645","doi":"https://doi.org/10.1145/3787109.3815303","title":"METAttack: Quantifying the Vulnerabilities of Metadata Bit Flips in N:M Sparse Models","display_name":"METAttack: Quantifying the Vulnerabilities of Metadata Bit Flips in N:M Sparse Models","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165139645","doi":"https://doi.org/10.1145/3787109.3815303"},"language":null,"primary_location":{"id":"doi:10.1145/3787109.3815303","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787109.3815303","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3787109.3815303","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138926103","display_name":"Qiang Fu","orcid":"https://orcid.org/0009-0007-4256-2633"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Fu","raw_affiliation_strings":["Electrical and Computer Engineering, Binghamton University, Binghamton, USA"],"raw_orcid":"https://orcid.org/0009-0007-4256-2633","affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Binghamton University, Binghamton, USA","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087651642","display_name":"Wenfeng Zhao","orcid":"https://orcid.org/0000-0002-2933-750X"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenfeng Zhao","raw_affiliation_strings":["Electrical and Computer Engineering, Binghamton University, Binghamton, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-2933-750X","affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Binghamton University, Binghamton, NY, USA","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"509","last_page":"515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.007799999788403511,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11424","display_name":"Security and Verification in Computing","score":0.00559999980032444,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.9380000233650208},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5656999945640564},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5619000196456909},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5238999724388123},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.478300005197525},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3910999894142151},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.3452000021934509}],"concepts":[{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.9380000233650208},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7893999814987183},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5656999945640564},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5619000196456909},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3910999894142151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34860000014305115},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3278000056743622},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C30872290","wikidata":"https://www.wikidata.org/wiki/Q1172389","display_name":"Data element","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C117011727","wikidata":"https://www.wikidata.org/wiki/Q1278488","display_name":"Bit (key)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3787109.3815303","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787109.3815303","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3787109.3815303","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787109.3815303","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4214857816696167}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2751366252","https://openalex.org/W2963122961","https://openalex.org/W2981860227","https://openalex.org/W4245276998","https://openalex.org/W4291653336","https://openalex.org/W4380875579","https://openalex.org/W4390872667","https://openalex.org/W4413147853","https://openalex.org/W7106159637","https://openalex.org/W7133243328"],"related_works":[],"abstract_inverted_index":{"N:M":[0,13,28,35,134],"sparsity":[1,29,70],"is":[2],"an":[3],"emerging":[4],"paradigm":[5],"for":[6,53],"structurally":[7],"sparsifying":[8],"deep":[9,152],"learning":[10,153],"models.":[11,97,166],"In":[12],"sparse":[14,36,96,135],"models,":[15],"each":[16],"contiguous":[17],"block":[18],"of":[19,65,88,94,116,141,151],"M":[20],"weights":[21,120],"contains":[22],"exactly":[23],"N":[24],"non-zero":[25,44,119],"entries.":[26],"Hence,":[27],"facilitates":[30],"efficient":[31,164],"hardware":[32],"designs.":[33],"However,":[34],"models":[37,136,154],"rely":[38],"on":[39],"compact":[40],"metadata":[41,71,93,111],"to":[42,82,109,162],"index":[43],"weights,":[45],"they":[46],"inevitably":[47],"introduce":[48],"a":[49,79,106,138,148,159],"novel":[50,80],"attack":[51,100],"surface":[52],"adversarial":[54],"techniques":[55],"like":[56],"Bit-Flip":[57],"Attacks":[58],"(BFA).":[59],"To":[60],"date,":[61],"the":[62,86,92,110,122,127,163],"security":[63],"implications":[64],"such":[66],"bit":[67,89,142],"flips":[68,90],"within":[69],"remain":[72],"largely":[73],"unexplored.":[74],"This":[75],"paper":[76],"presents":[77],"METAttack,":[78],"framework":[81],"exploit":[83],"and":[84,118,155],"quantify":[85],"vulnerabilities":[87],"in":[91],"2:4":[95],"The":[98],"new":[99],"features":[101],"Non-Collision":[102],"Metadata":[103],"Attack":[104],"(NCMA),":[105],"stealthier":[107],"BFA":[108],"which":[112],"induces":[113],"downstream":[114],"misalignment":[115],"activations":[117],"during":[121],"decoding":[123],"process.":[124],"Without":[125],"altering":[126],"weight":[128],"magnitude,":[129],"METAttack":[130,146],"can":[131],"still":[132],"compromise":[133],"with":[137],"few":[139],"round":[140],"flips.":[143],"We":[144],"validated":[145],"across":[147],"diverse":[149],"set":[150],"datasets,":[156],"positioning":[157],"it":[158],"potential":[160],"threat":[161],"AI":[165]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-06-19T00:00:00"}
