{"id":"https://openalex.org/W7140146991","doi":"https://doi.org/10.48550/arxiv.2603.19375","title":"Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents","display_name":"Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7140146991","doi":"https://doi.org/10.48550/arxiv.2603.19375"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19375","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19375","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.19375","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130391787","display_name":"Toan Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tran, Toan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021188335","display_name":"Olivera Kotevska","orcid":"https://orcid.org/0000-0003-1677-2243"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kotevska, Olivera","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130331064","display_name":"Li Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Li","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5130391787"],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9301000237464905,"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":0.9301000237464905,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.013199999928474426,"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/T10028","display_name":"Topic Modeling","score":0.010099999606609344,"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/scalability","display_name":"Scalability","score":0.6970000267028809},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6875},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5580999851226807},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.43220001459121704},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3540000021457672},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3481000065803528}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7175999879837036},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6970000267028809},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6123999953269958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5927000045776367},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5580999851226807},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26440000534057617}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19375","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19375","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19375","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19375","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4648057520389557,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Membership":[0],"inference":[1],"attacks":[2],"(MIAs),":[3],"which":[4],"enable":[5],"adversaries":[6],"to":[7,26,56,77,122,133],"determine":[8],"whether":[9],"specific":[10],"data":[11],"points":[12],"were":[13],"part":[14],"of":[15,53,83,99,106,131],"a":[16,44,67,96],"model's":[17],"training":[18],"dataset,":[19,127],"have":[20],"emerged":[21],"as":[22,152],"an":[23,153],"important":[24],"framework":[25,69],"understand,":[27],"assess,":[28],"and":[29,81,126,155,160],"quantify":[30],"the":[31,79,104,144],"potential":[32,58,100],"information":[33],"leakage":[34],"associated":[35],"with":[36,163],"machine":[37],"learning":[38],"systems.":[39],"Designing":[40],"effective":[41,154],"MIAs":[42,117,162],"is":[43],"challenging":[45],"task":[46],"that":[47,70,118,147],"usually":[48],"requires":[49],"extensive":[50],"manual":[51],"exploration":[52],"model":[54,74,125],"behaviors":[55],"identify":[57],"vulnerabilities.":[59],"In":[60],"this":[61],"paper,":[62],"we":[63,92],"introduce":[64],"AutoMIA":[65,112],"--":[66],"novel":[68,107],"leverages":[71],"large":[72],"language":[73],"(LLM)":[75],"agents":[76,149],"automate":[78],"design":[80],"implementation":[82],"new":[84,116,168],"MIA":[85],"signal":[86],"computations.":[87],"By":[88],"utilizing":[89],"LLM":[90,148],"agents,":[91],"can":[93,113,150],"systematically":[94],"explore":[95],"vast":[97],"space":[98],"attack":[101],"strategies,":[102],"enabling":[103],"discovery":[105],"strategies.":[108],"Our":[109],"experiments":[110],"demonstrate":[111],"successfully":[114],"discover":[115],"are":[119],"specifically":[120],"tailored":[121],"user-configured":[123],"target":[124],"resulting":[128],"in":[129,135],"improvements":[130],"up":[132,167],"0.18":[134],"absolute":[136],"AUC":[137],"over":[138],"existing":[139],"MIAs.":[140],"This":[141],"work":[142],"provides":[143],"first":[145],"demonstration":[146],"serve":[151],"scalable":[156],"paradigm":[157],"for":[158,170],"designing":[159],"implementing":[161],"SOTA":[164],"performance,":[165],"opening":[166],"avenues":[169],"future":[171],"exploration.":[172]},"counts_by_year":[],"updated_date":"2026-03-24T06:04:31.470712","created_date":"2026-03-24T00:00:00"}