{"id":"https://openalex.org/W4412877189","doi":"https://doi.org/10.1145/3711896.3736573","title":"A Survey on Model Extraction Attacks and Defenses for Large Language Models","display_name":"A Survey on Model Extraction Attacks and Defenses for Large Language Models","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877189","doi":"https://doi.org/10.1145/3711896.3736573"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3736573","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736573","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736573","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736573","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112128593","display_name":"Kaixiang Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kaixiang Zhao","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"raw_orcid":"https://orcid.org/0009-0005-8174-0581","affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005362290","display_name":"Lincan Li","orcid":"https://orcid.org/0000-0003-3797-4055"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lincan Li","raw_affiliation_strings":["Florida State University, Tallahassee, FL, USA"],"raw_orcid":"https://orcid.org/0000-0003-3797-4055","affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044455276","display_name":"Kaize Ding","orcid":"https://orcid.org/0000-0001-6684-6752"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaize Ding","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"raw_orcid":"https://orcid.org/0000-0001-6684-6752","affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009102659","display_name":"Neil Zhenqiang Gong","orcid":"https://orcid.org/0000-0002-9900-9309"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neil Zhenqiang Gong","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"raw_orcid":"https://orcid.org/0000-0002-9900-9309","affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057711796","display_name":"Yue Zhao","orcid":"https://orcid.org/0000-0003-3401-4921"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Zhao","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-3401-4921","affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047581320","display_name":"Yushun Dong","orcid":"https://orcid.org/0000-0001-7504-6159"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yushun Dong","raw_affiliation_strings":["Florida State University, Tallahassee, FL, USA"],"raw_orcid":"https://orcid.org/0000-0001-7504-6159","affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5112128593"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":9.2517,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.97614121,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6227","last_page":"6236"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9962999820709229,"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":0.9962999820709229,"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":0.9897000193595886,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9735000133514404,"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/computer-science","display_name":"Computer science","score":0.6558560132980347},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4447246789932251},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4370889365673065},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4196300804615021},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33520442247390747}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6558560132980347},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4447246789932251},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4370889365673065},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4196300804615021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33520442247390747},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3736573","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736573","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736573","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3736573","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736573","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736573","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877189.pdf","grobid_xml":"https://content.openalex.org/works/W4412877189.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W2294710185","https://openalex.org/W2770885069","https://openalex.org/W2963465081","https://openalex.org/W2984103614","https://openalex.org/W3123486603","https://openalex.org/W3213793813","https://openalex.org/W4285224048","https://openalex.org/W4296567394","https://openalex.org/W4308391439","https://openalex.org/W4383473937","https://openalex.org/W4385571011","https://openalex.org/W4385573569","https://openalex.org/W4389364443","https://openalex.org/W4392353733","https://openalex.org/W4394745382","https://openalex.org/W4399534449","https://openalex.org/W4402169037","https://openalex.org/W4402442681","https://openalex.org/W4402628259","https://openalex.org/W4403588805","https://openalex.org/W4403792250","https://openalex.org/W4404782149","https://openalex.org/W4404792952","https://openalex.org/W4405181744","https://openalex.org/W4406302454","https://openalex.org/W4406458681","https://openalex.org/W4406859873","https://openalex.org/W4411985537","https://openalex.org/W4412130408","https://openalex.org/W4412945265","https://openalex.org/W6600062588","https://openalex.org/W6600577311","https://openalex.org/W6601548533","https://openalex.org/W6603143895","https://openalex.org/W6604801084","https://openalex.org/W6853601813","https://openalex.org/W6910785893"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Model":[0],"extraction":[1,26],"attacks":[2,27,31],"pose":[3],"significant":[4],"security":[5,129,141],"threats":[6],"to":[7,144],"deployed":[8],"language":[9,102,146],"models,":[10],"potentially":[11],"compromising":[12],"intellectual":[13],"property":[14],"and":[15,28,38,54,74,93,114,123,140],"user":[16],"privacy.":[17],"This":[18,133],"survey":[19],"provides":[20],"a":[21],"comprehensive":[22],"taxonomy":[23],"of":[24,100],"LLM-specific":[25],"defenses,":[29],"categorizing":[30],"into":[32,68],"functionality":[33],"extraction,":[34,37],"training":[35],"data":[36,71],"prompt-targeted":[39,75],"attacks.":[40],"We":[41,62,84],"analyze":[42],"various":[43],"attack":[44,91,121],"methodologies":[45,122],"including":[46,119],"API-based":[47],"knowledge":[48],"distillation,":[49],"direct":[50],"querying,":[51],"parameter":[52],"recovery,":[53],"prompt":[55],"stealing":[56],"techniques":[57],"that":[58,127],"exploit":[59],"transformer":[60],"architectures.":[61],"then":[63],"examine":[64],"defense":[65,94,125],"mechanisms":[66,126],"organized":[67],"model":[69,131],"protection,":[70,73],"privacy":[72],"strategies,":[76],"evaluating":[77,89],"their":[78],"effectiveness":[79,92],"across":[80],"different":[81],"deployment":[82],"scenarios.":[83],"propose":[85,115],"specialized":[86],"metrics":[87],"for":[88],"both":[90],"performance,":[95],"addressing":[96],"the":[97],"specific":[98],"challenges":[99],"generative":[101],"models.":[103],"Through":[104],"our":[105],"analysis,":[106],"we":[107],"identify":[108],"critical":[109],"limitations":[110],"in":[111,148],"current":[112],"approaches":[113],"promising":[116],"research":[117],"directions,":[118],"integrated":[120],"adaptive":[124],"balance":[128],"with":[130],"utility.":[132],"work":[134],"serves":[135],"NLP":[136],"researchers,":[137],"ML":[138],"engineers,":[139],"professionals":[142],"seeking":[143],"protect":[145],"models":[147],"production":[149],"environments.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":5}],"updated_date":"2026-05-29T09:21:14.243279","created_date":"2025-10-10T00:00:00"}
