{"id":"https://openalex.org/W7149160476","doi":"https://doi.org/10.48550/arxiv.2604.01904","title":"Combating Data Laundering in LLM Training","display_name":"Combating Data Laundering in LLM Training","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7149160476","doi":"https://doi.org/10.48550/arxiv.2604.01904"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01904","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01904","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.01904","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100598636","display_name":"Muxing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Muxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132837522","display_name":"Zesheng Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Zesheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132894731","display_name":"Sharon Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Sharon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132857988","display_name":"Feng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11986","display_name":"Scientific Computing and Data Management","score":0.18019999563694,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.18019999563694,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.04830000177025795,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.033799998462200165,"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/process","display_name":"Process (computing)","score":0.6536999940872192},{"id":"https://openalex.org/keywords/countermeasure","display_name":"Countermeasure","score":0.6085000038146973},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5343000292778015},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4864000082015991},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.44589999318122864},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4415999948978424},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4339999854564667},{"id":"https://openalex.org/keywords/data-transformation","display_name":"Data transformation","score":0.4302999973297119},{"id":"https://openalex.org/keywords/data-verification","display_name":"Data verification","score":0.4025000035762787}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7254999876022339},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6536999940872192},{"id":"https://openalex.org/C21593369","wikidata":"https://www.wikidata.org/wiki/Q1032176","display_name":"Countermeasure","level":2,"score":0.6085000038146973},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5008999705314636},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4415999948978424},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C150670458","wikidata":"https://www.wikidata.org/wiki/Q4272815","display_name":"Data transformation","level":3,"score":0.4302999973297119},{"id":"https://openalex.org/C49895821","wikidata":"https://www.wikidata.org/wiki/Q5227368","display_name":"Data verification","level":2,"score":0.4025000035762787},{"id":"https://openalex.org/C42199009","wikidata":"https://www.wikidata.org/wiki/Q1172378","display_name":"Data cleansing","level":4,"score":0.3977999985218048},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39570000767707825},{"id":"https://openalex.org/C2780005421","wikidata":"https://www.wikidata.org/wiki/Q151900","display_name":"Money laundering","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C165609540","wikidata":"https://www.wikidata.org/wiki/Q1172486","display_name":"Data breach","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C184356942","wikidata":"https://www.wikidata.org/wiki/Q830382","display_name":"Best practice","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C69360830","wikidata":"https://www.wikidata.org/wiki/Q1172237","display_name":"Data Protection Act 1998","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01904","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01904","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.01904","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01904","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8228819370269775,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Post-hoc":[0],"unauthorized-training":[1],"data":[2,23,54,72,240],"detection":[3,33,110,210,232],"for":[4],"large":[5],"language":[6],"models":[7],"(LLMs)":[8],"typically":[9],"assumes":[10],"a":[11,17,115,143,177,180,235],"query-with-originals":[12],"regime:":[13],"rights":[14],"holders":[15],"query":[16,120],"target":[18,58,124,222],"LLM":[19,59,150,223],"with":[20,111],"raw":[21,112],"proprietary":[22,71,113],"and":[24,119,187,200,221,227],"assess":[25],"whether":[26],"the":[27,57,81,91,94,123,127,136,170,196,214],"model":[28],"assigns":[29],"them":[30],"stronger":[31,208],"memorization-based":[32],"signals,":[34,233],"e.g.,":[35,184,190],"higher":[36],"confidence":[37],"or":[38,66],"lower":[39],"loss,":[40],"than":[41],"held-out":[42,116],"non-training":[43],"reference":[44,117],"texts.":[45],"We":[46,103],"show":[47],"that":[48,98,151],"this":[49,105,159],"regime":[50],"becomes":[51],"brittle":[52],"under":[53],"laundering,":[55],"where":[56],"is":[60,130,139],"trained":[61],"on":[62,90,213],"semantics-preserving":[63],"but":[64],"stylistically":[65],"structurally":[67],"transformed":[68],"surrogates":[69],"of":[70,135,173],"to":[73,122],"obfuscate":[74],"provenance.":[75],"Since":[76,132],"training-time":[77],"exposure":[78],"occurs":[79],"in":[80],"laundered":[82,137],"form,":[83],"memorization":[84],"signals":[85],"may":[86],"no":[87],"longer":[88],"appear":[89],"originals,":[92],"collapsing":[93],"candidate-reference":[95],"signal":[96],"separation":[97],"standard":[99],"detectors":[100],"rely":[101],"on.":[102],"counter":[104],"threat":[106],"by":[107],"studying":[108],"laundering-aware":[109],"data,":[114],"corpus,":[118],"access":[121],"LLM,":[125],"while":[126],"laundering":[128,219],"transformation":[129,182],"undisclosed.":[131],"exact":[133],"recovery":[134],"corpus":[138],"infeasible,":[140],"we":[141,162],"infer":[142],"detection-useful":[144],"synthesis":[145],"process":[146],"via":[147],"an":[148],"auxiliary":[149],"maps":[152],"originals":[153],"into":[154],"training-like":[155],"queries.":[156],"To":[157],"make":[158],"search":[160],"tractable,":[161],"introduce":[163],"Synthesis":[164],"Data":[165],"Reversion":[166],"(SDR),":[167],"which":[168],"constrains":[169],"unbounded":[171],"space":[172],"natural-language":[174],"transformations":[175],"through":[176],"goal-details":[178],"abstraction:":[179],"high-level":[181],"goal,":[183],"\"lyrical":[185],"rewriting\",":[186],"fine-grained":[188],"details,":[189],"\"with":[191],"vivid":[192],"imagery\".":[193],"SDR":[194,229],"identifies":[195],"most":[197],"likely":[198],"goal":[199],"iteratively":[201],"refines":[202],"details":[203],"so":[204],"synthesized":[205],"queries":[206],"elicit":[207],"target-model":[209],"signals.":[211],"Evaluated":[212],"MIMIR":[215],"benchmark":[216],"against":[217,239],"diverse":[218],"practices":[220],"families":[224],"(Pythia,":[225],"Llama2,":[226],"Falcon),":[228],"consistently":[230],"restores":[231],"offering":[234],"practical":[236],"auditing":[237],"layer":[238],"laundering.":[241]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-04T00:00:00"}