{"id":"https://openalex.org/W7162013498","doi":"https://doi.org/10.48550/arxiv.2605.20759","title":"Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders","display_name":"Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162013498","doi":"https://doi.org/10.48550/arxiv.2605.20759"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20759","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.2605.20759","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136637466","display_name":"Laura Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Laura","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136670776","display_name":"Reza Ryan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryan, Reza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136685114","display_name":"Qian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087546771","display_name":"Nasim Ferdosian","orcid":"https://orcid.org/0000-0003-2014-1831"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferdosian, Nasim","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.4440999925136566,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.4440999925136566,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.30000001192092896,"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/T11644","display_name":"Spam and Phishing Detection","score":0.044599998742341995,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/false-positive-paradox","display_name":"False positive paradox","score":0.6690999865531921},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.492900013923645},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4828000068664551},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43939998745918274}],"concepts":[{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.6690999865531921},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6284000277519226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122000217437744},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.492900013923645},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.4106999933719635},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2791999876499176}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20759","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.2605.20759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20759","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":[{"id":"https://metadata.un.org/sdg/16","score":0.6679843664169312,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Single-turn":[0],"safety":[1,189],"evaluation":[2],"is":[3,145,155,167],"a":[4,31,41],"poor":[5],"proxy":[6],"for":[7,183],"real":[8],"fraud":[9,20,63,87,119,172],"defense,":[10],"where":[11],"attackers":[12],"escalate":[13],"across":[14,92],"multiple":[15],"rounds.":[16],"This":[17],"paper":[18],"evaluates":[19],"defenders":[21,49],"under":[22,58],"replay":[23,60],"and":[24,28,61,88,134,137,150,169,191],"adaptive":[25,62],"multi-round":[26,43],"attacks":[27],"measures":[29],"when":[30],"defender":[32,105],"refuses,":[33],"not":[34,156],"just":[35],"whether":[36],"it":[37],"eventually":[38],"refuses.":[39],"On":[40],"frozen":[42],"suite":[44],"built":[45],"from":[46,120],"Fraud-R1,":[47],"graph-context":[48],"improve":[50],"early":[51],"safe":[52],"refusal":[53,163,179],"relative":[54],"to":[55,102,112,127,139,196,201],"text-only":[56],"baselines":[57],"both":[59,86],"pressure,":[64],"but":[65,154],"they":[66],"also":[67],"produce":[68],"substantially":[69],"more":[70],"benign":[71,89,184],"over-refusal.":[72],"Direct":[73],"probing":[74],"of":[75,130],"the":[76,96,104,115,123,128,161,197,203],"trained":[77],"graph":[78,132,143,198],"encoder,":[79],"together":[80],"with":[81],"paired":[82],"shuffle-risk":[83],"ablations":[84],"on":[85,95,160],"sides":[90],"replicated":[91],"two":[93],"seeds":[94],"Qwen-1.5B":[97],"backbone,":[98],"localises":[99],"this":[100],"cost":[101],"how":[103,202],"LLM":[106,124,204],"consumes":[107,205],"structured":[108,131],"context":[109,144],"rather":[110],"than":[111,148],"graph-encoder":[113],"quality:":[114],"encoder":[116],"cleanly":[117],"separates":[118],"benign,":[121],"while":[122],"responds":[125],"primarily":[126],"presence":[129],"fields":[133],"only":[135],"secondarily,":[136],"asymmetrically,":[138],"risk-score":[140],"magnitude.":[141],"Temporal":[142],"directionally":[146],"stronger":[147],"static":[149],"significantly":[151],"better":[152],"grounded,":[153],"yet":[157],"conclusively":[158],"superior":[159],"main":[162],"metrics.":[164],"The":[165],"contribution":[166],"evaluative":[168],"measurement-oriented:":[170],"robust":[171],"assessment":[173],"must":[174,177,181,192],"be":[175],"multi-round,":[176],"report":[178],"timing,":[180],"account":[182],"false":[185],"positives":[186],"alongside":[187],"fraud-side":[188],"gains,":[190],"localize":[193],"observed":[194],"costs":[195],"signal":[199],"or":[200],"it.":[206]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}