{"id":"https://openalex.org/W7152464280","doi":"https://doi.org/10.1145/3774904.3792502","title":"Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs","display_name":"Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152464280","doi":"https://doi.org/10.1145/3774904.3792502"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792502","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792502","pdf_url":null,"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 ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792502","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zihui Chen","orcid":"https://orcid.org/0009-0004-0881-3441"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihui Chen","raw_affiliation_strings":["School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0004-0881-3441","affiliations":[{"raw_affiliation_string":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102791006","display_name":"Yuling Wang","orcid":"https://orcid.org/0000-0002-5804-9742"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuling Wang","raw_affiliation_strings":["School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5804-9742","affiliations":[{"raw_affiliation_string":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005761664","display_name":"Pengfei Jiao","orcid":"https://orcid.org/0000-0003-1049-1002"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Jiao","raw_affiliation_strings":["School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1049-1002","affiliations":[{"raw_affiliation_string":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kai Wu","orcid":"https://orcid.org/0009-0009-7974-4638"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Wu","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0009-7974-4638","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0002-4444-7811"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4444-7811","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068007462","display_name":"Xiang Ao","orcid":"https://orcid.org/0000-0001-9633-8361"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Ao","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9633-8361","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129658226","display_name":"Dalin Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dalin Zhang","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5869-6544","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.51687775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1217","last_page":"1228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8116999864578247,"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":0.8116999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.08449999988079071,"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":0.031199999153614044,"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/adversarial-system","display_name":"Adversarial system","score":0.6980000138282776},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3928999900817871},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3215000033378601},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.25130000710487366}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6980000138282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5652999877929688},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5523999929428101},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3174000084400177},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.30309998989105225},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28209999203681946},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.2126999944448471}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792502","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792502","pdf_url":null,"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 ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792502","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792502","pdf_url":null,"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 ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2162630660","https://openalex.org/W2803831897","https://openalex.org/W2949208225","https://openalex.org/W2984488829","https://openalex.org/W3114904768","https://openalex.org/W3167334189","https://openalex.org/W3170068709","https://openalex.org/W4312442707","https://openalex.org/W4321452549","https://openalex.org/W4386066309","https://openalex.org/W4387847387","https://openalex.org/W4392384460","https://openalex.org/W4393277515","https://openalex.org/W4396722619","https://openalex.org/W4400392053","https://openalex.org/W4401857609","https://openalex.org/W4409362846","https://openalex.org/W4409364811","https://openalex.org/W4409657360","https://openalex.org/W4409985741","https://openalex.org/W4410089540","https://openalex.org/W4416034797"],"related_works":[],"abstract_inverted_index":{"Text-attributed":[0],"graphs":[1],"(TAGs)":[2],"enhance":[3],"graph":[4,26,39,96,135,156],"learning":[5,27],"by":[6],"integrating":[7],"rich":[8],"textual":[9,53,144],"semantics":[10],"and":[11,43,52,92,94,143,176,181,193],"topological":[12],"context":[13],"for":[14],"each":[15],"node.":[16],"While":[17],"boosting":[18],"expressiveness,":[19],"they":[20],"also":[21],"expose":[22],"new":[23],"vulnerabilities":[24],"in":[25,55,88],"through":[28],"text-based":[29],"adversarial":[30,68],"surfaces.":[31],"Recent":[32],"advances":[33],"leverage":[34],"diverse":[35],"backbones,":[36],"such":[37],"as":[38],"neural":[40],"networks":[41],"(GNNs)":[42],"pre-trained":[44],"language":[45,129],"models":[46],"(PLMs),":[47],"to":[48,74,112,137,158,186],"capture":[49],"both":[50,140],"structural":[51],"information":[54],"TAGs.":[56],"This":[57],"diversity":[58],"raises":[59],"a":[60,121,149,187],"key":[61],"question:":[62],"How":[63],"can":[64],"we":[65,118,147],"design":[66,148],"universal":[67,175],"attacks":[69,111,178],"that":[70,102,125,154,172],"generalize":[71],"across":[72,179],"architectures":[73],"assess":[75],"the":[76,85,100],"security":[77],"of":[78,133],"TAG":[79],"models?":[80],"The":[81],"challenge":[82],"arises":[83],"from":[84],"stark":[86],"contrast":[87],"how":[89],"different":[90],"backbones\u2014GNNs":[91],"PLMs\u2014perceive":[93],"encode":[95],"patterns,":[97],"coupled":[98],"with":[99,184],"fact":[101],"many":[103],"PLMs":[104],"are":[105],"only":[106],"accessible":[107],"via":[108],"APIs,":[109],"limiting":[110],"black-box":[113],"settings.":[114],"To":[115],"address":[116],"this,":[117],"propose":[119],"BadGraph,":[120],"novel":[122],"attack":[123,162],"framework":[124],"deeply":[126],"elicits":[127],"large":[128],"models'":[130],"(LLMs)":[131],"understanding":[132],"general":[134],"knowledge":[136],"jointly":[138],"perturb":[139],"node":[141],"topology":[142],"semantics.":[145],"Specifically,":[146],"target":[150],"influencer":[151],"retrieval":[152],"module":[153],"leverages":[155],"priors":[157],"construct":[159],"cross-modally":[160],"aligned":[161],"shortcuts,":[163],"thereby":[164],"enabling":[165],"efficient":[166],"LLM-based":[167,182],"perturbation":[168],"reasoning.":[169],"Experiments":[170],"show":[171],"BadGraph":[173],"achieves":[174],"effective":[177],"GNN-":[180],"reasoners,":[183],"up":[185],"76.3%":[188],"performance":[189],"drop,":[190],"while":[191],"theoretical":[192],"empirical":[194],"analyses":[195],"confirm":[196],"its":[197],"stealthy":[198],"yet":[199],"interpretable":[200],"nature.":[201]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-10T00:00:00"}