{"id":"https://openalex.org/W4416378108","doi":"https://doi.org/10.48550/arxiv.2511.12423","title":"GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs","display_name":"GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs","publication_year":2025,"publication_date":"2025-11-16","ids":{"openalex":"https://openalex.org/W4416378108","doi":"https://doi.org/10.48550/arxiv.2511.12423"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.12423","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.12423","pdf_url":"https://arxiv.org/pdf/2511.12423","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.12423","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101721460","display_name":"Jian Ma","orcid":"https://orcid.org/0000-0002-3713-3240"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ma, Jiaji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042391338","display_name":"Puja Trivedi","orcid":"https://orcid.org/0000-0003-1874-8992"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trivedi, Puja","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015996266","display_name":"Danai Koutra","orcid":"https://orcid.org/0000-0002-3206-8179"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koutra, Danai","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101721460"],"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":0.7573999762535095,"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":0.7573999762535095,"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":0.08240000158548355,"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":0.07959999889135361,"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":0.6040999889373779},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5831999778747559},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5388000011444092},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5214999914169312},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.32760000228881836},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.32100000977516174},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.30640000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8133000135421753},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6040999889373779},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5831999778747559},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5388000011444092},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5214999914169312},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4242999851703644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37439998984336853},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2851000130176544},{"id":"https://openalex.org/C2780741293","wikidata":"https://www.wikidata.org/wiki/Q4818019","display_name":"Attack patterns","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2533000111579895},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.12423","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.12423","pdf_url":"https://arxiv.org/pdf/2511.12423","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.12423","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.12423","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.12423","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.12423","pdf_url":"https://arxiv.org/pdf/2511.12423","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1357080765","display_name":"Collaborative Research: III: Medium: Empowering Graph Neural Networks from a Data Perspective","funder_award_id":"2504090","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4496747893","display_name":null,"funder_award_id":"IS 1845491","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5962262462","display_name":"Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications","funder_award_id":"2212143","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6595624021","display_name":null,"funder_award_id":"IIS 1845491","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7903790144","display_name":null,"funder_award_id":"1845491","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G895001607","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Text-attributed":[0],"graphs":[1],"(TAGs),":[2],"which":[3],"combine":[4],"structural":[5,57],"and":[6,31,37,67,134,163,183,192],"textual":[7],"node":[8,121],"information,":[9],"are":[10,54,62],"ubiquitous":[11],"across":[12],"many":[13,93],"domains.":[14],"Recent":[15],"work":[16],"integrates":[17],"Large":[18],"Language":[19],"Models":[20],"(LLMs)":[21],"with":[22,130,173],"Graph":[23],"Neural":[24],"Networks":[25],"(GNNs)":[26],"to":[27,56,64,136],"jointly":[28],"model":[29,138,145,149],"semantics":[30,135],"structure,":[32],"resulting":[33],"in":[34,89],"more":[35],"general":[36],"expressive":[38],"models":[39,197],"that":[40,82,178,199],"achieve":[41],"state-of-the-art":[42,194],"performance":[43],"on":[44,148,189],"TAG":[45],"benchmarks.":[46],"However,":[47],"this":[48],"integration":[49],"introduces":[50,167],"dual":[51],"vulnerabilities:":[52],"GNNs":[53],"sensitive":[55],"perturbations,":[58],"while":[59],"LLM-derived":[60],"features":[61],"vulnerable":[63],"prompt":[65],"injection":[66,122],"adversarial":[68,72],"phrasing.":[69],"While":[70],"existing":[71,94],"attacks":[73,84,95],"largely":[74],"perturb":[75],"structure":[76,133],"or":[77,103,151],"text":[78],"independently,":[79],"we":[80,113],"find":[81],"uni-modal":[83],"cause":[85],"only":[86],"modest":[87],"degradation":[88],"LLM-enhanced":[90,125,195],"GNNs.":[91,126],"Moreover,":[92],"assume":[96],"unrealistic":[97],"capabilities,":[98],"such":[99],"as":[100],"white-box":[101],"access":[102],"direct":[104],"modification":[105],"of":[106,161],"graph":[107,185],"data.":[108],"To":[109,154],"address":[110],"these":[111],"gaps,":[112],"propose":[114],"GRAPHTEXTACK,":[115],"the":[116,156],"first":[117],"black-box,":[118],"multi-modal{,":[119],"poisoning}":[120],"attack":[123],"for":[124],"GRAPHTEXTACK":[127,166,200],"injects":[128],"nodes":[129],"carefully":[131],"crafted":[132],"degrade":[137],"performance,":[139],"operating":[140],"under":[141],"a":[142,168,174],"realistic":[143],"threat":[144],"without":[146],"relying":[147],"internals":[150],"surrogate":[152],"models.":[153],"navigate":[155],"combinatorial,":[157],"non-differentiable":[158],"search":[159],"space":[160],"connectivity":[162],"feature":[164],"assignments,":[165],"novel":[169],"evolutionary":[170],"optimization":[171],"framework":[172],"multi-objective":[175],"fitness":[176],"function":[177],"balances":[179],"local":[180],"prediction":[181],"disruption":[182],"global":[184],"influence.":[186],"Extensive":[187],"experiments":[188],"five":[190],"datasets":[191],"two":[193],"GNN":[196],"show":[198],"significantly":[201],"outperforms":[202],"12":[203],"strong":[204],"baselines.":[205]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-11-19T00:00:00"}
