{"id":"https://openalex.org/W4401864009","doi":"https://doi.org/10.1145/3637528.3671956","title":"Cross-Context Backdoor Attacks against Graph Prompt Learning","display_name":"Cross-Context Backdoor Attacks against Graph Prompt Learning","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401864009","doi":"https://doi.org/10.1145/3637528.3671956"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671956","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://inria.hal.science/hal-04827883","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075844359","display_name":"Xiaoting Lyu","orcid":"https://orcid.org/0000-0003-0732-0261"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoting Lyu","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001937964","display_name":"Yufei Han","orcid":"https://orcid.org/0000-0002-9035-6718"},"institutions":[{"id":"https://openalex.org/I2802519937","display_name":"Institut de Recherche en Informatique et Syst\u00e8mes Al\u00e9atoires","ror":"https://ror.org/00myn0z94","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I205703379","https://openalex.org/I2802204017","https://openalex.org/I2802519937","https://openalex.org/I28221208","https://openalex.org/I4210127572","https://openalex.org/I4210159245","https://openalex.org/I56067802"]},{"id":"https://openalex.org/I56067802","display_name":"Universit\u00e9 de Rennes","ror":"https://ror.org/015m7wh34","country_code":"FR","type":"education","lineage":["https://openalex.org/I56067802"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yufei Han","raw_affiliation_strings":["Inria, Univ. Rennes, IRISA, Rennes, France"],"affiliations":[{"raw_affiliation_string":"Inria, Univ. Rennes, IRISA, Rennes, France","institution_ids":["https://openalex.org/I2802519937","https://openalex.org/I56067802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777576","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-5974-1589"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]},{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Beijing Jiaotong University & Xi'an Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University & Xi'an Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070","https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052221167","display_name":"Hangwei Qian","orcid":"https://orcid.org/0000-0003-4831-0748"},"institutions":[{"id":"https://openalex.org/I4210161496","display_name":"A*STAR Graduate Academy","ror":"https://ror.org/059yjzn93","country_code":"SG","type":"education","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210161496"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Hangwei Qian","raw_affiliation_strings":["CFAR, A*STAR, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"CFAR, A*STAR, Singapore, Singapore","institution_ids":["https://openalex.org/I4210161496"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021751767","display_name":"Ivor W. Tsang","orcid":"https://orcid.org/0000-0001-8095-4637"},"institutions":[{"id":"https://openalex.org/I4210161496","display_name":"A*STAR Graduate Academy","ror":"https://ror.org/059yjzn93","country_code":"SG","type":"education","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210161496"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ivor Tsang","raw_affiliation_strings":["CFAR, A*STAR, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"CFAR, A*STAR, Singapore, Singapore","institution_ids":["https://openalex.org/I4210161496"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000755750","display_name":"Xiangliang Zhang","orcid":"https://orcid.org/0000-0002-3574-5665"},"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":false,"raw_author_name":"Xiangliang Zhang","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5075844359"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":3.637,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.93782622,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2094","last_page":"2105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9945999979972839,"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":0.9945999979972839,"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":0.9768000245094299,"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":0.9671000242233276,"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/backdoor","display_name":"Backdoor","score":0.982680082321167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6994789838790894},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5187892317771912},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45220279693603516},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.43870440125465393},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4210861027240753},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3414658308029175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3268696069717407},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3172991871833801}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.982680082321167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6994789838790894},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5187892317771912},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45220279693603516},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.43870440125465393},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4210861027240753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3414658308029175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3268696069717407},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3172991871833801},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3637528.3671956","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04827883v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-04827883","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"KDD 2024 : 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2024, Barcelona, Spain. pp.2094-2105, ⟨10.1145/3637528.3671956⟩","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04827883v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-04827883","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"KDD 2024 : 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2024, Barcelona, Spain. pp.2094-2105, ⟨10.1145/3637528.3671956⟩","raw_type":"Conference papers"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2099438806","https://openalex.org/W2914304175","https://openalex.org/W2964971928","https://openalex.org/W2998099211","https://openalex.org/W3167334189","https://openalex.org/W3173777717","https://openalex.org/W3185341429","https://openalex.org/W4206352019","https://openalex.org/W4247950230","https://openalex.org/W4308410017","https://openalex.org/W4323066547","https://openalex.org/W4367046771","https://openalex.org/W4382239704","https://openalex.org/W4383468961","https://openalex.org/W4385570691","https://openalex.org/W4386907188","https://openalex.org/W4387947060","https://openalex.org/W4387995379","https://openalex.org/W6600050674","https://openalex.org/W6601517772","https://openalex.org/W6605486432","https://openalex.org/W6607888529"],"related_works":["https://openalex.org/W4320031223","https://openalex.org/W4200629851","https://openalex.org/W4281902577","https://openalex.org/W4309417370","https://openalex.org/W4292107232","https://openalex.org/W3009072493","https://openalex.org/W4386080799","https://openalex.org/W3140988292","https://openalex.org/W4317672133","https://openalex.org/W4386185023"],"abstract_inverted_index":{"Graph":[0],"Prompt":[1],"Learning":[2],"(GPL)":[3],"bridges":[4],"significant":[5],"disparities":[6],"between":[7],"pretraining":[8,75],"and":[9,31,88,122,128,159],"downstream":[10,81,108,147],"applications":[11,148],"to":[12,58,107,166,176],"alleviate":[13],"the":[14,34,64,74,101,144,172,183],"knowledge":[15,29,79],"transfer":[16,30,100],"bottleneck":[17],"in":[18,27,43,182],"real-world":[19],"graph":[20,28,129],"learning.":[21],"While":[22],"GPL":[23,115,177,186],"offers":[24],"superior":[25],"effectiveness":[26],"computational":[32],"efficiency,":[33],"security":[35],"risks":[36],"posed":[37],"by":[38],"backdoor":[39,59,67,102,174],"poisoning":[40],"effects":[41],"embedded":[42],"pretrained":[44,105],"models":[45],"remain":[46],"largely":[47],"unexplored.":[48],"Our":[49,83,169],"study":[50,170],"provides":[51],"a":[52],"comprehensive":[53],"analysis":[54],"of":[55,80,126,146,185],"GPL's":[56],"vulnerability":[57],"attacks.":[60],"We":[61,152],"introduce":[62],"CrossBA,":[63],"first":[65],"cross-context":[66,120],"attack":[68,139],"against":[69,157],"GPL,":[70],"which":[71],"manipulates":[72],"only":[73],"phase":[76],"without":[77],"requiring":[78],"applications.":[82],"investigation":[84],"reveals":[85],"both":[86],"theoretically":[87],"empirically":[89],"that":[90,134,161],"tuning":[91],"trigger":[92],"graphs,":[93],"combined":[94],"with":[95],"prompt":[96],"transformations,":[97],"can":[98],"seamlessly":[99],"threat":[103],"from":[104],"encoders":[106],"applications.Through":[109],"extensive":[110],"experiments":[111],"involving":[112],"3":[113],"representative":[114],"methods":[116],"across":[117],"5":[118,123],"distinct":[119],"scenarios":[121],"benchmark":[124],"datasets":[125],"node":[127],"classification":[130],"tasks,":[131],"we":[132],"demonstrate":[133],"CrossBA":[135,158],"consistently":[136],"achieves":[137],"high":[138],"success":[140],"rates":[141],"while":[142],"preserving":[143],"functionality":[145],"over":[149],"clean":[150],"input.":[151],"also":[153],"explore":[154],"potential":[155],"countermeasures":[156],"conclude":[160],"current":[162],"defenses":[163],"are":[164],"insufficient":[165],"mitigate":[167],"CrossBA.":[168],"highlights":[171],"persistent":[173],"threats":[175],"systems,":[178],"raising":[179],"trustworthiness":[180],"concerns":[181],"practices":[184],"techniques.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}