{"id":"https://openalex.org/W7163409272","doi":"https://doi.org/10.48550/arxiv.2606.02947","title":"BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks","display_name":"BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163409272","doi":"https://doi.org/10.48550/arxiv.2606.02947"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02947","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02947","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":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.2606.02947","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024138013","display_name":"Ivan Saboli\u0107","orcid":"https://orcid.org/0000-0002-2587-9109"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saboli\u0107, Ivan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137765232","display_name":"Marin Or\u0161i\u0107","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Or\u0161i\u0107, Marin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137789407","display_name":"Josip \u0160ari\u0107","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u0160ari\u0107, Josip","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047540566","display_name":"Sven Lon\u010dari\u0107","orcid":"https://orcid.org/0000-0002-4857-5351"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lon\u010dari\u0107, Sven","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9207000136375427,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9207000136375427,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.01940000057220459,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.014000000432133675,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9884999990463257},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6671000123023987},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5676000118255615},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4595000147819519}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.9884999990463257},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6671000123023987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5837000012397766},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5676000118255615},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4595000147819519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38019999861717224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.25769999623298645},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2409999966621399},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.23510000109672546}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02947","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02947","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.02947","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02947","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Supervised":[0],"fine-tuning":[1,44],"is":[2,21],"the":[3,48,59,81,85,98,101,105,111,152],"predominant":[4],"approach":[5],"for":[6],"adapting":[7],"autoregressive":[8],"vision-language":[9],"models":[10],"to":[11,24,97,120,150],"downstream":[12],"tasks.":[13],"Recent":[14],"work":[15],"has":[16],"shown":[17],"that":[18,28,50,141],"this":[19],"paradigm":[20],"highly":[22],"vulnerable":[23],"backdoor":[25,121],"attacks,":[26],"and":[27,63,72,89,134],"existing":[29],"defenses":[30],"are":[31,54],"ineffective":[32],"in":[33],"open-ended":[34],"generation":[35],"settings.":[36],"In":[37],"response,":[38],"we":[39,139],"propose":[40],"BYORn,":[41],"a":[42,64,128],"backdoor-robust":[43],"framework":[45],"motivated":[46],"by":[47,80],"observation":[49],"poisoned":[51],"target":[52,90],"responses":[53,71,78],"often":[55],"semantically":[56],"implausible":[57],"given":[58],"corresponding":[60],"image-text":[61],"inputs":[62],"pretrained":[65],"model.":[66],"BYORn":[67,116,142],"identifies":[68],"such":[69],"misaligned":[70],"dynamically":[73],"replaces":[74],"them":[75],"with":[76],"alternative":[77],"generated":[79],"model,":[82],"thereby":[83],"breaking":[84],"correlation":[86],"between":[87,132],"triggers":[88],"outputs.":[91],"The":[92],"resulting":[93],"objective":[94],"gradient":[95,99],"corresponds":[96],"of":[100,104],"empirical":[102],"estimate":[103],"population":[106],"risk":[107],"upper":[108],"bound":[109],"over":[110],"clean":[112],"data":[113],"distribution.":[114],"Empirically,":[115],"consistently":[117],"improves":[118],"robustness":[119],"attacks":[122,147],"while":[123],"preserving":[124],"clean-task":[125],"performance,":[126],"establishing":[127],"new":[129],"trade-off":[130],"frontier":[131],"generalization":[133],"attack":[135],"success":[136],"rate.":[137],"Finally,":[138],"demonstrate":[140],"remains":[143],"effective":[144],"against":[145],"adaptive":[146],"specifically":[148],"designed":[149],"circumvent":[151],"proposed":[153],"defense.":[154]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}
