{"id":"https://openalex.org/W7162755632","doi":"https://doi.org/10.48550/arxiv.2605.29396","title":"Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization","display_name":"Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162755632","doi":"https://doi.org/10.48550/arxiv.2605.29396"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29396","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.2605.29396","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137346870","display_name":"Zhihao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137325184","display_name":"Yifan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137334838","display_name":"Jian Lou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lou, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137382059","display_name":"Di Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104222101","display_name":"Yuxi Zhou","orcid":"https://orcid.org/0009-0001-5641-235X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yuxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005443916","display_name":"Yuke Hu","orcid":"https://orcid.org/0000-0001-5780-6898"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yuke","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":0.8219000101089478,"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":0.8219000101089478,"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/T13295","display_name":"Safety Systems Engineering in Autonomy","score":0.0348999984562397,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.02850000001490116,"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/robustness","display_name":"Robustness (evolution)","score":0.8873999714851379},{"id":"https://openalex.org/keywords/robustness-testing","display_name":"Robustness testing","score":0.5758000016212463},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3643999993801117},{"id":"https://openalex.org/keywords/unexpected-events","display_name":"Unexpected events","score":0.27959999442100525}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8873999714851379},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312999963760376},{"id":"https://openalex.org/C137726913","wikidata":"https://www.wikidata.org/wiki/Q7353550","display_name":"Robustness testing","level":3,"score":0.5758000016212463},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4108999967575073},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3529999852180481},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3003999888896942},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.29429998993873596},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.27790001034736633},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.25279998779296875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29396","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.2605.29396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29396","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Safety":[0],"alignment":[1,23,58,86,110,128],"for":[2],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"aims":[7],"to":[8,48,80,99,134,171,180],"reduce":[9],"harmful":[10],"or":[11,37],"unsafe":[12],"behavior":[13],"while":[14,153],"preserving":[15,154],"general":[16],"utility.":[17],"However,":[18],"recent":[19],"findings":[20],"reveal":[21],"that":[22,122,143],"effects":[24],"can":[25,39,150],"be":[26],"fragile:":[27],"lightweight":[28],"post-alignment":[29],"manipulations,":[30],"such":[31],"as":[32],"parameter":[33,62],"noise,":[34,36],"activation":[35],"quantization,":[38],"easily":[40],"weaken":[41],"the":[42,65,68,78,82,88,91,160,177],"intended":[43],"safety":[44,85,109,127,155],"behavior.":[45],"Prior":[46],"efforts":[47],"improve":[49,135,159],"robustness":[50,83,152,174],"have":[51],"primarily":[52],"focused":[53],"on":[54,114,183],"data":[55],"curation,":[56],"modified":[57],"objectives,":[59],"and":[60,129,139],"safety-critical":[61],"identification,":[63],"leaving":[64],"role":[66],"of":[67,84,90,162],"optimizer":[69],"itself":[70],"largely":[71],"unexplored.":[72],"In":[73],"this":[74,115],"paper,":[75],"we":[76,117,141],"are":[77],"first":[79,123],"study":[81],"from":[87],"perspective":[89],"base":[92],"optimizer.":[93],"This":[94],"optimizer-centric":[95],"view":[96],"naturally":[97],"points":[98],"zeroth-order":[100,132,147,163],"optimization,":[101],"which":[102],"provides":[103],"a":[104,119,145],"robustness-oriented":[105],"signal":[106],"by":[107,165],"evaluating":[108],"under":[111],"perturbations.":[112],"Based":[113],"insight,":[116],"propose":[118],"hybrid":[120],"framework":[121],"performs":[124],"standard":[125],"first-order":[126],"then":[130],"applies":[131],"refinement":[133,148,164,178],"robustness.":[136],"Both":[137],"theoretically":[138],"empirically,":[140],"show":[142],"only":[144],"few":[146],"steps":[149],"enhance":[151],"alignment.":[156],"We":[157],"further":[158],"efficiency":[161],"exploiting":[166],"its":[167],"inherent":[168],"perturbation-based":[169],"evaluations":[170],"estimate":[172],"layer-wise":[173],"sensitivity,":[175],"enabling":[176],"process":[179],"concentrate":[181],"updates":[182],"robustness-critical":[184],"layers":[185],"with":[186],"modest":[187],"training":[188],"overhead.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}