{"id":"https://openalex.org/W7134929191","doi":"https://doi.org/10.48550/arxiv.2603.09452","title":"CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research?","display_name":"CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research?","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134929191","doi":"https://doi.org/10.48550/arxiv.2603.09452"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09452","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09452","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.2603.09452","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128734424","display_name":"Xiangsen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiangsen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128702070","display_name":"Xuan Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128721243","display_name":"Shuo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Shuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128756181","display_name":"Matthieu Maitre","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maitre, Matthieu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128725085","display_name":"Sudipto Rakshit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rakshit, Sudipto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128711249","display_name":"Diana Duvieilh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duvieilh, Diana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128782764","display_name":"Ashley Picone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Picone, Ashley","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125228033","display_name":"Nan Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Nan","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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.18629999458789825,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.18629999458789825,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.07609999924898148,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12572","display_name":"Intelligence, Security, War Strategy","score":0.05119999870657921,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.8564000129699707},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6941999793052673},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5896999835968018},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.43540000915527344},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.35199999809265137},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3382999897003174}],"concepts":[{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.8564000129699707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627999782562256},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6941999793052673},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5896999835968018},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.531499981880188},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.43540000915527344},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.4065000116825104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4020000100135803},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31859999895095825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.302700012922287},{"id":"https://openalex.org/C151719136","wikidata":"https://www.wikidata.org/wiki/Q3972943","display_name":"Publishing","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C188220564","wikidata":"https://www.wikidata.org/wiki/Q3325097","display_name":"Workflow engine","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09452","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09452","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.2603.09452","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09452","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":[{"id":"https://metadata.un.org/sdg/16","score":0.5034952163696289,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Analyzing":[0],"Open":[1],"Source":[2],"Intelligence":[3],"(OSINT)":[4],"from":[5,116],"large":[6],"volumes":[7],"of":[8,53,72,121,164],"data":[9],"is":[10,114,218],"critical":[11],"for":[12,93,213],"drafting":[13],"and":[14,30,150,180,186,201,222],"publishing":[15],"comprehensive":[16],"CTI":[17,119,194],"reports.":[18],"This":[19,125],"process":[20],"usually":[21],"follows":[22],"a":[23,39,122],"three-stage":[24,104],"workflow":[25,120,195],"--":[26],"triage,":[27],"deep":[28],"search":[29],"TI":[31],"drafting.":[32],"While":[33],"Large":[34],"Language":[35],"Models":[36],"(LLMs)":[37],"offer":[38],"promising":[40],"route":[41],"toward":[42],"automation,":[43],"existing":[44,76],"benchmarks":[45,50,77],"still":[46],"have":[47],"limitations.":[48],"These":[49],"often":[51,78,170],"consist":[52],"tasks":[54,68,132],"that":[55,83,144],"do":[56],"not":[57],"reflect":[58],"real-world":[59],"analyst":[60],"workflows.":[61],"For":[62,167],"example,":[63,168],"human":[64,202,207],"analysts":[65],"rarely":[66],"receive":[67],"in":[69],"the":[70,102,117,162,172,193],"form":[71],"multiple-choice":[73],"questions.":[74],"Also,":[75],"rely":[79],"on":[80,130],"model-centric":[81],"metrics":[82,143],"emphasize":[84],"lexical":[85],"overlap":[86],"rather":[87],"than":[88],"actionable,":[89],"detailed":[90],"insights":[91,160],"essential":[92],"security":[94],"analysts.":[95],"Moreover,":[96],"they":[97],"typically":[98],"fail":[99],"to":[100,176,182,209],"cover":[101],"complete":[103],"workflow.":[105],"To":[106,189],"address":[107,190],"these":[108,191],"issues,":[109],"we":[110],"introduce":[111],"CyberThreat-Eval,":[112],"which":[113],"collected":[115],"daily":[118],"world-leading":[123],"company.":[124],"expert-annotated":[126],"benchmark":[127,157],"assesses":[128],"LLMs":[129,169],"practical":[131],"across":[133],"all":[134],"three":[135],"stages":[136],"as":[137],"mentioned":[138],"above.":[139],"It":[140],"utilizes":[141],"analyst-centric":[142],"measure":[145],"factual":[146],"accuracy,":[147],"content":[148],"quality,":[149],"operational":[151],"costs.":[152],"Our":[153],"evaluation":[154],"using":[155],"this":[156],"reveals":[158],"important":[159],"into":[161],"limitations":[163],"current":[165],"LLMs.":[166],"lack":[171],"nuanced":[173],"expertise":[174],"required":[175],"handle":[177],"complex":[178],"details":[179],"struggle":[181],"distinguish":[183],"between":[184],"correct":[185],"incorrect":[187],"information.":[188],"challenges,":[192],"incorporates":[196],"both":[197],"external":[198],"ground-truth":[199],"databases":[200],"expert":[203],"knowledge.":[204],"TRA":[205],"allows":[206],"experts":[208],"iteratively":[210],"provide":[211],"feedback":[212],"continuous":[214],"improvement.":[215],"The":[216],"code":[217],"available":[219],"at":[220],"\\href{https://github.com/xschen-beb/CyberThreat-Eval}{\\texttt{GitHub}}":[221],"\\href{https://huggingface.co/datasets/xse/CyberThreat-Eval}{\\texttt{HuggingFace}}.":[223]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-12T00:00:00"}
