{"id":"https://openalex.org/W7160433987","doi":"https://doi.org/10.48550/arxiv.2605.03697","title":"Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts","display_name":"Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160433987","doi":"https://doi.org/10.48550/arxiv.2605.03697"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03697","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":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.2605.03697","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135498421","display_name":"Xing Zhang","orcid":"https://orcid.org/0000-0002-4960-0221"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101940025","display_name":"Keyu Zhang","orcid":"https://orcid.org/0000-0001-5296-9735"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Keyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135501909","display_name":"Taohong Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Taohong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135490315","display_name":"Anbang Ruan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruan, Anbang","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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.7702000141143799,"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"}},"topics":[{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.7702000141143799,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.03999999910593033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.029999999329447746,"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/vulnerability","display_name":"Vulnerability (computing)","score":0.6930999755859375},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.6668000221252441},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6141999959945679},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4993000030517578},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4945000112056732},{"id":"https://openalex.org/keywords/vulnerability-assessment","display_name":"Vulnerability assessment","score":0.45680001378059387}],"concepts":[{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.6930999755859375},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.6668000221252441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6516000032424927},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6478999853134155},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6141999959945679},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C167063184","wikidata":"https://www.wikidata.org/wiki/Q1400839","display_name":"Vulnerability assessment","level":3,"score":0.45680001378059387},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.43209999799728394},{"id":"https://openalex.org/C178148461","wikidata":"https://www.wikidata.org/wiki/Q1632136","display_name":"Security controls","level":3,"score":0.39500001072883606},{"id":"https://openalex.org/C2779950589","wikidata":"https://www.wikidata.org/wiki/Q7544035","display_name":"Smart contract","level":3,"score":0.35409998893737793},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2897000014781952},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2612999975681305},{"id":"https://openalex.org/C2779777834","wikidata":"https://www.wikidata.org/wiki/Q4202277","display_name":"Enforcement","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C527648132","wikidata":"https://www.wikidata.org/wiki/Q189900","display_name":"Information security","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03697","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.03697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03697","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":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":{"Smart":[0],"contracts":[1],"on":[2,34],"blockchains":[3],"are":[4],"prone":[5],"to":[6,13,18,88],"diverse":[7],"security":[8,130],"vulnerabilities":[9],"that":[10],"can":[11],"lead":[12],"significant":[14],"financial":[15],"losses":[16],"due":[17],"their":[19],"immutable":[20],"nature.":[21],"Existing":[22],"detection":[23],"approaches":[24],"often":[25],"lack":[26],"flexibility":[27],"across":[28,72],"vulnerability":[29,51,64,95],"types":[30],"and":[31,55,84,109,126],"rely":[32],"heavily":[33],"manually":[35],"crafted":[36],"expert":[37],"rules.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42],"present":[43],"an":[44,103,110],"LLM-based":[45],"framework":[46],"for":[47,92,124],"practical":[48],"smart":[49,128],"contract":[50,129],"detection.":[52],"We":[53],"construct":[54],"release":[56],"a":[57],"large-scale":[58],"dataset":[59],"comprising":[60],"31,165":[61],"professionally":[62],"annotated":[63],"instances":[65],"collected":[66],"from":[67],"over":[68],"3,200":[69],"real-world":[70],"projects":[71],"15":[73],"major":[74],"blockchain":[75],"platforms.":[76],"Our":[77],"approach":[78],"leverages":[79],"precise":[80],"AST-based":[81],"context":[82],"extraction":[83],"vulnerability-specific":[85],"prompt":[86],"design":[87],"instantiate":[89],"customized":[90],"detectors":[91],"13":[93],"prevalent":[94],"categories.":[96],"Experimental":[97],"results":[98],"demonstrate":[99],"strong":[100],"effectiveness,":[101],"achieving":[102],"average":[104,111],"positive":[105],"recall":[106,113],"of":[107,114,119],"0.92":[108],"negative":[112],"0.85,":[115],"highlighting":[116],"the":[117],"potential":[118],"carefully":[120],"engineered":[121],"contextual":[122],"prompting":[123],"scalable":[125],"high-precision":[127],"analysis.":[131]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}