{"id":"https://openalex.org/W7157046506","doi":"https://doi.org/10.48550/arxiv.2604.23374","title":"Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents","display_name":"Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7157046506","doi":"https://doi.org/10.48550/arxiv.2604.23374"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23374","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.2604.23374","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017012201","display_name":"Yuandao Cai","orcid":"https://orcid.org/0000-0001-6340-1416"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yuandao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102714233","display_name":"Wensheng Tang","orcid":"https://orcid.org/0000-0002-4259-3321"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Wensheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134778393","display_name":"Cheng Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134751867","display_name":"Shengchao Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Shengchao","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/T11986","display_name":"Scientific Computing and Data Management","score":0.2870999872684479,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.2870999872684479,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.12690000236034393,"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/T11424","display_name":"Security and Verification in Computing","score":0.07590000331401825,"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/context","display_name":"Context (archaeology)","score":0.6103000044822693},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5649999976158142},{"id":"https://openalex.org/keywords/information-flow","display_name":"Information flow","score":0.5322999954223633},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4415000081062317},{"id":"https://openalex.org/keywords/fuzz-testing","display_name":"Fuzz testing","score":0.4207000136375427},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4092000126838684},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4034999907016754},{"id":"https://openalex.org/keywords/taint-checking","display_name":"Taint checking","score":0.4025999903678894},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.3864000141620636},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.36059999465942383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8252999782562256},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6103000044822693},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C2779136372","wikidata":"https://www.wikidata.org/wiki/Q10283002","display_name":"Information flow","level":2,"score":0.5322999954223633},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C111065885","wikidata":"https://www.wikidata.org/wiki/Q1189053","display_name":"Fuzz testing","level":3,"score":0.4207000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41819998621940613},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C63116202","wikidata":"https://www.wikidata.org/wiki/Q7676227","display_name":"Taint checking","level":3,"score":0.4025999903678894},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.37369999289512634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3716000020503998},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.36059999465942383},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3483999967575073},{"id":"https://openalex.org/C157486923","wikidata":"https://www.wikidata.org/wiki/Q1376436","display_name":"String (physics)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.30379998683929443},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.29510000348091125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29440000653266907},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.2939000129699707},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C204495577","wikidata":"https://www.wikidata.org/wiki/Q1205349","display_name":"Callback","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23374","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.2604.23374","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23374","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":[{"id":"https://metadata.un.org/sdg/16","score":0.6655275821685791,"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":{"Autonomous":[0],"Large":[1],"Language":[2],"Model":[3],"(LLM)":[4],"agents":[5,29,109],"are":[6],"increasingly":[7],"deployed":[8],"to":[9,30,66,139,145],"conduct":[10],"complex":[11],"tasks":[12],"by":[13,73],"interacting":[14],"with":[15,216],"external":[16,25],"tools,":[17],"APIs,":[18],"and":[19,39,128,153,211],"memory":[20,60],"stores.":[21],"However,":[22],"processing":[23],"untrusted":[24,143],"data":[26,69],"exposes":[27],"these":[28,44],"severe":[31],"security":[32],"threats,":[33],"such":[34],"as":[35,115,121],"indirect":[36],"prompt":[37],"injection":[38],"unauthorized":[40],"tool":[41],"execution.":[42],"Securing":[43],"systems":[45],"requires":[46],"effective":[47,204],"information":[48,94],"flow":[49,95],"tracking.":[50],"Yet,":[51],"traditional":[52],"taint":[53,87,105],"analysis":[54],"that":[55,104,182,201],"is":[56,71,103],"designed":[57],"for":[58,91,191],"program":[59],"states":[61],"fundamentally":[62],"fails":[63],"when":[64],"applied":[65],"LLMs,":[67],"where":[68],"propagation":[70,106,196],"governed":[72],"probabilistic":[74],"natural":[75],"language":[76],"reasoning.":[77],"In":[78],"this":[79],"paper,":[80],"we":[81],"present":[82],"NeuroTaint,":[83],"the":[84,92],"first":[85],"comprehensive":[86],"tracking":[88],"framework":[89],"tailored":[90],"unique":[93],"characteristics":[96],"of":[97],"LLM":[98,108,192],"agents.":[99],"Our":[100],"key":[101],"insight":[102],"in":[107,194],"must":[110],"be":[111],"understood":[112],"not":[113],"only":[114],"explicit":[116],"content":[117],"transfer,":[118],"but":[119],"also":[120],"semantic":[122,149],"transformation,":[123],"causal":[124,151],"influence":[125],"on":[126,160,205],"decisions,":[127],"cross-session":[129],"persistence":[130],"through":[131],"memory.":[132],"NeuroTaint":[133,183,202],"therefore":[134],"audits":[135],"execution":[136],"traces":[137],"offline":[138,215],"reconstruct":[140],"provenance":[141],"from":[142],"sources":[144],"privileged":[146],"sinks":[147],"using":[148,171],"evidence,":[150],"reasoning,":[152],"persistent":[154],"context":[155],"tracking,":[156],"rather":[157],"than":[158],"relying":[159],"exact":[161],"string":[162],"matches":[163],"or":[164],"pre-defined":[165],"source-sink":[166,195],"paths":[167],"alone.":[168],"Extensive":[169],"evaluation":[170],"TaintBench,":[172],"our":[173],"400-scenario":[174],"benchmark":[175],"spanning":[176],"20":[177],"real-world":[178],"agent":[179],"frameworks,":[180],"shows":[181],"substantially":[184],"outperforms":[185],"FIDES,":[186],"an":[187],"information-flow-control":[188],"(IFC)-style":[189],"baseline":[190],"agents,":[193],"detection.":[197],"We":[198],"further":[199],"show":[200],"remains":[203],"established":[206],"agent-security":[207],"benchmarks,":[208],"including":[209],"InjecAgent":[210],"ToolEmu,":[212],"while":[213],"operating":[214],"modest":[217],"additional":[218],"auditing":[219],"cost.":[220]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}