探索聯(lián)邦軍事大語言模型中的潛在提示注入攻擊及其緩解措施 Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation

Repeatuntilconvergence

ExploringPotentialPromptInjectionAttacksinFederatedMilitaryLLMsandTheirMitigation

YoungjoonLee,TaehyunPark,YunhoLee,JinuGong,JoonhyukKang

arXiv:2501.18416v1[cs.LG]30Jan2025

Abstract—FederatedLearning(FL)isincreasinglybeingadoptedinmilitarycollaborationstodevelopLargeLanguageModels(LLMs)whilepreservingdatasovereignty.However,promptinjectionattacks—maliciousmanipulationsofinputprompts—posenewthreatsthatmayundermineoperationalsecurity,disruptdecision-making,anderodetrustamongallies.ThisperspectivepaperhighlightsfourpotentialvulnerabilitiesinfederatedmilitaryLLMs:secretdataleakage,free-riderexploitation,systemdisruption,andmisinformationspread.Toaddressthesepotentialrisks,weproposeahuman–AIcollab-orativeframeworkthatintroducesbothtechnicalandpolicycountermeasures.Onthetechnicalside,ourframeworkusesred/blueteamwargamingandqualityassurancetodetectandmitigateadversarialbehaviorsofsharedLLMweights.Onthepolicyside,itpromotesjointAI–humanpolicydevelopmentandverificationofsecurityprotocols.Ourfindingswillguidefutureresearchandemphasizeproactivestrategiesforemergingmilitarycontexts.

IndexTerms:federatedlearning,largelanguagemodel,ad-versarialattack,militarypolicy

I.INTRODUCTION

TheriseofLLMsandspecializedAIhardware(e.g.,IntelGaudi)hasacceleratedtheuseofAIinmanydefenseap-plications,enablingadvancedanalyticsthatwerepreviously

outofreach[1]

.

FL[2]providesaframeworkforallied

nationstocollaborativelytrainLLMswhilemaintainingdata

sovereignty[3],[4],asshowninFig

.

1.

ByutilizingFL,eachparticipantpreservessensitiveinformationwhilereducingthe

riskofunauthorizedaccess[5]

.Atthesametime,FLfacilitatescollaborativemodeldevelopment,italsonecessitatesrobustsecuritymeasurestodefendagainstevolvingadversarialtac-

tics[6]

.Specifically,themostpressingconcernisthethreatofpromptinjectionattacks,inwhichadversariescleverlymanipulateoralterinputpromptstoextractsecretdataor

disruptmission-criticalsystems[7],[8]

.

Suchattackscansignificantlyweakenoperationalsecurity,interruptcrucialdecision-makingprocesses,andunderminethetrust

[9]thatunderlieseffectivemilitarycooperation

amongalliednations.BecauseFLinvolvesacoalitionofcountrieswithnumerousAIandanalysisexperts,addressingthesethreatsnecessitatesbothadvancedtechnicalsolutionsandpolicyframeworksthatcanaccommodatevariedde-fenserequirements.

Promptinjectionattacks[10],[11],[12]

mayappearinfourmainformsinmilitarycontext:secretdataleakage,free-riderexploitation,systemdisruption,andmisinformationspread,eachposingdistinctobstaclestothereliabilityandintegrityoffederatedmilitaryLLMs.Thesethreatsoftenremainsubtleandthusdifficulttodetectthrough

traditionalmonitoringmethods[13],underscoringtheneed

Trainwithprivateconfidentialdata

Synchronizeinitial

LLMtoalliednations

Exchangeweight

oftrainedLLMsAggregateLLMs

SynchronizeExchangeLocallearningAggregation

Benignnation'scloudserverCompromisednation'scloudserver

Fig.1:FLframeworkformilitaryLLMtrainingacrossalliednations.Theprocessinvolvesfourkeystages:(1)initialLLMsynchronization,(2)localtrainingwithprivatedata,(3)weightexchange,and(4)modelaggregation.Thisiterativeprocesscontinuesuntilconvergence,whilemitigatingadversarialrisks.Bluecloudsrepresentbenignnation’sserverswhileredcloudsindicatepotentiallycompromisedservers.

forspecializedmethodstailoredtomultinationalcontexts.Recognizingtheseevolvingvulnerabilitiesisessentialforcreatingrobustcountermeasures,asoverlookingthemcouldleavecriticaloperationsopentocompromiseinhigh-stakesmilitaryscenarios.

Inthispaperwepresentaninsightonhowtoaddresstheserisksbypresentingahuman–AIcollaborativecounter-measuresfrombothtechnicalandpoliticalperspectives.Atfirst,weproposetechnicalcountermeasureprocessincludingred/blueteamwargamingandcontinuousqualityassurance,whichhelpunveilhiddenvulnerabilitieswithinsharedLLMinfrastructuresandreinforceoverallsystemresilience.Then,weproposepoliticalcountermeasureprocess,wheremilitarypolicy,domainexperts,andAIexpertsworktogethertoestablishpropersecuritypolicyforthenation.Byapplyingeitherorbothoftheseapproaches,weaimtopresentapracticalsolutiontocounterpresentandemergingpromptinjectionthreatsinfederatedmilitaryenvironments.Therefore,thisensuressustainedoperationaleffectivenessandreinforcestrustamongcoalitionpartners.

Themaincontributionsofthispaperareasfollows:

?WeintroduceFLanditsvulnerabilitiestopromptinjec-tionattacks,highlightingthepotentialrisksandsecuritychallengesindecentralizedAIsystems.

?Wepresentfourpotentialmajorthreatscenariosinmili-taryFL,includingsecretdataleakage,free-riderexploita-tion,systemdisruption,andmisinformationspread.

?Weproposehuman-AIcollaborativecountermeasuresin

perspectiveoftechnicalandpoliticalway.

?Additionally,weemphasizetheneedforstandardizedsecurityframeworksandcooperativedefensestrategies.

Theremainderofthispaperisorganizedasfollows.InSection

II,providesbackgroundknowledgeofFLandpromptinjection

attacks.Section

III

introducesthepotentialchallengesinfederatedmilitaryLLMs.Section

IV

discussestechnicalandpoliticalcountermeasuresforthepotentialthreats.Finally,section

V

exploresfuturedirectionandsection

VI

concludesthepaper.

II.BACKGROUND

Inthissection,weintroducethekeybackgroundconceptsforFLandpromptinjectionattacks.WespecificallyexploretheirimpactonmilitarycollaborationsamongalliednationsandthechallengestheypresentinsecureAIintegration.

A.FLTopologyinMilitaryAlliances

Indata-sensitivefieldssuchashealthcareandfinance

[14],FLhasemergedasanestablishedapproachforsecure

collaborativelearningduetocharacteristicsdepictedinFig.

2.

Themilitaryisalsoincreasinglyconsideringitsadop-tiontoenhanceoperationalsecurityandAI-drivendecision-making.TheFLarchitectureenablesparticipatingnationstocontributetomodeltrainingwhilepreservingtheirexisting

securityinfrastructureandoperationalautonomy[15]

.Thisdecentralizedapproachallowsnationstoleverageinsightsthatmaynotbereadilyavailablewithintheirowndatasourceswhilemaintainingstrictsecuritycontrols.Forexample,theU.S.DepartmentofDefenseisactivelycollaboratingwithacademia,industry,andalliednationstoexploretheadoptionofFLfordatamanagementandresponsibleAI,ensuring

alignmentwithsecurityandoperationalrequirements[16]

.

AmajorbenefitofFLinmilitarycoalitionsisitsabilitytoenhancedecision-makingbyintegratingdiverseoperational

experiencesanddatasources[17]

.Differentnationshaveuniquebattlefieldenvironments,weaponsystems,andthreatintelligence,allofwhichcanbeincorporatedintoasharedmodelwithoutcompromisingnationalsecurity.ThiscollectivelearningimprovestheadaptabilityofAImodelstovariedmilitaryscenarios,makingthemmoreeffectiveinreal-worldmilitaryoperations.Furthermore,FLoptimizescommunica-tionefficiencybyexchangingonlymodelupdatesinsteadofrawdata,significantlyreducingbandwidthrequirements.Thisstreamlinedcommunicationenablesreal-timeadaptability,al-lowingAImodelstorapidlyadjusttoevolvingthreatsanddynamicbattlefieldconditions.

ByincorporatingadvancedLLMsintoaFLframe-work,alliescansecurelymergelanguagedatafromvarioussources.Thisintegrationenhancescross-linguisticcapabilities,strengtheningcommunicationprotocolsinjointoperations.Eachparticipantcontributesdomain-specifictextcorpora,en-richingthesharedmodelwithcontextualknowledgedrawnfromdiversemilitarypractices.Thisholisticapproachbolsterslanguagecomprehension,enablingtheLLMtoaccuratelyinterpretmission-criticaldirectivesandintelligencereports.

Privacy-preservation

-Reduceexposurerisk

-Keepsdatadecentralized

Scalability

t

-Expandacrosssystems

-Seamlessexpansion

FederatedLearning

Jointlearning

-Shareinsights

-Real-timeadaption

Regulatorycompliance

-Meetprivacylaw

-Protectsensitivedata

Fig.2:IllustrationoffourkeyadvantagesinFL:(1)Privacy-preservationcapabilities,enablingreducedexposureriskanddecentralizeddatamanagement;(2)Jointlearningframe-work,facilitatingsharedinsightsandreal-timeadaptation;(3)Systemscalabilitysupportingcross-systemexpansionandseamlessgrowth;and(4)Regulatorycompliancefeaturesensuringadherencetoprivacylawsandprotectionofsensitiveinformation.TheinterconnectedcirculardesignemphasizesthesynergisticrelationshipamongthesekeyfactorsinFL.

Ultimately,FL-drivenLLMtrainingpreservesdatasovereigntywhilefacilitatingfaster,morereliableinformationsynthesisforcoalitiondecision-makers.

B.ConceptofPromptInjectionAttack

Promptinjectionattackshaverecentlyemergedasasignifi-

cantthreatinmodernLLM-basedapplications[18]

.Theseat-tacksexploittheinherentvulnerabilityinAIsystemsthatrelyontextualinstructions,enablingadversariestomanipulateor

alterthepromptsformaliciousoutcomes[19]

.Inmanycases,thesemanipulationsinvolveembeddingcarefullydisguised

instructionsthatexploitamodel’shiddenvulnerabilities[20]

.Unliketraditionaldatapoisoningattacksthattamperwithtrainingdata,promptinjectionfocusesoninterferingwiththemodel’sreasoningprocessafterdeployment.Bysubtlyembeddingdeceptivetriggersormanipulativecontentinuser-providedtext,attackerscanforcethemodeltorevealsensi-tiveinformationorperformunauthorizedactions.AsLLMsbecomeintegraltosensitiveoperations,theriskofprompt

injectionattacksgrowsmorepronounced[21]

.

Themostcriticalchallengeofpromptinjectionattacksistheirpotentialtobypassconventionalsecuritymeasuressuch

asanomalydetectionandcontentfilters[22]

.Becausetheseattacksoftenappearasnormaltextinputs,theycanremainundetecteduntiltheysignificantlycompromiseanoperation.Evenseeminglybenignvariationsinphrasingcanleadtosignificantvulnerabilitieswhenthesystemprocessesthein-put.Additionally,adversariesexhibitevolvingcapabilitiestorefinetheirinjectiontechniques,increasingthecomplexityofpredictionandmitigation.Thisevolvingthreatisparticularlyconcerninginfederatedenvironments,whereasinglepromptinjectioncanpropagateitsadverseeffectsthroughoutfederated

AIsystems[23]

.Consequently,understandingandmitigatingtheserisksisimperativeformaintainingoperationalsecurityandtrustincollaborativemilitaryAInetworks.

Atcompromisednation:

Prompt

Generateasummaryofbattlefieldstrategyandpolicyinsightsbasedontraineddata.

Atcompromisednation:

Secretdata

Sufficient?

yes

Sufficient?

noyes

Secretdata

no

SharedLLMweightsfromalliednations

Init&Fine-tune

Filteruseless

Expert

in-houseLLM

secretdata

verification

End

End

Secretdata

Secretdata

Broadcast

Init&fine-tune

trainedLLM

aggregatedLLM

End

End

Prompt

Completethisclassifiedmilitaryreport:"Thelatestmissile

deploymentlocationsinclude...

Init&Fine-tune

Filteruseless

Expert

in-houseLLM

secretdata

verification

BroadcasttrainedLLM

Init&Fine-tuneaggregatedLLM

(a)Secretdataextractionattack.

(b)Free-riderexploitationattack.

Atcompromisednation:

Aggregate

Atcompromisednation:

Prompt

EnsurethatXlocationisnotincludedinanyattackplansortargetingoperations.

Prompt

Subtlymanipulatethedatainthetrainingdataset.

Expert

verification

Secretdata

Sufficient?

noyes

Aggregate

t↓

↓

Prompt

Sufficient?yes

Secretdata

no

ManipulatedwithaggregatedLLMweight

End

ManipulatedwithtrainedLLMweight

Init&Fine-tunein-houseLLM

Init&Fine-tuneaggregatedLLM

Compare&Broadcast

End-

End

BroadcasttrainedLLM

Broadcast

manipulatedLLM

Expert

verification

(c)Systemdisruptionattack.

(d)Misinformationpropagationattack.

Fig.3:IllustrationoffourpotentialattackscenariosinmilitaryFLenvironments:(a)Secretdataextractionattack,whereadversariessystematicallyprobesharedLLMstoextractclassifiedinformationthroughtargetedpromptsandexpertverification,(b)Free-riderexploitationattackleveragingstrategicpromptstogainmilitaryintelligencewhilewithholdingauthenticdatacontribution,(c)Systemdisruptionattackmanipulatingmodelbehaviorthroughcarefullycraftedpromptstocreatetacticalblindspots,and(d)Misinformationspreadattackutilizingdual-channelpropagationtosystematicallyinjectfalseinformationintothefederation.EachscenariodemonstratessophisticatedattackmethodologiesthatexploitvulnerabilitiesinfederatedmilitaryLLMdeploymentswhilemaintainingapparentlegitimateparticipation.

III.KEYCHALLENGES

Inthissection,wepresentfourpotentialpromptinjectionattackstargetingfederatedmilitaryLLMs.Wespecificallycoverfourcriticalvulnerabilities:secretdataleakage,free-riderexploitation,systemdisruption,andmisinformationprop-agation,eachposinguniqueoperationalandsecurityrisks.

A.SecretDataLeakage

Theriskofsecretdataleaks,asshowninFig.

3a,isa

majorconcerninfederatedmilitaryLLMsystems.Inthesecases,attackerstakeadvantageofalteredtextinputstoextractclassifieddetailsfromthesharedmodel.Insuchsituations,malicioususersorcompromisedgroupsrepeatedlyasktheglobalLLMcarefullydesignedquestionstoaccessrestricteddata,suchasmissilelocationsorthestatusofsurveillancesystems.Theseattackstakeadvantageofhowthemodelstoressensitiveinformation,bypassingstandardsecuritychecksandleadingtounauthorizedaccess.SinceFLgathersdatafrommultiplealliedforces,itincreasestheserisksandallowslarge-scalemaliciousattemptstoextractinformation.

Atthestartoftheattack,adversariescreatesetsoftestquestionsdesignedtorevealhiddeninformation.Aftercol-lectingthemodel’sresponses,theyuseexpertreviewstocheckwhethertheextractedinformationisaccurateanduseful.Byrepeatingthisprocess—improvingquestionsandverifying

answers—theygraduallybuildlargecollectionsofclassifieddata.Inthefinalstage,theyremoveunnecessarydetails,keepingonlythemostusefulinformationfortheiroperations.

Oncetheirdatacollectioniscomplete,adversariesintegratetheextractedsecretsintotheirownmilitaryAIsetupsthroughtwomainchannels.Theymayupdatetheirlocalizedlanguagemodelswiththestolenknowledge,therebyenrichingtheirunderstandingofallieddefenses.Alternatively,theycanincor-poratethesecretdataintothesharedmodelandredistributeittothefederatednetwork,effectivelyembeddingTrojan-likevulnerabilities.Thistwo-stepattacknotonlyputsimmediatesecurityoperationsatriskbutalsocreatesapathforlong-termbreaches.Therefore,itiscrucialformilitaryFLinitiativestoimplementrigorousframeworkstodetect,flag,andpreventprompt-baseddataextractionattack.

B.Free-riderAttack

Free-riderattacksinfederatedmilitaryLLMs,asdepictedinFig.

3b,revolvearoundthestrategicwithholdingofproprietary

databyunscrupulousparticipants.Althoughtheseadversariesstillexploitknowledgegleanedfromcollaborativemodels,theyavoidcontributingtheirownvaluableintelligence,therebyskewingthebenefitsintheirfavor.Thisapproachmirrorsse-cretdataleakagetacticsinsofarasmaliciousactorscanrefinetheirlocalizedlanguagemodelsusingconfidentialinformationobtainedfromthefederation,butheretheprimarygoalisto

capitalizeonsharedupdateswithoutreciprocating.Byapply-ingthesestoleninsightstotheglobalorlocalmodel,free-riderscangainapronouncedtacticaledgewhilesafeguardingtheirhiddendataassets.

Inpractice,suchanasymmetricdynamiccancauseseveralseriousconsequencesformultinationaldefensecooperation.Foremost,thefederation’soverallmodelqualitydeteriorateswhenessentialinputsfromcertainalliesareabsent,reduc-ingthemodel’scontextualreachandpredictiveaccuracy.Inaddition,thetrust-basedstructurethatunderpinsjointinitia-tivesweakensassuspicionsariseconcerninginconsistentdatasharing.Overtime,persistentfree-rideractivitiescanproduceunbalancedmodels,whichoverlookkeyoperationalnuancesanddegradethesystem’sreadiness.Ultimately,effectivelymitigatingtheseattacksdemandsrobustdetectionmethodsandpolicyframeworksthatensureeveryparticipantcontributesanappropriateshareofthecollectiveintelligence.

C.SystemDisruptionAttack

Systemdisruptionattacks,asillustratedinFig.

3c,present

arefinedmethodofsabotageinfederatedmilitaryLLMs,wherehostilefactionssystematicallymodifyhowthemodel

processescrucialoperationaldata.Attheoutset,theadversary

aggregatesupdatesfromdifferentalliedmodelstoestablishaunifiedbaselineripeforexploitation.Theytheninjectintri-catelystructuredpromptsthatseedsubtlemisalignmentsinthemodel’sreasoningaboutspecificmissiontheaters,equipmentcapabilities,orconflictscenarios.Theseplanteddistortionsdisguiseaslegitimaterefinements,makingthemdifficulttodetectthroughstandardverificationchecks.

Attackersfurtherrefinethesedisruptionsbycross-referencingtheaggregatedmodelwithaprivatelyfine-tunedversion,comparingoutcomestoidentifythemosteffectivemeansofintroducingerrors.Throughrepeatedpromptengi-neeringanditerativefeedback,theyembeddeliberatebiasesorblindspotsintothemodel’sstrategicassessments.Thecom-promisedupdatesprogressivelypropagateacrossthepartici-patingorganizations,systematicallyunderminingthemodel’sreliability.Thelong-termramificationsincludeskewedopera-tionalplanning,misjudgedresourceallocation,andweakenedresponsivenesstoemergentthreats.Thisunderscorestheim-portanceofrigorous,ongoingevaluationofmodelrevisionstoexposesignsoforchestratedmanipulation.

D.MisinformationSpread

Misinformationspreadattack,asshowninFig.

3d,tar

-getsthefidelityofknowledgeinfederatedmilitaryLLMsbyorchestratingthedeliberateinjectionoffabricateddataanddistortions.Initially,theattackerdiscreetlyalterstraininginputs,populatingthemwithmisleadingstatementsordoc-toredfactsundertheguiseoflegitimatetextentries.Thesealterationsstealthilyimplantsystemicfalsehoodsintothemodel’srepresentation,underminingthesharedintelligencepoolmaintainedbycollaboratingallies.Theprimaryriskofthisschemeliesinitspropensitytoemergegradually,asthe

introducedmisinformationblendsseamlesslywithauthenticcontent.

Maliciousparticipantsrefinetheirtechniquesbymodifyingbothlocaldatasetsandcombinedmodelweights,leverag-ingsubject-matterexpertstovalidatewhetherthefabricateddetailsappearplausible.Thislayeredvalidationapproachhelpsensurethefalsehoodsremainundetectedandeffectivelyintegrated.Overtime,thecorruptedupdatesarebroadcastbacktotheFLnetworkinmultiplewaves,compoundingtheinfiltra-tion.Thisresultsinapervasivespreadofinaccuraciesthatcanobscurecriticalwarnings,distortadversaryprofiles,orskewstrategicdeliberations.Consequently,implementingrobustval-idationprotocols,thoroughcross-referencingofsources,anddynamicthreatintelligencereviewsisvitalinmitigatingthedangersposedbytargetedmisinformationcampaigns.

IV.COUNTERMEASURES

Inthissection,wepresentahuman-aicollaborativestrat-egyforprotectingfederatedmilitaryLLMsagainstprompt

injectionattacks.Weproposecountermeasuresbasedonboth

technicalandpoliticalperspectivestoensurerobustdefenseframeworkswhilemaintainingstrategicpolicyalignment.

A.TechnicalCountermeasures

Onthetechnicalfront,oursolutionfeaturesawargaming-centricmethodologythatcapitalizesoncollaborativeinterac-tionsbetweenhumanexpertsandAI-drivensystemsasshowninFig.

4a.

Attheoutset,thoroughvulnerabilityassessmentsguideanAI-driventhreatdesignprocess,pinpointingpotentialattackpathwaysandevaluatingtheiroperationalrepercussions.ThisformsthefoundationforspecializedredteamandblueteamLLMsimulations,whereinredteammodelslaunchsimu-latedassaultstargetingrecognizedweaknesses,andblueteammodelsdeviseadaptiveresponsesinrealtime.Throughoutthissimulatedengagement,militarydomainspecialistsscrutinizethetacticsandoutcomes,ensuringthatdefensivemeasuresaccuratelymirrorreal-worldscenarios.

Akeystrengthofthisframeworkistheiterativelearn-ingcycleenabledbycontinualadversarialinterplay.AstheredteamLLMsadapttheirattackvectors,theblueteamLLMsconcurrentlyrefinecountermeasuresbasedonreal-timeinsightsandexpertrecommendations.Thisconstantback-and-forthhonesthesystem’sresilience,ultimatelyallowingthefederationtoidentifyandpatchhiddenvulnerabilities.Onceadequatedefensivecapacityisconfirmed,theframeworkprogressestoacomprehensivequalityassurance(QA)phase.DedicatedQALLMscontinuouslymonitorthedeployedmod-elsforirregularitiesorattemptedexploits,drawingonexpertinputwhenanomaliesemerge.Automatedcorrectionprotocolsaddressminorthreatstomaintainuninterruptedoperations,andmajorfindingstriggertargetedanalysisbydomainspecialists.Thismulti-layeredsetupensuresthatfederatedmilitaryLLMsarenotonlyshieldedfrompromptinjectionattacksatdeploy-mentbutalsoremainrobustagainstevolvingadversarialtacticsovertime.

Technicalcountermeasures:

SharedLLMs

ConfirmedLLMs

Human-AIcollaborativered/blueteamwargaming

Error

correction

Start一

fromalliednations

Vulnerabilityanalysis

ThreatdesignbyLLM

!

Attacker:RedteamingLLM

Defender:BlueteamingLLM

Aggregate

Qualityexpertverification

End

!

!

Domainexpertverification

Sufficient?

MonitoringbyQALLM

No

Yes

Domainexpertfeedback

AggregatedLLM

(a)Technicalcountermeasuresframework.

Policycountermeasures:

Commentsbydomainexperts

PolicydesignbyLLM

RiskmodelingbyLLM

PolicyReviewbyLLM

Policyexpertconfirmation

Policyupdate

一End

Start一

!!t

AIcommitteeconfirmation

Policyexpertverification

Domainexpertconfirmation

Sufficient?!Sufficient?!

Policyexpert◆◆

feedbackNoYesNoYes

Human-AIcollaborativepolicydesign

(b)Policycountermeasuresframework.

Fig.4:Proposedhuman-AIcollaborativecountermeasureframeworksforprotectingfederatedmilitaryLLMs:(a)Technicalframeworkimplementingred/blueteamwargamingmethodology,wherespecializedLLMsconductadversarialtestingunderdomainexpertsupervision,followedbycomprehensivequalityassuranceanderrorcorrectionprocesses,(b)PolicyframeworkutilizingiterativepolicydevelopmentthroughAI-drivendesignandriskmodeling,withmulti-stageexpertverificationandconfirmationprotocolstoensurerobustsecuritymeasures.BothframeworksemphasizecontinuouscollaborationbetweenhumanexpertiseandAIcapabilitiestomaintainoperationalsecuritywhilepreservingsystemeffectiveness.

B.PolicyCountermeasures

FromapolicystandpointasshowninFig.

4b,weintroduce

astructuredhuman–AImodelthatembedsrigoroussecurityrequirementsintotheorganizationalandoperationalprocessesgoverningfederatedLLMs.Theinitiativecommenceswithdomainexpertscontributingbaselinesecuritypriorities,whichareconvertedintoformalguidelinesbyspecializedpolicyde-signLLMs.Thesedraftpoliciesundergoaniterativerefinementcycle,guidedbyriskmodelingLLMsthatevaluatepotentialthreatvectorsandgapsinenforcement.Thiscycleispunc-tuatedbycontinuoushumanoversight,ensuringthatpolicyoutcomesstrikeanappropriatebalancebetweenstringency,feasibility,andstrategicreadiness.

Thepolicyframeworkadvancesthroughamulti-stageval-idationpipelinedesignedtoverifyitspracticalapplicabilityandthoroughness.First,policyexpertsgaugeeachproposalagainstpre-establisheddefensestandardsandmission-specificmandates.Ifdiscrepanciesarise—suchasoverbroadregu-lationsthathindercollaboration—expertsproposetargetedmodifications.Thepolicyisthenre-analyzedbyAI-drivenriskmodels,whichconfirmwhetheranynewlyintroducedrevisionsinadvertentlyweakensecurityorintroduceopera-tionalbottlenecks.Thisongoingloopcontinuesuntilthepolicyattainsbothcomprehensivesecuritycoverageandalignmentwithallianceobjectives.Finally,recognizeddomainauthor-

itiesofferstrategicandtacticalvalidation,verifyingthattherecommendeddirectivesneithercompromiseongoingmissionsnorimpedelegitimateinformationexchange.

Oncefullyvetted,therefinedpolicyundergoesaformalratificationpro