OWASP：LLM与生成式AI安全解决方案（2025Q1）

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10/15/2024

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Re-factorSolutionsLandscapecategories,

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ScottClinton,Contributors,ReviewerInputs

NewentriesfromtheOnline

SolutionsLandscapeCatalog,Updatedlayoutand

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■OWASPTop10forLLMs-LLMSecOpsSolutionsLandscape

■OWASPTop10forLLMs-CyberSecuritySolutionandLLMSecOpsLandscapeGuide

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Page1

Page2

TableofContent

TableofContent2

WhoIsThisDocumentFor?

5

Objectives5

Scope

5

Introduction7

DefiningtheSecuritySolutionsLandscape8

LandscapeConsiderations8

LLMApplicationCategories,SecurityChallenges9

StaticPromptAugmentationApplications10

AgenticApplications11

LLMPlug-ins,Extensions12

ComplexApplications13

LLMDevelopmentandConsumptionModels14

LLMOpsandLLMSecOpsDefined1

5

AQuickOpsPrimer-FoundationforLLMOps15

LLMOpsLifeCycleStages-FoundationforLLMDevSecOps16

Scoping/Planning18

Page3

DataAugmentationandFine-Tuning1

9

ApplicationDevelopmentandExperimentation20

TestandEvaluation21

Release22

Deploy2

3

Operate24

Monitor25

Govern2

6

MappingtotheOWASPTop10forLLMThreatModel27

OWASPTop10forLLMsSolutionsLandscape29

EmergingGenAI/LLM-SpecificSecuritySolutions29

LLM&GenerativeAISecuritySolutions31

SolutionLandscapeMatrixDefinitions32

LandscapeSolutionMatrix33

Acknowledgements43

OWASPTop10forLLMProjectSponsors44

SilverSponsors44

References4

5

ProjectSupporters46

Page4

Letterfromtheleadauthor

Whywecreatedthiscompanionresource

ThecreationofthisdocumentwasinitiatedafterwediscussedasacoreteamthatwhiletheOWASPTop10ListforLLMsandGenerativeAIListprovidedagreatlistofrisksandpotentialmitigations,itfellshortonprovidingthenextlevelofguidance.ThisisinpartduetothestructureofwhatmakesOWASPtop10listsopopular.Thisisbeingconciseandfocusedtohighlightthetoprisksandmitigationforacertainapplicationspace.Thereweremorethanenoughcandidatestogobeyond10,butthefocusof10wefeltessentialtobeabletoensurepracticalfocusedguidance.DeviatingfromthetraditionalOWASPTop10formatwouldbloatthedocumentandimpactitsconciselisting.

Adoptingasolutionsapproachfortheproject

WhiletheTop10listforLLMandGenAIprovidesthelistTop10RiskandMitigations,wefeltitbeneficialgofurtherthantraditionalTop10ListsandtotakeasolutionsapproachandhelpconnecttheTop10RiskstotheopenssourceandcommercialsecuritysolutionsorganizationscouldlooktotohelpaddresstheTop10RisksforLLMsandGenerativeAIinapracticalway.

Inaddition,sincetheGenAIsecuritylandscapeismovingsoquickly,coveringarangeofnewapplication

typesfromstaticpromptaugmentation,throughRAG,pluginsandAgenticAIarchitectures,wesawarangeofnewsecuritysolutionsemergingandwantedtobeabletoprovidearegularlyupdatedresourcetoidentifythesolutionthatcouldbeusedtoaddressthesenewarchitecturesandapplicationriskshighlightedintheTop10forLLMandGenAIList.

Structuringthedocument

Toorganizethesolutions,wechosetoleverageanddocumenttheapplicationtypesandtheLLM/GenAIOpsandSecOpslifecycleandcategoriestoprovideanactionablewaytobothorganizethesolutionsandmap

themtotheTop10forLLMandGenAI,whichwewouldupdatequarterly.Toaccompanythisdocumentwealsodecidedtopublishan

onlinedirectory

.WehopethissolutionguideishelpfulinimplementingyourownstrategyforsecureLLMandGenAIadoptionwithinyourorganization.

-ScottClinton

Co-LeadOWASPTop10forLLMProject

&AI,SecuritySolutionsInitiativeLead

Page5

WhoIsThisDocumentFor?

Thisdocumentistailoredforadiverseaudiencecomprisingdevelopers,AppSecprofessionals,DevSecOpsandMLSecOpsteams,dataengineers,datascientists,CISOs,andsecurityleaderswhoarefocusedon

developingstrategiestosecureLargeLanguageModels(LLMs)andGenerativeAIapplications.ItprovidesareferenceguideofthesolutionsavailabletoaidinsecuringLLMapplications,equippingthemwiththe

knowledgeandtoolsnecessarytobuildrobust,secureAIapplications.

Objectives

ThisdocumentisintendedtobeacompaniontotheOWASPTop10forLargeLanguageModel(LLM)

ApplicationsListandtheCISOCybersecurity&GovernanceChecklist.Itsprimaryobjectiveistoprovidea

referenceresourcefororganizationsseekingtoaddresstheidentifiedrisksandenhancetheirsecurity

programs.Whilenotdesignedtobeanall-inclusiveresource,thisdocumentoffersaresearchedpointof

viewbasedonthetopsecuritycategoriesandemergingthreatareas.Itcapturesthemostimpactfulexistingandemergingcategories.Bycategorizing,defining,andaligningapplicabletechnologysolutionareaswiththeemergingLLMandgenerativeAIthreatlandscape,thisdocumentaimstosimplifyresearcheffortsandserveasasolutionsreferenceguide.

Scope

Thescopeofthisdocumentistocreateashareddefinitionofsolutioncategoryareasthataddressthe

securityoftheLLMandgenerativeAIlifecycle,fromdevelopmenttodeploymentandusage.ThisalignmentsupportstheOWASPTop10ListForLLMsoutcomesandtheCISOCybersecurityandGovernanceChecklist.Toachievethis,thedocumentwillcreateaninitialframeworkandcategorydescriptors,utilizingboth

open-sourcesolutionsandprovidingmechanismsforsolutionproviderstoaligntheirofferingswithspecificcoverageareasasexamplestosupporteachcategory.

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Thedocumentadherestoseveralkeyrulestomaintainitsintegrityandusefulness:

●Vendor-AgnosticandOpenApproach:Itmaintainsaneutralstance,avoidingrecommendationsofonetechnologyoveranother,insteadprovidingcategoryguidancewithchoicesandoptions.

●Straightforward,ActionableGuidance:Thedocumentoffersclear,actionableadvicethatorganizationscanreadilyimplement.

●CoordinatedKnowledgeGraph:Itincludescoordinatedterms,definitions,anddescriptionsforkeyconcepts.

●PointtoExistingStandards:Whereexistingstandardsorsourcesoftruthareavailable,the

documentreferencestheseinsteadofcreatingnewsources,ensuringconsistencyandreliability.

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Introduction

WiththegrowthofGenerativeAIadoption,usage,andapplicationdevelopmentcomesnewrisksthataffecthoworganizationsstrategizeandinvest.Astheserisksevolve,sodoriskmitigationsolutions,technologies,frameworks,andtaxonomies.Toaidsecurityleadersinprioritization,conversationsaboutemerging

technologyandsolutionareasmustbealignedappropriatelytoclearlyunderstoodbusinessoutcomesforAIsecuritysolutions.ThebusinessoutcomesofAIsecuritysolutionsmustbeproperlydefinedtoaidsecurityleadersinbudgeting

Manyorganizationshavealreadyinvestedheavilyinvarioussecuritytools,suchasvulnerabilitymanagementsystems,identityandaccessmanagement(IAM)solutions,endpointsecurity,DynamicApplicationSecurityTesting(DAST),observabilityplatforms,andsecureCI/CD(ContinuousIntegration/ContinuousDeployment)tools,tonameafew.However,thesetraditionalsecuritytoolsmaynotbesufficienttofullyaddressthe

complexitiesofAIapplications,leadingtogapsinprotectionthatmaliciousactorscanexploit.Forexample,traditionalsecuritytoolsmaynotsufficientlyaddresstheuniquedatasecurityandsensitiveinformation

disclosureprotectioninthecontextofLLMandGenAIapplications.Thisincludesbutisnotlimitedtothechallengesofsecuringsensitivedatawithinprompts,outputs,andmodeltrainingdata,andthespecificmitigationstrategiessuchasencryption,redaction,andaccesscontrolmechanisms.

EmergentsolutionslikeLLMFirewalls,AI-specificthreatdetectionsystems,securemodeldeployment

platforms,andAIgovernanceframeworksattempttoaddresstheuniquesecurityneedsofAI/ML

applications.However,therapidevolutionofAI/MLtechnologyanditsapplicationshasdrivenanexplosionofsolutionapproaches,whichhasonlyaddedtotheconfusionfacedbyorganizationsindeterminingwheretoallocatetheirsecuritybudgets.

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DefiningtheSecuritySolutionsLandscape

TherehavebeenmanyapproachestocharacterizingthesolutionslandscapeforLargeLanguageModeltoolsandinfrastructure.InordertodevelopasolutionslandscapethatfocusesonthesecurityofLLMapplicationsacrossthelifecyclefromplanning,development,deployment,andoperation,therearefourkeyareasofinputwehavefocusedontodevelopbothadefinitionforLargeLanguageModelDevSecOPsandrelatedsolutionslandscapecategories.

LandscapeConsiderations

ApplicationTypesandScope-whichimpactsthepeople,processes,andtoolsneededbasedonthecomplexityoftheapplicationandtheLLMenvironment,as-a-service,self-hosted,orcustom-built.

EmergingLLMSecOpsProcess-whilethisisaworkinprogress,manyarelookingtoadaptandadoptexistingDevOpsandMLOpsandassociatedsecuritypractices.WeexpectourdefinitiontoevolveasthedevelopmentprocessesforLLMapplicationsbegintomature.

ThreatandRiskModeling-understandingtherisksposedbyLLMsystems,applicationusage,ormisuselikethoseoutlinedintheOWASPTop10forLLMsandGenerativeAIApplications,arekeytounderstandingwhichsolutionsarebestsuitedtoimprovethesecuritypostureandcombatarangeofattacks.

TrackingEmergingSolutions-manyexistingsecuritysolutionsareadaptingtosupportLLMdevelopmentworkflowsandusecaseshowevergiventhenatureofnewthreatsandevolvingtechnologyandarchitecturesnewtypesofLLM-specificsecuritysolutionswillbenecessary.

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LLMApplicationCategories,SecurityChallenges

OrganizationshavebeenleveragingMachineLearninginapplicationsfordecades.Thisoftenrequired

detailedexpertiseinDataScienceandextensivemodeltraining.GenerativeAIhaschangedthis.Specifically,LargeLanguageModels(LLMs)havemademachinelearningtechnologywidelyaccessible.Theabilityto

dynamicallyinteractinplainlanguagehasopenedthedoorforthecreationofanewclassofdata-driven

applicationsandapplicationintegrations.Furthermore,usageisnolongerlimitedtothehighlyskilledeffortsoftraditionaldevelopersanddatascientists.Pre-trainedmodelsenablenearlyanyonetoperformcomplexcomputationaltasks,regardlessofpriorexposuretoprogrammingorsecurity.Organizationshavebeen

leveragingMachineLearninginapplicationsfordecadesincludingNaturalLanguageProcessing(NLP)modelsthatoftenrequiredetailedexpertiseinDataScienceandextensivemodeltraining.

Withtheadventoftransformerstechnologyenablinggenerativecapabilitiescombinedwiththeeaseofaccessforpre-trainedas-a-servicemodelslikeChatGPTandotheras-a-service,FourmajorcategoriesofLLMApplicationArchitectureemerged;Prompt-centric,AIAgents,Plug-ins/extensions,andcomplex

generativeAIapplicationwheretheLLMplaysakeyroleinalargerapplicationusecase.

(figure:ApplicationCategories&SummaryAttributes)

HavingacommonviewoftypicalLLMapplicationarchitectures,includingagents,models,LLMs,andtheMLapplicationstack,iscrucialfordefiningandaligningtheapplicationstack,securitymodel,andapplicationofferings.Below,wehaveprovidedashortdescriptionofkeycharacteristics,usecases,andsecurity

challengesforeachapplicationcategory.

StaticPromptAugmentationApplications

Theseapplicationsinvolvespecificstaticnaturallanguageinputstoguidethebehaviorofalargelanguagemodel(LLM)towardgeneratingthedesiredoutput.Thistechniqueoptimizestheinteractionbetweenthe

userandthemodelbyfine-tuningthephrasing,context,andinstructionsgiventotheLLM.These

applicationsallowuserstoaccomplishawiderangeoftasksbysimplyrefininghowtheyaskquestionsorprovideinstructions.

KeyCharacteristics

●Humantomodel/modeltohumaninteractionandresponse

●Staticpromptaugmentation

●FlexibilityandCreativity

●SimplicityandAccessibility

●RapidPrototypingandExperimentation

UseCaseExamples

●Experimentation/RapidPrototyping

●ContentGenerationTools

●TextSummarizationApplications

●Question-AnsweringSystems

●LanguageTranslationTools

●ChatbotsandVirtualAssistants

SecurityChallenges

●Prompt-basedapplicationsfacesecurityriskslikepromptinjectionattacksanddataleakagefrompoorlycraftedprompts.Lackofcontextorstatemanagementcanleadtounintendedoutputs,

increasingmisusevulnerability.User-generatedpromptsmaycauseinconsistentorbiasedresponses,riskingcomplianceorethicalviolations.Ensuringpromptintegrity,robustinputvalidation,andsecuringtheLLMenvironmentarecrucialtomitigatetheserisks.

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AgenticApplications

TheseapplicationsleverageLargeLanguageModels(LLMs)toautonomouslyorsemi-autonomouslyperformtasks,makedecisions,andinteractwithusersorothersystems.Theseagentsaredesignedtoactonbehalfofusers,handlingcomplexprocessesthatofteninvolvemultiplesteps,integrations,andreal-time

decision-making.Theyoperatewithalevelofautonomy,allowingthemtocompletetaskswithoutconstanthumanintervention.

KeyCharacteristics

●AutonomyandDecision-Making

●InteractionwithExternalSystems

●StateManagementandMemory

●ComplexWorkflowAutomation

●Human-AgentCollaboration

UseCaseExamples

●VirtualAssistants

●CustomerSupportBots

●ProcessAutomationAgents

●DataAnalysisandReportingAgents

●IntelligentPersonalizationAgents

●SecurityandComplianceAgents

SecurityChallenges

●Agentapplications,withtheirautonomyandaccesstovarioussystems,mustbecarefullysecuredtopreventmisuse.Theyfacesecuritychallengeslikeunauthorizedaccess,increasedexploitationrisksduetointeractionwithmultiplesystems,andvulnerabilitiesindecision-makingprocesses.If

someonegainscontrolofanautonomousagent,theconsequencescouldbesevere,especiallyin

criticalsystems.Ensuringrobustaccesscontrolsandencryptionmethodstoprotectagainstthisisessential.Ensuringdataintegrityandconfidentialityiscritical,asagentsoftenhandlesensitive

informationitisimportanttosecuredataatallstages,includingat-rest,inmotion,andaccess

throughsecuredAPIs.Theirautonomyalsoposesrisksofunintendedorharmfuldecisionswithoutoversight.Robustauthentication,encryption,monitoring,andfail-safemechanismsareessentialtomitigatethesesecurityrisks.ObservabilityandTraceabilitysolutionsthatmonitortheentire

lifecycleoftheAgents(Design,Development,Deployment,andVisibilityondecision-making)mustbeconsideredtoensurereal-timecorrectionsusingahumans-in-the-loopprocesscanbeenforced.

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LLMPlug-ins,Extensions

Plug-insareextensionsoradd-onsthatintegrateLLMsintoexistingapplicationsorplatforms,enablingthem

toprovideenhancedornewfunctionalities.Plug-instypicallyserveasabridgebetweentheLLMandtheapplication,facilitatingseamlessintegration,suchasaddingalanguagemodeltoawordprocessorfor

grammarcorrectionorintegratingwithcustomerrelationshipmanagement(CRM)systemsforautomatedemailresponses.

WhileitcanbesometimesdifficulttodrawthelinebetweenAgentsandplug-insorextensionswhichareoftencomponentsoflargerapplications,onemeasureisthewayitisdeployedandused.Forexample,a

plug-inwouldbeapre-builtagentdesignedforreusethatyoucallexplicitly,throughanAPI,oraspartofanLLMspluginorextensionframeworkvs.customcoderunninginthebackgroundonaperiodicbasis.

KeyCharacteristics

●ModularityandFlexibility

●SeamlessIntegration

●TaskSpecificFocus

●EaseofDeploymentandUse

●RapidUpdatesandMaintenance

UseCaseExamples

●ContentGenerationTools

●TextSummarizationApplications

SecurityChallenges

●Pluginsinteractingwithsensitivedataorcriticalsystemsmustbecarefullyvettedforsecurity

vulnerabilities.Poorlydesignedormaliciouspluginscancausedatabreachesorunauthorized

access.LLMpluginsfacechallengeslikecompatibilityissues,whereupdatescanintroduce

vulnerabilities,andintegrationwithsensitivesystemsincreasestheriskofdataleaks.Ensuring

secureAPIinteractions,regularupdates,androbustaccesscontrolsiscrucial.Resource-intensivepluginsmaydegradeperformance,riskingexploitation.

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ComplexApplications

ComplexapplicationsaresophisticatedsoftwaresystemsthatdeeplyintegrateLargeLanguageModels

(LLMs)asacentralcomponenttoprovideadvancedfunctionalitiesandsolutions.Theseapplicationsare

characterizedbytheircomprehensivescope,scalability,andtheintegrationofmultipletechnologiesandcomponents.Theyaretypicallydesignedtosolveintricateproblems,ofteninenterpriseenvironments,andrequireextensivedevelopment,engineering,andongoingmaintenanceefforts.

KeyCharacteristics

●Multi-componentarchitecturesaredesignedtoprocesspromptsfromothernon-humansystems.

●Oftenusemultipleintegrations,includingothermodels.

●Multi-ComponentArchitecture

●ScalabilityandPerformance

●AdvancedFeaturesandCustomization

●End-to-EndWorkflowAutomation

UseCaseExamples

●LegalDocumentAnalysisPlatforms

●AutomatedFinancialReportingSystems

●CustomerServicePlatforms

●HealthcareDiagnostics

SecurityChallenges

●ComplexLLMapplicationsfacemajorsecuritychallengesduetotheirintegrationwithmultiple

systemsandextensivedatahandling.TheseincludeAPIvulnerabilities,databreaches,and

adversarialattacks.Thecomplexityincreasestheriskofmisconfigurations,leadingtounauthorizedaccessordataleaks.Managingcomplianceacrosscomponentsisalsodifficult.Robustencryption,accesscontrols,regularsecurityaudits,andcomprehensivemonitoringareessentialtoprotect

theseapplicationsfromsophisticatedthreatsandensuredatasecurity.

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LLMDevelopmentandConsumptionModels

OneofthefirstconsiderationsforanorganizationisdecidingupontheapproachtoleveragingLLM

capabilitiesbasedonthetypeofapplicationandgoalsfortheproject.Today,developershaveachoiceoftwoprimarydeploymentmodelswhenimplementingLLM-basedapplicationsandsystems.

CreateaNewModel:ThetrainingprocessforcustomLLMsisintensive,ofteninvolvingdomain-specificdatasetsandextensivefine-tuningtoachievedesiredperformancelevels.ThisapproachismoreakintoMLOpsbuildingMLmodelsfromthegroundup,withdetaileddataanalysis,collectionformatting,cleaning,andlabeling.Oneofthebenefitsofthisapproachisthatyouknowthelineageandsourceofthedatathemodelisbuiltonandcanattestdirectlytoitsvalidityandfit.However,amajordownsideistheresources,cost,andexpertisenecessarytobuild,train,andverifyamodelthatmeetstheprojectobjectives.CustomLLMsprovidetailoredsolutionsoptimizedforspecifictasksanddomains,offeringhigheraccuracyand

alignmentwithanorganization'sspecificneeds.

ConsumeandCustomizeExistingModels:Pre-trained(foundation)models,whetherself-hostedorofferedasaservice,suchaswithChatGPT,Bertandothersontheotherhandprovideamoreaccessibleentrypointfororganizations.ThesemodelscanbequicklydeployedviaAPIs,allowingforrapidsolutionvalidationand

integrationintoexistingsystems.TheLLMOpsprocessinthisscenarioemphasizescustomizationthrough

fine-tuningwithspecificdatasets,ensuringthemodelmeetstheapplication'suniquerequirements,followedbyrobustdeploymentandmonitoringtomaintainperformanceandsecurity.

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LLMOpsandLLMSecOpsDefined

HavingacommonviewoftypicalLLMapplicationarchitectures,includingagents,models,LLMs,andtheMLapplicationstack,iscrucialfordefiningandaligningtheapplicationstackandsecuritymodel.

(figure:LLMOpsrelatedOperationsProcessforData,MachineLearningandDevOps)

AQuickOpsPrimer-FoundationforLLMOps

DevOps,whichemphasizescollaboration,automation,andcontinuousintegrationanddeployment(CI/CD),haslaidthegroundworkforefficientsoftwaredevelopmentandoperations.Bystreamliningthesoftwaredevelopmentlifecycle,DevOpsenablesrapidandreliabledeliveryofapplications,fosteringacultureof

collaborationbetweendevelopmentandoperationsteams.

DataOpsbuildsonDevOps,wheredatapipelinesaremanagedwithsimilarautomation,versioncontrol,andcontinuousmonitoring,ensuringdataqualityandcomplianceacrossthedatalifecycle.MLOpsalsoextendstheDevOpsprinciplestomachinelearning,focusingontheuniquechallengesofmodeldevelopment,

training,deployment,andmonitoring.UtilizingDevOpsasafoundationensuresthatbothDataOpsandMLOpsinheritarobustinfrastructurethatprioritizesefficiency,scalability,security,andfasterinnovationin

data-drivenandmachinelearningapplications.

MLOpsandDataOpsarefoundationaltoLLMOpsbecausetheyestablishthecriticalprocessesand

infrastructureneededformanagingthelifecycleoflargelanguagemodels(LLMs).DataOpsensuresthatdatapipelinesareefficientlymanaged,fromdatacollectionandpreparationtostorageandretrieval,providing

high-quality,consistent,andsecuredatathatLLMsrelyonfortrainingandinference.MLOpsextendsthese

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principlesbyautomatingandorchestratingthemachinelearninglifecycle,includingmodeldevelopment,training,deployment,andmonitoring.

LLMOpsandMLOps,whilerootedinthesamefoundationalprinciplesoflifecyclemanagement,diverge

significantlyintheirfocusandrequirementsduetothespecificdemandsoflargelanguagemodels(LLMs).

LLMOpsencompassesthecomplexitiesoftraining,deploying,andmanagingLLMs,whichrequiresubstantialcomputationalresourcesandsophisticatedhandling.LLMOpsensurethatLLMsareefficientlyintegrated

intoproductionenvironments,monitoredforperformanceandbiases,andupdatedasneededtomaintain

theireffectiveness.ThisholisticapproachensuresthatthedeploymentandoperationofLLMsare

streamlined,scalable,andsecure,includingconsiderationsfordatavalidationandprovenancetoensurethatthedatausedfortrainingandfine-tuningLLMsistrustworthyandfreefromtampering.Thiscaninclude

techniquesfordataauditingandverification.

LLMOpsLifeCycleStages-Foundation