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OWASP answers what can go wrong inside an LLM application; the NIST AI Risk Management Framework (AI RMF) explains how an organization should understand, measure, govern, and treat AI risk over the system lifecycle. They are complementary rather than competing standards. Use the OWASP Top 10 for LLM Applications as a concrete security-risk inventory, then use NIST AI RMF processes to assign context, owners, measurements, remediation decisions, documentation, and ongoing oversight.
The short answer: security taxonomy versus risk-management system
The OWASP Top 10 for LLM Applications 2025 is a focused taxonomy of ten application-security risks that arise when products use large language models, retrieval, tools, agents, embeddings, and generated output. It is most useful to developers, architects, application-security teams, red teams, and product-security leaders who need to identify attack surfaces and design or test controls.
NIST AI RMF 1.0 is voluntary, non-sector-specific, rights-preserving, and use-case-agnostic guidance for organizations that design, develop, deploy, or use AI systems. Its purpose is not to enumerate only technical vulnerabilities. It organizes the work of understanding a system and its affected people, measuring risk and control performance, deciding how to treat risk, and keeping accountability active throughout the lifecycle.
In practical terms, OWASP supplies the technical questions for an LLM feature: Can instructions be injected? Can a connected tool be misused? Can retrieved data leak? Can an attacker consume unlimited resources? NIST supplies the management loop around those questions: What is the intended use, who could be affected, who owns the decision, what evidence shows the control works, what residual risk is accepted, and how will the system be monitored after release?
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Side-by-side comparison
| Axis | OWASP Top 10 for LLM Applications | NIST AI RMF |
|---|---|---|
| Primary job | Name and explain major LLM application security risks. | Organize enterprise AI risk-management work and accountability. |
| Granularity | Ten named risks with technical attack and mitigation detail. | Functions, categories, subcategories, outcomes, and suggested actions. |
| Main users | Application security, product security, developers, architects, and red teams. | Executives, risk and compliance teams, product owners, engineers, legal teams, and affected stakeholders. |
| Lifecycle | Especially valuable during design, implementation, testing, and operation of LLM features. | Applies across framing, development, deployment, use, evaluation, and monitoring. |
| Evidence | Threat models, secure-design decisions, abuse cases, tests, logs, access controls, and output validation. | Context records, impact assessments, metrics, owners, treatment decisions, monitoring, and residual-risk records. |
| Best combined use | Provide the technical risk inventory. | Map that inventory to context, measurement, decisions, accountability, and continuous oversight. |
What the OWASP Top 10 for LLM Applications covers
OWASP published the 2025 Top 10 for LLM Applications on November 18, 2024. The OWASP GenAI Security Project launched in May 2023 and describes the update as part of a broader effort that includes a cybersecurity and governance checklist and a solutions landscape for security leaders. The Top 10 itself is application-facing: it turns common failure modes into named categories that can be used in architecture reviews, threat models, abuse cases, security testing, and engineering backlogs.
LLM01:25 Prompt Injection
Prompt injection occurs when an attacker-controlled instruction changes how the model interprets or follows the intended instructions. The hostile text can arrive directly from a user or indirectly through retrieved documents, web pages, messages, or tool output. Review trust boundaries between system instructions, developer instructions, user content, and external data. Test whether untrusted content can override policy, cause unauthorized tool calls, or alter the model’s final response.
LLM02:25 Sensitive Information Disclosure
This risk covers unintended exposure of confidential data through prompts, retrieval results, model responses, logs, traces, error messages, or connected services. Identify which data classes the application can receive and return, enforce least-privilege access before retrieval, redact where appropriate, and test both direct requests and indirect extraction attempts. Logging and support workflows need the same data-handling rules as the user interface.
LLM03:25 Supply Chain
LLM applications depend on more than a model. They may include model providers, plugins, libraries, embedding models, vector stores, datasets, evaluation packages, and hosted services. A weakness or compromise in one dependency can affect the application. Maintain an inventory of model and software components, evaluate provenance and update paths, review permissions, and test changes before they reach production.
LLM04:25 Data and Model Poisoning
Poisoning introduces malicious or misleading content into training data, fine-tuning data, retrieval indexes, feedback pipelines, or model artifacts. The result can be biased, unsafe, or targeted behavior that is difficult to notice in ordinary tests. Protect data-ingestion paths, record provenance, validate changes, separate approval from submission, and include adversarial or outlier cases in evaluation.
LLM05:25 Improper Output Handling
Generated text, code, structured data, or tool arguments must be treated as untrusted until validated. Passing model output directly into a browser, database query, shell, workflow engine, or downstream API can turn a language error into a conventional security vulnerability. Use strict schemas, escaping, allowlists, authorization checks, and human or service-level confirmation for consequential actions.
LLM06:25 Excessive Agency
An LLM becomes excessively agentic when it can take actions, access data, or call tools beyond what the use case requires. Limit tools and permissions to the smallest useful set, isolate high-impact operations, constrain destinations and parameters, and require confirmation or independent policy checks for irreversible actions. Record which identity performed each action so an incident can be investigated.
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System prompts can contain instructions, business logic, internal identifiers, or other information that developers mistakenly treat as a secret boundary. Users may induce the model to reveal portions of those instructions. Keep secrets and authorization decisions outside the prompt, assume prompt text can become known, and rely on enforceable controls rather than concealment.
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LLM08:25 Vector and Embedding Weaknesses
Retrieval-augmented applications introduce risks in chunking, indexing, embedding generation, similarity search, metadata filters, and tenant separation. Weaknesses can cause cross-user retrieval, malicious documents to outrank trusted content, or sensitive fragments to be returned to an unauthorized requester. Test access filters independently of semantic similarity, validate index updates, and monitor retrieval quality and isolation.
LLM09:25 Misinformation
Misinformation is the production or amplification of false, fabricated, or unjustifiably confident content. It is a security and reliability concern when people rely on responses for decisions. Define the claims the system is allowed to make, provide provenance or citations where the product requires them, measure factual performance for the intended use, and route high-impact cases to qualified review.
LLM10:25 Unbounded Consumption
Unbounded consumption covers uncontrolled use of tokens, compute, model calls, storage, or external services. It can create denial-of-service conditions, runaway costs, degraded availability, or resource exhaustion. Set limits for request size, output length, recursion, tool calls, concurrency, and spend; apply rate controls; and alert on anomalous usage.
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What NIST AI RMF covers
NIST released AI RMF 1.0 on January 26, 2023. It is voluntary guidance for organizations designing, developing, deploying, or using AI systems. NIST describes it as rights-preserving, non-sector-specific, and use-case-agnostic, so it can frame governance for a customer-support assistant, an internal coding tool, a decision-support system, or another AI deployment without replacing domain-specific law or technical security practices.
The AI RMF Core has four functions. GOVERN is cross-cutting and informs the other three; MAP establishes context and identifies risks; MEASURE assesses, analyzes, or tracks risks and controls; and MANAGE prioritizes and acts on risks. NIST says this work should be continuous throughout the AI system lifecycle rather than a one-time approval exercise.
GOVERN: establish accountability and operating rules
GOVERN sets policies, roles, responsibilities, authority, documentation expectations, and escalation paths. It connects AI risk decisions to organizational values, legal obligations, procurement, security, privacy, and stakeholder participation. For an LLM application, GOVERN determines who can approve a model or data source, who owns a risk exception, how incidents are escalated, and what evidence must exist before and after release.
MAP: understand the system and its context
MAP records intended purpose, users, deployment environment, data flows, dependencies, affected people, foreseeable misuse, and potential impacts. It is where a team decides which OWASP risks are relevant and how serious they could be in this particular use case. A public chatbot, an employee assistant, and an agent that can alter customer records should not receive the same risk rating merely because they use similar models.
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MEASURE: produce evidence
MEASURE turns risk statements into evaluation activities and metrics. Depending on the system, evidence can include prompt-injection tests, access-control tests, retrieval-isolation checks, output-schema validation rates, hallucination or misinformation evaluations, latency and resource-use telemetry, red-team findings, incident trends, and human-impact assessments. The important point is traceability: a metric should connect to a stated risk, control, owner, and decision threshold.
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MANAGE uses the context and evidence to choose actions. A team may mitigate a risk through architecture or controls, transfer part of it through a contract, avoid a use case, or accept residual risk with an accountable decision-maker. MANAGE also covers tracking remediation, verifying that fixes work, monitoring changes, and revisiting priorities when the model, data, tools, users, or operating environment changes.
The AI RMF Playbook
The AI RMF Playbook adds suggested actions, references, and documentation practices for achieving outcomes under the four functions. It is voluntary, like the framework itself. Organizations can adapt its suggestions to their size, sector, risk tolerance, and existing security or compliance processes rather than treating it as a mandatory certification checklist.
Why the frameworks are not substitutes
Using only OWASP can produce a technically sharp list of vulnerabilities without a durable decision process. A team may identify prompt injection but fail to document intended use, affected stakeholders, ownership, residual risk, or post-release monitoring. Using only NIST can produce a well-organized governance program whose risk register is too abstract to guide engineers toward concrete tests and controls for retrieval, tools, model output, or resource limits.
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A practical OWASP-to-NIST operating model
The mapping below is an implementation pattern, not an official crosswalk published by either organization. It shows how to turn each OWASP item into work that fits the NIST functions.
| OWASP risk | MAP: context questions | MEASURE: evidence to collect | MANAGE and GOVERN actions |
|---|---|---|---|
| Prompt Injection | Which inputs and retrieved sources are untrusted? What actions could an injected instruction trigger? | Adversarial prompt tests, tool-call policy tests, and logs of blocked or allowed instructions. | Separate instruction and data channels, limit tool permissions, assign an owner for abuse-case remediation, and monitor new injection patterns. |
| Sensitive Information Disclosure | What confidential data can enter prompts, indexes, logs, or responses? Who is authorized to see it? | Data-flow reviews, authorization tests, redaction checks, and leakage tests across direct and indirect prompts. | Apply data classification and least privilege, define incident escalation, and record residual exposure decisions. |
| Supply Chain | Which models, libraries, datasets, indexes, plugins, and hosted services are dependencies? | Component inventory, provenance records, version-change tests, and supplier security evidence. | Set procurement and change-approval rules, define dependency owners, and maintain rollback or replacement plans. |
| Data and Model Poisoning | Who can submit or alter training, fine-tuning, feedback, or retrieval data? | Provenance checks, approval logs, data-quality tests, and evaluations for targeted behavior changes. | Restrict ingestion rights, require review for material changes, and track remediation of contaminated data or artifacts. |
| Improper Output Handling | Where does generated output go, and what downstream systems can interpret it as code or commands? | Schema and escaping tests, authorization tests, and negative tests for malformed or malicious output. | Enforce validation at every boundary, require confirmation for high-impact actions, and assign downstream-system owners. |
| Excessive Agency | Which tools, data, identities, and actions are necessary for the intended purpose? | Permission tests, action logs, sandbox tests, and checks for unauthorized parameter or destination changes. | Reduce privileges, isolate sensitive operations, define approval thresholds, and review exceptions under GOVERN. |
| System Prompt Leakage | Does the prompt contain secrets or controls that should be enforced elsewhere? | Extraction tests and reviews confirming that authorization does not depend on prompt secrecy. | Move secrets and policy enforcement into protected services, document prompt exposure assumptions, and monitor disclosures. |
| Vector and Embedding Weaknesses | How are tenants, metadata filters, index updates, and document trust levels separated? | Cross-tenant retrieval tests, poisoning tests, filter-bypass tests, and retrieval-quality measurements. | Enforce authorization outside similarity search, validate index changes, and assign data-store ownership. |
| Misinformation | Which claims and decisions are within scope, and who could be harmed by an incorrect answer? | Task-specific factual evaluations, confidence or abstention checks, provenance checks, and human-review outcomes. | Constrain use cases, add review for high-impact decisions, communicate limitations, and monitor incident patterns. |
| Unbounded Consumption | What resources can each request, workflow, user, or agent consume? | Token, latency, concurrency, tool-call, storage, and cost telemetry; load and abuse tests. | Set quotas and rate limits, define availability and spend thresholds, and maintain an incident response path. |
Step-by-step implementation for a real LLM feature
1. Inventory the complete system
Start with the feature, not only the model. Record the model version or provider, prompts, user interfaces, retrieval stores, embeddings, datasets, plugins, tools, downstream APIs, logs, monitoring, and human-review points. Identify where data enters, is transformed, stored, displayed, or acted upon. This inventory gives MAP a concrete system boundary and gives OWASP risks a place to attach.
2. Write the intended use and foreseeable misuse
State what the feature is allowed to do, who uses it, what decisions it supports, and what it must not do. Add foreseeable misuse cases: attempts to extract data, manipulate instructions, invoke tools outside policy, poison a knowledge base, or exhaust resources. Record affected parties, including people who may never directly use the interface but may be affected by generated recommendations or actions.
3. Create an OWASP-based risk register
Use the ten OWASP categories as prompts for a system-specific inventory. For each applicable risk, record the attack path, assets, preconditions, potential impact, existing controls, owner, test status, and residual risk. Mark an item not applicable only after documenting why the architecture or use case excludes it; do not omit a category simply because it is inconvenient to test.
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4. Define measurable control objectives
Translate each risk into an observable result. Examples include denying unauthorized tool calls, preventing cross-tenant retrieval, rejecting malformed output, limiting recursive agent steps, detecting abnormal consumption, or routing uncertain high-impact answers to review. Choose measurements that can be repeated after model, prompt, data, dependency, or policy changes.
5. Test before release
Combine automated tests, manual adversarial testing, code review, architecture review, and—where appropriate—independent red-team exercises. Test both normal and hostile inputs, including indirect instructions in retrieved content and malformed outputs sent to downstream systems. Keep test cases, versions, findings, and decisions as evidence under MEASURE.
6. Assign treatment and acceptance decisions
For every material finding, MANAGE should record whether the team will mitigate, avoid, transfer, or accept the risk. Acceptance needs an accountable owner, a stated rationale, a review condition, and monitoring requirements. A release approval without a named owner or residual-risk record is not durable governance.
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7. Operate with telemetry and escalation
Monitor security and reliability signals after deployment: blocked prompts, unusual retrievals, permission denials, output-validation failures, tool actions, resource consumption, reported misinformation, and dependency changes. Protect logs because they may contain sensitive prompts or responses. Define how operators pause a workflow, revoke a tool, roll back a model or index, and notify affected stakeholders.
8. Reassess on material change
Repeat the cycle when the model, system prompt, retrieval corpus, embedding model, tool permission, user population, deployment region, or intended use changes. A model update can alter prompt-injection behavior, factual performance, output format, resource consumption, or data handling even when application code is unchanged.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which framework should you start with?
| Your immediate need | Best starting point | What to do next |
|---|---|---|
| You are designing or testing an LLM feature. | OWASP Top 10 for LLM Applications. | Build the threat model and test the applicable risks, then document context and ownership with NIST MAP and GOVERN. |
| You need organization-wide AI accountability. | NIST AI RMF. | Establish GOVERN and MAP processes, then use OWASP to give engineering teams concrete LLM risks and tests. |
| You operate an agent with sensitive data or powerful tools. | Use both immediately. | Use OWASP for attack-surface analysis and NIST for authorization, measurement, residual-risk decisions, and monitoring. |
| You are evaluating a non-LLM AI system. | NIST AI RMF. | Use domain-specific security taxonomies where relevant; OWASP’s LLM list will not cover every AI architecture. |
Common mistakes and how to correct them
Treating the OWASP list as a checkbox
A list of ten risks is not a threat model. Correct the mistake by connecting each applicable item to a real data flow, privilege boundary, abuse case, test, owner, and remediation record.
Treating NIST AI RMF as a certification or a security control catalog
AI RMF 1.0 is voluntary guidance, not a certification and not a substitute for secure coding, access control, privacy safeguards, incident response, or sector-specific requirements. Use its functions to organize those activities and make decisions traceable.
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GOVERN is cross-cutting. Policies, roles, escalation paths, and accountability must remain active while the system changes and operates. A signed launch document cannot replace monitoring or reassessment.
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Relying on prompt secrecy
System prompt leakage demonstrates why hidden instructions should not be treated as an authorization boundary. Keep secrets in protected services and enforce permissions in code or policy layers that do not depend on model compliance.
Testing only the model response
Many serious failures occur around the model: retrieval filters, tool permissions, parsers, logs, indexes, dependencies, and resource controls. Include the complete application path in security and risk tests.
Ignoring residual risk
Controls rarely eliminate every failure mode. Record what remains, who accepted it, what evidence supports the decision, and which event or change will trigger another review. This makes uncertainty visible instead of allowing it to disappear in a launch checklist.
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NIST reported that development of the AI RMF received about 400 formal comment sets from more than 240 organizations. Deputy Commerce Secretary Don Graves described the goal this way: “This voluntary framework will help develop and deploy AI technologies in ways that enable the United States, other nations and organizations to enhance AI trustworthiness while managing risks based on our democratic values.”
The voluntary status means organizations can tailor the framework to their risk, sector, and maturity. It does not mean the work is optional for a responsible deployment; it means NIST provides a flexible structure rather than imposing one universal control set. Technical teams still need concrete safeguards, and OWASP helps make those safeguards specific for LLM applications.
How to explain the difference to stakeholders
For executives, say that OWASP identifies the LLM security exposure while NIST creates the accountability and decision process around it. For engineers, say that OWASP provides the attack categories and NIST tells the team how to document context, measure controls, prioritize fixes, and monitor the result. For risk and legal teams, say that NIST provides the lifecycle structure while OWASP supplies concrete evidence about application threats. For auditors or customers, present the risk register, test results, owners, treatment decisions, and monitoring records together rather than claiming that adoption of one document proves security.
The strongest implementation is therefore a traceable chain: an OWASP risk is tied to a system component and abuse case; NIST MAP records context and affected parties; MEASURE records tests and outcomes; MANAGE records treatment and residual risk; and GOVERN keeps authority, policy, documentation, and monitoring in place as the system evolves.
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