OWASP answers “what can go wrong inside an LLM application?” NIST answers “how should an organization identify, measure, assign, and manage AI risk throughout the system’s life?” They are complementary, not competing standards. Use the OWASP Top 10 for LLM Applications 2025 as a focused security inventory, then use the NIST AI Risk Management Framework (AI RMF) to put each risk in context, assign ownership, select measurements, document treatment decisions, and monitor residual risk.
The decision in one minute
The OWASP Top 10 for LLM Applications 2025 is a technical taxonomy for security teams building or assessing features that use large language models. It names ten application-facing risk classes, from prompt injection to unbounded consumption, and turns them into subjects for threat modeling, secure design, abuse-case testing, access control, validation, and logging.
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The NIST AI RMF 1.0 is a voluntary, rights-preserving, non-sector-specific and use-case-agnostic process for organizations that design, develop, deploy, or use AI. Its four functions—GOVERN, MAP, MEASURE, and MANAGE—organize accountability and decisions over the entire AI lifecycle. It does not replace application security controls or provide a list of LLM attacks.
If you can adopt only one first, choose OWASP for an engineering security review of a specific LLM feature. Choose NIST first when the immediate problem is enterprise accountability, impact assessment, risk ownership, policy, or oversight across many AI systems. A defensible program uses both: OWASP supplies the concrete security findings; NIST supplies the operating model that decides what those findings mean and what happens next.
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What the OWASP Top 10 for LLM Applications is
OWASP’s 2025 list was published on November 18, 2024. The project began in May 2023 and describes the 2025 update, a cybersecurity and governance checklist, and a solutions landscape for security leaders. The list is deliberately application-facing: it helps an architect or tester ask where an LLM feature can be attacked, misused, or made unreliable.
These are risk categories, not a certification checklist and not a ranking of the ten most severe incidents in every deployment. A single application may expose several categories at once—for example, a retrieval-augmented assistant can combine prompt injection, sensitive information disclosure, vector weaknesses, improper output handling, and excessive agency.
LLM01:25 Prompt Injection
An attacker supplies instructions that alter the model’s behavior or override the application’s intended instructions. The payload can arrive in a user message, retrieved document, web page, file, tool result, or other content the model is asked to process. Treat all untrusted content as data, separate instructions from data where the architecture allows, constrain tools, and test direct and indirect injection paths.
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LLM02:25 Sensitive Information Disclosure
The application exposes confidential, personal, proprietary, or otherwise restricted information through model responses, retrieval, logs, prompts, memory, or connected tools. Define which identities may access which data, minimize what is sent to the model, filter retrieval results by authorization, and inspect both normal and adversarial outputs for leakage.
LLM03:25 Supply Chain
Risk enters through models, datasets, plugins, libraries, hosted APIs, containers, evaluation assets, or other third-party components. Record provenance and versions, review providers and dependencies, control where artifacts come from, and make it possible to replace or disable a compromised component.
LLM04:25 Data and Model Poisoning
Malicious or low-quality training, fine-tuning, retrieval, or evaluation data changes a model or its behavior. Protect data-ingestion paths, preserve provenance, validate and review changes, separate development and production sources, and retain enough history to investigate when behavior shifts.
LLM05:25 Improper Output Handling
Downstream code trusts model output as if it were safe, authoritative, or correctly formatted. A response rendered as HTML, passed to a database, used in a command, or sent to another system needs ordinary application validation, encoding, authorization, and error handling. Do not make the model the final security boundary.
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The application gives the model too much ability to act, too many tools, excessive permissions, or an overly broad operating scope. Limit tools and arguments, use least-privilege identities, require confirmation for consequential actions, enforce rate and transaction limits, and keep an auditable record of requests, decisions, and results.
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LLM07:25 System Prompt Leakage
System instructions or hidden configuration are revealed through conversation, debugging, logs, or crafted requests. Avoid placing secrets in prompts, assume instructions may become known, enforce security in code and policy layers, and classify prompt content so that disclosure does not become a privilege escalation.
LLM08:25 Vector and Embedding Weaknesses
Embedding and vector-search design can create unauthorized retrieval, cross-tenant exposure, manipulated rankings, or poor isolation between trusted and untrusted material. Apply access control before and after retrieval, isolate tenants and collections, validate ingestion, monitor unusual search behavior, and test whether an attacker can influence what context is returned.
LLM09:25 Misinformation
The system produces false, unsupported, misleading, or overconfident information. Define the consequences of an incorrect answer, test factuality and grounding for the intended use, show uncertainty where appropriate, provide escalation or human review for high-impact decisions, and monitor real-world feedback rather than treating a fluent answer as evidence.
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LLM10:25 Unbounded Consumption
Unrestricted prompts, context, recursive tool calls, generated output, or concurrent requests can consume excessive tokens, compute, time, or money and may create denial-of-service conditions. Set request, context, output, recursion, and spending limits; enforce quotas and timeouts; and alert on abnormal consumption.
What the NIST AI RMF is
NIST released AI RMF 1.0 on January 26, 2023. It is guidance for organizations designing, developing, deploying, or using AI systems, rather than a sector-specific law or a product-security standard. NIST describes the framework as voluntary, rights-preserving, non-sector-specific, and use-case-agnostic. That broad scope lets one organization use the same vocabulary for a customer-service assistant, an internal decision-support model, and a high-impact automated workflow while still documenting their different consequences.
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 them; and MANAGE prioritizes and acts on them. NIST expects risk work to be continuous throughout the AI system lifecycle, not a one-time approval gate. The AI RMF Playbook adds suggested actions, references, and documentation practices for achieving the Core’s outcomes.
GOVERN: make accountability real
GOVERN establishes policies, roles, responsibilities, authority, stakeholder engagement, documentation expectations, and processes for handling residual risk. For an LLM application, that can mean naming the product owner, security owner, privacy owner, and incident authority; setting rules for human oversight and acceptable use; defining when a system must be paused; and requiring evidence that those decisions are followed. Because GOVERN is cross-cutting, it continues while the system is designed, changed, operated, and retired.
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MAP records the intended purpose, users, affected people, operating environment, data flows, dependencies, assumptions, and foreseeable harms. It is where a team distinguishes a low-consequence drafting assistant from an agent that can change records or make decisions about people. For LLM work, MAP should include model and provider boundaries, retrieval stores, tools, human handoffs, tenants, jurisdictions, and the kinds of failure that matter to each stakeholder.
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MEASURE: produce evidence
MEASURE turns risk questions into evaluations, metrics, tests, monitoring, and analysis. Evidence may include injection test results, authorization checks, leakage probes, output-validation tests, factuality or grounding evaluations, latency and consumption telemetry, incident rates, and review findings. A useful measurement states the population, test conditions, threshold, owner, date, and limitation; a single benchmark score is not a complete risk assessment.
MANAGE: decide and act
MANAGE uses context and evidence to prioritize controls, accept or transfer residual risk, remediate defects, communicate decisions, and monitor whether treatment remains effective. Actions can include redesigning a workflow, reducing tool permissions, adding a human approval step, changing a provider, restricting a use case, or stopping deployment. The decision should record who approved it, what evidence supported it, what remains unresolved, and when it will be revisited.
OWASP and NIST compared directly
| Axis | OWASP Top 10 for LLM Applications 2025 | NIST AI RMF 1.0 |
|---|---|---|
| Primary job | Name and explain major security risks in LLM applications. | 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 emphasis | Especially useful during design, implementation, testing, and operation of LLM features. | Applies across framing, development, deployment, use, evaluation, monitoring, and retirement. |
| Evidence expected | Threat models, secure-design decisions, abuse tests, logs, access controls, and output validation. | Context records, impact assessments, metrics, named owners, treatment decisions, monitoring, and residual-risk records. |
| What it does not do | It does not establish an organization-wide governance process or decide acceptable residual risk. | It does not enumerate LLM attack classes or replace technical security testing and controls. |
| Best combined use | Provide the technical risk inventory that feeds MAP and MEASURE. | Provide the context, ownership, measurement, treatment, and continuous oversight for OWASP findings. |
How to use both frameworks in one program
The following workflow keeps the frameworks in their proper roles and creates an auditable trail from a technical weakness to an organizational decision.
1. Inventory the LLM system and its attack surface
Start with a system record: purpose, users, model and provider, prompts, data sources, retrieval and embedding stores, tools, downstream systems, environments, tenants, human reviewers, and lifecycle status. Mark every place where untrusted input enters, where model output is interpreted, and where the system can read, write, spend, or trigger an external action.
Use the ten OWASP categories as an initial threat inventory. Do not mark a risk “absent” merely because a feature is not visible in the user interface; check the model context, retrieval pipeline, tool layer, logs, and administrative paths.
2. MAP the context and affected parties
For each OWASP risk that applies, document who could be harmed, what assets or rights are involved, the severity of an incorrect or unauthorized result, relevant legal or contractual constraints, and the assumptions behind the design. Record whether the system is advisory or action-taking, whether outputs affect people, and what human intervention is available.
For example, prompt injection in an internal summarizer may threaten confidentiality, while the same weakness in an agent that edits financial records may also threaten integrity, availability, and accountability. The OWASP label is the same; the NIST MAP context changes the priority and required controls.
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3. MEASURE controls and outcomes
Turn each risk into a testable claim. Examples include: unauthorized documents are not retrieved; model output cannot execute an unapproved command; system instructions do not contain secrets; an agent cannot exceed a transaction or spending limit; and answers in a defined evaluation set meet the application’s grounding or escalation requirement.
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Record test method, data set or scenario, pass threshold, date, environment, model version, and limitations. Combine pre-release tests with production telemetry because a control that passes a laboratory test can fail after a prompt, model, dependency, data source, or user population changes.
4. MANAGE by priority and treatment
Rank findings using the mapped impact, likelihood or exposure, exploitability, affected population, and confidence in the evidence. Select a treatment: mitigate, avoid, transfer, accept with an owner and expiry, or stop the use case. A mitigation is not complete until its effect is measured and the remaining risk is recorded.
5. GOVERN the decision and the operating rules
Assign decision rights before an incident. Define who can approve deployment, who can change prompts or tools, who reviews exceptions, who receives alerts, and who can disable the system. Set documentation, training, supplier, retention, privacy, and incident-response requirements. Review those rules whenever the system’s purpose, model, data, tools, or affected population changes.
6. Monitor and repeat after every material change
Lifecycle risk management means rerunning the relevant OWASP tests and NIST activities after model updates, prompt changes, new connectors, new retrieval data, dependency changes, policy changes, or unexpected incidents. Track residual risk, control drift, user complaints, security events, factuality failures, and resource consumption until retirement.
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The table is a starting point, not a substitute for a system-specific assessment. Each row shows how a technical risk can become a context question, an evidence plan, a treatment decision, and a governance requirement.
| OWASP risk | MAP question | MEASURE evidence | MANAGE and GOVERN action |
|---|---|---|---|
| Prompt Injection | Which inputs can influence instructions, retrieval, or tools, and what could they cause? | Direct and indirect injection scenarios, tool-boundary tests, and incident telemetry. | Reduce trust in untrusted content, constrain tools, assign an incident owner, and revisit after prompt or model changes. |
| Sensitive Information Disclosure | Which data is restricted, who may access it, and where can it appear? | Authorization tests, leakage probes, log and memory review, and access-monitoring results. | Minimize data, enforce authorization at retrieval and output boundaries, and set privacy and breach-response responsibilities. |
| Supply Chain | Which providers, models, datasets, libraries, and services are dependencies? | Inventory completeness, version and provenance records, supplier assessments, and change alerts. | Approve suppliers, pin or review versions, maintain replacement plans, and assign dependency risk ownership. |
| Data and Model Poisoning | Who can add or alter training, fine-tuning, retrieval, or evaluation data? | Integrity checks, provenance review, change history, and behavior-drift evaluations. | Restrict ingestion and approvals, quarantine suspect data, and define rollback authority. |
| Improper Output Handling | Where is model output parsed, rendered, executed, or used to make a decision? | Schema, encoding, authorization, error-path, and adversarial-output tests. | Keep validation outside the model, block unsafe actions, and make the downstream system owner accountable. |
| Excessive Agency | What can the model read, write, call, approve, or spend, and on whose identity? | Permission-boundary tests, approval-path tests, action logs, and transaction-limit monitoring. | Apply least privilege, confirmations, quotas, and a rapid disable path under named authority. |
| System Prompt Leakage | Would disclosure of hidden instructions reveal secrets or bypass a control? | Extraction attempts, secret scans, log reviews, and privilege-separation tests. | Remove secrets from prompts, enforce controls in code, and document disclosure impact and response. |
| Vector and Embedding Weaknesses | How are tenants, collections, metadata, and retrieval permissions separated? | Cross-tenant retrieval tests, poisoning scenarios, ranking checks, and access logs. | Enforce isolation before and after search, review ingestion, and assign data-store ownership. |
| Misinformation | Who may be harmed by an incorrect answer, and when is human review required? | Grounding or factuality evaluations, escalation rates, complaint analysis, and outcome monitoring. | Limit high-impact use, communicate uncertainty, add review, and set a stop-use threshold. |
| Unbounded Consumption | What resources can a request, loop, or tool chain consume? | Token, latency, concurrency, recursion, cost, and quota telemetry with alert thresholds. | Enforce limits and timeouts, define budget ownership, and prepare throttling or shutdown procedures. |
Which framework should each team start with?
Application security and engineering teams
Begin with OWASP when reviewing a new chatbot, retrieval system, copilot, or agent. Build a threat model around all ten categories, test the controls that apply, and attach findings to the application’s backlog. Then use NIST MAP and GOVERN to make sure the team has documented purpose, affected parties, owners, and escalation authority; use MEASURE and MANAGE to turn findings into tracked decisions.
Executives, risk, compliance, and legal teams
Begin with NIST when the challenge is a portfolio of AI systems, inconsistent ownership, unclear approval criteria, or undocumented impact. Require each system record to include an OWASP-based security inventory where LLMs are involved. This prevents a governance register from saying only “AI risk” while omitting concrete attack surfaces.
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Use NIST MAP to define the intended use, users, limitations, and handoffs before launch. Use OWASP to review prompts, retrieval, output handling, tools, and resource limits. Use NIST MEASURE and MANAGE for service-level monitoring, incident triage, customer communications, and decisions about whether to restrict or retire a feature.
Auditors and assessors
Ask for both kinds of evidence. OWASP evidence should show that relevant attack paths were considered and tested. NIST evidence should show why the system was approved, who accepted residual risk, how affected stakeholders were considered, what metrics are tracked, and how decisions change when evidence or context changes.
Evidence a combined program should retain
- System context: purpose, users, affected parties, data flows, model and provider versions, retrieval stores, tools, environments, and lifecycle status.
- Security analysis: OWASP risk applicability, threat model, abuse cases, trust boundaries, permissions, and known limitations.
- Test records: scenarios, test data, model version, environment, expected result, observed result, threshold, date, and reviewer.
- Operational records: access logs, tool calls, output-validation failures, injection alerts, retrieval anomalies, factuality escalations, incidents, and resource-consumption metrics.
- Decision records: treatment choice, residual risk, owner, approval authority, exceptions, expiry date, compensating controls, and review trigger.
- Change history: prompt, model, dependency, data, connector, policy, and user-population changes, with the tests rerun because of each change.
Common mistakes and how to correct them
Calling OWASP a complete AI governance framework
A ten-item security list cannot assign organizational accountability, define affected stakeholders, or decide whether a risk is acceptable. Correct this by placing the OWASP findings inside NIST GOVERN, MAP, MEASURE, and MANAGE records.
Using NIST as a replacement for technical testing
Framework language about trustworthy or managed AI does not demonstrate that an agent resists injection or that a tenant cannot retrieve another tenant’s documents. Correct this by attaching concrete OWASP-oriented tests and telemetry to the relevant NIST outcomes.
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GOVERN is cross-cutting, and risk work is continuous. Correct this by reopening context, measurements, and treatment decisions after material changes and incidents instead of filing the assessment once.
Measuring only model quality
Accuracy or benchmark scores do not establish authorization, secrecy, safe output handling, agency limits, or resource control. Correct this by measuring security, privacy, reliability, human-impact, and operational outcomes that match the system’s context.
Hiding security in the system prompt
A prompt is not a reliable authorization boundary, and its disclosure should not grant access. Correct this by implementing permissions, validation, isolation, quotas, and approval logic outside the model.
Recording risks without owners or deadlines
A finding with no accountable decision-maker is not managed risk. Correct this by naming an owner, treatment, evidence threshold, review date, and authority to pause or change the system.
A concise implementation checklist
- Register every LLM feature and describe its purpose, users, affected parties, data, tools, providers, and lifecycle stage.
- Screen the architecture against all ten OWASP 2025 categories and document why each category applies or does not apply.
- Map applicable risks to harms, rights, assets, stakeholders, legal or contractual constraints, and business consequences.
- Assign accountable owners for product, security, privacy, operations, suppliers, incident response, and residual-risk approval.
- Define measurable control claims and test them under realistic, adversarial, and production-like conditions.
- Set thresholds for escalation, human review, throttling, rollback, and shutdown before launch.
- Record treatment decisions, exceptions, compensating controls, residual risk, and review dates.
- Monitor incidents, access, tool actions, retrieval, output failures, factuality signals, and resource consumption.
- Repeat the assessment after model, prompt, data, connector, dependency, policy, or user-population changes.
Why the combination is stronger
OWASP makes an LLM application’s technical attack surface visible. NIST makes the organization explain why that surface matters, who is responsible, what evidence is sufficient, what trade-offs were accepted, and how the answer will change over time. Used together, they prevent two opposite failures: a technically detailed security review that no one is empowered to act on, and a high-level governance file that never tests the application’s real failure modes.
NIST reported that development of AI RMF 1.0 received about 400 formal comment sets from more than 240 organizations. Deputy Commerce Secretary Don Graves described the framework as voluntary guidance intended to help develop and deploy AI in ways that enhance trustworthiness while managing risk based on democratic values. Those facts explain the framework’s broad, consensus-oriented role; they do not turn it into a mandatory certification or a substitute for engineering controls.
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