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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsMeasure AI security with repeatable tests that reflect the system’s actual use: combine model testing, adversarial red teaming, and user or field testing, then record the scenarios, conditions, tools, metrics, and results. A benchmark score is evidence about what happened under its test conditions—not proof that a model or deployed AI system is secure in general.
What a credible AI security evaluation measures
Security is not one property that a single score can capture. The relevant risks depend on how an AI system is used and its social and operational context, as the NIST AI Risk Management Framework (AI RMF) Playbook emphasizes. A model tested on its own may behave differently once connected to tools, private data, users, or external content. An evaluation should therefore cover the system and conditions that matter to the intended deployment.
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NIST’s September 18, 2026, ARIA Evaluation Planning Manual describes three kinds of testing that work together:
| Evaluation layer | What it examines | What it can contribute |
|---|---|---|
| Model testing | Model behavior on defined tests and conditions | Evidence about specified capabilities, weaknesses, and risks at the model level |
| Red teaming | Adversarial or stress conditions designed to expose failure modes | Examples of how attacks may succeed and what consequences they could produce |
| User testing | How people interact with and experience the AI system | Evidence about system behavior in user-facing situations |
The manual calls this combination “Model Testing, Red Teaming, and User Testing.” NIST’s 2025 ARIA 0.1 pilot report instead describes model testing, red teaming, and field testing. Those labels reflect the wording of the respective documents; they should not be treated as interchangeable definitions of a universal test protocol.
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How to plan a repeatable evaluation
Plan around the system you intend to assess, not an abstract idea of a secure AI model. NIST’s AI RMF Playbook advises selecting measures for mapped risks, documenting test materials and metrics, and using red-team exercises to probe adversarial or stress conditions.
- Define the target and context. Record which system is under test, its intended use, the users and operating conditions in scope, and the tools, data sources, and integrations it can access. Specify whether the evaluation concerns a standalone model or a deployed application or agent.
- Map plausible harms to test scenarios. Choose scenarios based on the system’s capabilities and access. For an agent, that could include malicious instructions hidden in external content, attempts to trigger an unauthorized tool action, or an effort to expose sensitive data.
- Specify test materials and conditions. Preserve the test cases, relevant inputs, system configuration, tools, procedures, and conditions well enough for another evaluator to understand what was tested and reproduce it. State what was not in scope.
- Choose measures before running tests. Select metrics that match the risks. Record the measurement method, not just the resulting score, and explain how failures are classified and their consequences assessed.
- Combine evaluation layers. Use model testing, adversarial red teaming, and user or field testing as appropriate to the question. A test that omits a relevant layer should say so rather than implying comprehensive coverage.
- Report results and limits. Document findings, test coverage, conditions, tools, and what could or could not be measured. NIST recommends recording measurement limitations so readers can interpret results without overgeneralizing.
- Re-evaluate when conditions change. Refresh scenarios and repeat relevant tests after changes to the model, tools, data access, integrations, operating context, or threat environment.
Which metrics are useful—and what they mean
The NIST AI RMF Playbook gives examples of security measures: red-teaming activities, the frequency and rate of anomalous events, system downtime, incident-response times, and time-to-bypass. These are options to select and adapt to a system’s risks, not a mandatory scorecard or a universal set of pass thresholds.
- Attack outcomes: Record whether a scenario succeeded and what happened, rather than counting an attempted attack as equivalent to a harmful outcome.
- Failure consequences: Describe the severity and operational effect of a failure. A blocked prompt and an unauthorized external action should not be treated as equivalent merely because each is one test result.
- Operational resilience: Where relevant, track anomalous-event rates, downtime, incident-response time, and time-to-bypass. Define how each measure is calculated and the observation period.
- Coverage and transfer: Note which scenarios were tested and whether observed attacks also work against other models or in other contexts. Transfer should be tested rather than assumed.
Comparisons are most informative when candidates face the same relevant scenarios and test conditions. Report attack success by scenario, consequences, coverage of the deployed system’s tools and data, operational resilience, and transparency about materials and limitations. The available evidence does not establish a universal aggregate security score or a weighting scheme for combining these dimensions.
How to test an AI agent for prompt injection
For an agent, the security boundary includes more than the model’s direct conversation with a user. NIST describes indirect prompt injection through external data such as emails, websites, and code repositories. Depending on system access and behavior, resulting failures can include unintended actions, sensitive-data exfiltration, or malicious-code execution.
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- Identify what the agent can read and do. Inventory relevant external content sources, connected tools, permissions, and the data available to the agent. Choose scenarios that reflect those actual pathways.
- Place hostile instructions in realistic external content. Test material can represent an email, web page, or code repository containing instructions that conflict with the user’s task or attempt to redirect the agent. Keep the content and conditions documented.
- Check the full chain of behavior. Observe whether the agent follows the hostile instruction, selects an unsafe tool, takes an unauthorized action, or exposes sensitive information. Record the outcome and its consequence, not merely whether the model produced suspicious text.
- Test across relevant scenarios and configurations. Vary the content source and task where appropriate, and assess the specific tools and data the deployment exposes. A successful defense in one scenario does not establish protection against other injection paths.
NIST’s AI Metrology Center lists an agent and tool-abuse testing method with examples such as unsafe tool selection and unauthorized actions. The Center is a discovery resource for measurement methods and tools organized around AI RMF characteristics and lifecycle stages; NIST says that inclusion in its catalog is not an endorsement, validation, or determination that a method is suitable for a particular use.
Why benchmarks and one-time red-team results are not proof
A benchmark describes performance on its own test material, scope, and conditions. It cannot by itself establish how a system will behave under untested scenarios, with different integrations, or against attacks that emerge later. NIST’s security and resilience overview says AI-specific security remains an active research area and that existing frameworks do not comprehensively address attacks such as evasion, model extraction, membership inference, and availability attacks.
Results can also change across models and scenarios. NIST’s 2026 account of a public competition reports that more than 400 participants made over 250,000 attack attempts against 13 frontier models; at least one successful attack was found against every target model. NIST also reports non-uniform transfer across models and scenarios. These counts describe that competition, not a general attack rate or a forecast for any particular deployment. The account underscores why a fixed test suite can become stale and why attack transfer should be measured rather than presumed.
NIST describes Dioptra as a testbed for research into AI vulnerabilities and the effectiveness of defenses. MITRE’s ATLAS is a living knowledge base of AI adversary tactics and techniques based on real-world observations and realistic demonstrations. Both can help evaluators develop or organize scenarios; neither replaces tests tailored to the system being assessed.
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What current NIST evaluation figures do—and do not—show
NIST’s 2025 ARIA 0.1 pilot involved five organizations and seven AI applications. Those figures describe the scope of that pilot; they are not a representative sample of all deployed AI systems or evidence that every system was evaluated in the same way.
NIST’s broader measurement and evaluation program webpage describes hundreds of evaluations of thousands of AI systems, but does not state a publication year for that summary. Treat it as a broad program description rather than a precisely dated annual count.
NIST’s September 18, 2026, ARIA planning manual is a current starting point for structuring a holistic evaluation. It provides planning guidance, not evidence that any specific system is safe. More generally, a named framework, catalog entry, benchmark, or testbed does not convert a bounded result into a security guarantee.
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