Choose by testing each exact model version inside the application you plan to use—not by relying on a single benchmark or a model’s general reputation. Give candidates the same legitimate tasks, untrusted inputs, tools, permissions and safeguards; then measure whether they follow malicious instructions, how serious the resulting actions are, whether the attacker reaches their goal, and whether the model still completes legitimate work. A better result reduces one part of the risk; it does not prove an AI agent is safe.
What prompt-injection resistance means when you are choosing a model
Prompt injection is an attempt to make a model disregard its intended instructions and follow an attacker’s instructions instead. A direct attack appears in a message or other input intended to influence the model. An indirect attack is carried in material the model is asked to process, such as a web page, retrieved document, email or tool result. In an agent, that material can influence not only the answer but also tool use and actions taken on the user’s behalf.
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Resistance is therefore not just a question of whether the model produces an obviously unsafe sentence. A model may expose information, redirect a task, initiate a sensitive action or take steps toward an attacker’s objective without completing it. Conversely, a model that rejects every difficult request may appear resistant while failing at the legitimate task. Evaluate the model as part of the system that will actually receive the input and exercise the tools.
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This distinction matters when reading a test result. A chat-only evaluation may reveal whether a model follows an instruction in text, but it cannot establish how an agent with retrieval, private data or action-taking tools will behave. The application’s permissions and connected sources change both the attack surface and the potential consequences of a failure.
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Direct and indirect attacks test different paths
- Direct instruction override: try to persuade the model to ignore or replace higher-priority instructions through user input.
- Indirect injection: place malicious instructions in the kinds of content the application retrieves or processes, such as documents, pages, emails or tool output.
- Data disclosure: attempt to get the model to reveal sensitive information available to it, whether in its context or through connected sources.
- Action redirection: try to make it use a tool or take an action for the attacker’s purpose rather than the user’s.
These categories are a starting point, not a universal attack list. The relevant test cases depend on the application’s actual inputs, data access and available actions. A document-summary product should be tested with hostile documents; an agent that acts on tool results should be tested with the kinds of results it will actually receive.
Decide what a good result looks like before testing
Write down the intended task and the attacker’s objective for every test. For example, a legitimate task might be to summarize an untrusted document; the attacker’s objective might be to make the agent disclose information or perform an unauthorized action. This makes it possible to distinguish a successful summary from a compromised run, rather than scoring only the final text.
Make the task and objective explicit in the test record. If reviewers do not know what the model was supposed to accomplish, a refusal, an incomplete answer and an unsafe action can be difficult to compare. Clear test cases also make it easier to repeat a run after a model or application change and determine whether its behavior actually changed.
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Before selecting attack cases, identify what enters the model, what it can access and what it can do. Include the sensitive data that matters to your organization and the actions whose consequences would be material. A model with no external tools has a different attack surface from an agent that can retrieve private records or act on a user’s behalf. Your evaluation should reflect the latter system’s real permissions rather than an abstract chat exchange.
- List the legitimate tasks the application must complete.
- Identify every untrusted input source, including retrieved content and tool results.
- Record sensitive information the system can see or retrieve.
- List available tools and actions, highlighting those with meaningful consequences.
- Specify what constitutes an unacceptable disclosure, action or task failure.
Use this map to decide which attacks are relevant, rather than trying to test every possible behavior. If the system has no access to a particular category of information or action, a test focused on that capability may not help you choose between candidates. If a capability is available and consequential, include it in the evaluation and make sure the model has the same access in each comparable run.
Separate compromise from overrefusal
Resistance and usefulness are separate outcomes. Record whether an attack succeeds, but also whether the model completes the benign task and whether it refuses that task unnecessarily. OpenAI’s instruction-hierarchy evaluation reports both robustness and overrefusal or capability measures, illustrating why a single refusal rate is not a sufficient selection criterion.
A model that says “no” to a malicious instruction but also refuses to summarize the document has not delivered the intended service. Conversely, an answer that appears helpful should not be considered a success if the agent has already taken an unauthorized intermediate step. Treat safety and task completion as separate measures so a favorable score in one area cannot hide a weakness in another.
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Compare candidates on equivalent configurations
A model comparison is meaningful only when you know what was held constant. Record the model name and exact version, system instructions, tools, data sources, permissions, model settings, safeguards, test harness, attack set and evaluation date. Use the same configuration for each candidate wherever possible.
When exact parity is not possible—for example, because a provider’s product includes different protections—document the difference. Results then describe the full tested configurations, not an isolated model effect. Provider evaluations may use different protections and conditions, so their scores should not be treated as a direct cross-vendor ranking unless the test conditions support that comparison.
Configuration parity is not clerical detail: it determines what conclusion a comparison can support. If one candidate has access to fewer tools, or one product configuration adds safeguards unavailable in another, a lower attack success rate may reflect those differences. Preserve the distinction between “this configuration performed better” and “this model performed better.”
| Comparison element | What to keep consistent or record | Why it matters |
|---|---|---|
| Model | Provider, model name, version and date tested | Model behavior can change between versions; a result for one version does not automatically apply to another. |
| Instructions and settings | System and developer instructions, model settings and other relevant prompts | Different instructions or settings can change how a candidate handles the same attack. |
| Tools and permissions | Available tools, data access and action permissions | A test cannot establish how an agent behaves with capabilities it was not given. |
| Safeguards and product protections | Protections enabled in the tested application or provider product | Different safeguards mean the result may describe the product configuration rather than model behavior alone. |
| Evaluation | Attack set, harness, scoring rules, date and benchmark version where applicable | These details make a result interpretable and allow later regression testing. |
Measure the attack path, not only the final answer
For every run, preserve the input, untrusted content, tool outputs, model responses and action trace. Score distinct stages rather than collapsing them into a single pass/fail label. WASP reports attack initiation and goal completion separately; those are different outcomes, and an agent can begin following an attack without completing its objective.
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| Measure | Question to answer | What to record |
|---|---|---|
| Attack initiation | Did the model follow or begin to follow the malicious instruction? | The first response or action that advances the attacker’s objective. |
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| Attacker-goal completion | Did the attacker achieve the stated objective? | Whether the sensitive information was disclosed or the targeted action was completed. |
| Legitimate-task completion | Did the model still do the user’s benign task correctly? | Whether it completed the task, partially completed it or failed. |
| Overrefusal | Did it refuse a legitimate task without a relevant safety reason? | The refusal and the benign request it blocked. |
Do not treat these measures as interchangeable. A system that stops before an attacker’s final objective may still have exposed information or taken an unauthorized intermediate step. Conversely, a suspicious-looking response is not necessarily equivalent in severity to a completed sensitive action. Keep the trace so reviewers can see what happened and why the score was assigned.
A repeatable workflow for evaluating candidate models
- Define the application and its consequences. Describe legitimate tasks, untrusted input sources, sensitive data, available actions and the outcomes that would count as harmful.
- Build a task-matched attack set. Include direct overrides and indirect attacks through the application’s actual retrieved content or tool results. Add data-disclosure and action-redirection attempts wherever those risks apply. OWASP advises checking that the attack set covers strategies relevant to the threat.
- Set up equivalent candidate runs. Give each candidate the same harness, task, input, tools and permissions. Record any unavoidable differences in safeguards or product configuration.
- Save the evidence. Keep prompts, source content, tool outputs, responses and action traces so each result can be reviewed and reproduced.
- Score each outcome separately. Record attack initiation, severity, attacker-goal completion, legitimate-task completion and overrefusal rather than compressing the run into one vague safety score.
- Challenge the test set. Add attacks tailored to the candidate model and application, then rerun the evaluation. A fixed suite is useful for regression checks but may miss attacks adapted to a changed model or system.
- Choose for the application’s risk tolerance. Consider the consequences of a compromise alongside task performance. Add system-level controls around high-impact actions, then retest when the model, prompts, tools or safeguards change.
For useful comparisons, preserve the same benign task alongside each attack. That lets you see whether a candidate resists the attack while still serving the user, rather than comparing unrelated behaviors. Keep the evaluation record with the version, date and scoring rules; when you rerun it, you can tell whether a changed outcome came from a changed model, configuration or test.
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Use published benchmark results as evidence, not a universal ranking
Published evaluations can help identify what to test, but each result applies to the model, attack set, configuration and conditions in that evaluation. It is not a guarantee about a different application or a later model version.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhen comparing published numbers, check what each evaluation measured. A hierarchy-conflict score, a prompt-extraction result and an agent attack-success rate are not interchangeable measures of one universal property. Likewise, different test conditions can produce results that are useful in their own context but do not support a direct ranking across providers.
NIST’s adaptive-attack result
In a 2025 evaluation by NIST’s Center for AI Standards and Innovation (CAISI), the strongest baseline attack had an 11% attack-success rate, while the strongest newly developed attack reached 81% in the described AgentDojo Workspace evaluation of an upgraded Claude 3.5 Sonnet. These are results for that evaluation, not general attack-success rates for current systems. They demonstrate why a static test suite can become stale when attacks are adapted to a system.
The practical lesson is not to assume that an attack set that once produced few successes will continue to do so. Keep fixed cases for regression comparisons, but also challenge the current model and application with attacks adapted to them. NIST’s CAISI technical blog, published in January 2025, states: “When evaluating the robustness of AI systems in adversarial contexts such as agent hijacking, it is crucial to evaluate attacks that were optimized for these systems.”
Provider instruction-hierarchy results
OpenAI’s March 2026 instruction-hierarchy report gives provider-reported results for GPT-5-Mini and GPT-5 Mini-R on internal hierarchy evaluations. On System–User Conflict, the reported results were 0.84 and 0.95 respectively; on Developer–User Conflict, they were 0.83 and 0.95. These figures describe those experiments. They are not a cross-vendor ranking, and they do not by themselves establish how either model will perform with your tools, data and safeguards.
Use such results as a reason to ask what your own tests should include, not as a substitute for running them. A report about instruction hierarchy can inform direct-conflict test design, but your application may also need to test indirect instructions in retrieved content, tool-result attacks, data disclosure and action redirection.
Joint evaluations and difficult test conditions
An OpenAI and Anthropic joint evaluation reported distinct model strengths across instruction hierarchy, system-prompt extraction and jailbreak tests. It also cautioned that deliberately difficult evaluations are not directly representative of real-world behavior. Treat challenging tests as useful probes of particular weaknesses, not as a stand-in for the exact conditions of your deployment.
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Pair model choice with controls around tools and data
Model choice is one part of risk reduction. AgentDojo and WASP findings show that model and agent behavior can remain vulnerable under tested conditions. A favorable benchmark score cannot replace controls that limit what a compromised agent can access or do.
- Limit tool permissions and data access to what the legitimate task requires.
- Apply additional controls to actions with material consequences rather than assuming the model will reject every malicious instruction.
- Use the application’s own safeguards and review processes as part of the evaluated configuration.
- Re-run tests after changes to the model, prompts, tools, permissions or safeguards, because each can alter the system being assessed.
These controls do not make an evaluation unnecessary: they change the conditions and potential impact of a failure, so they should be included in the test and recorded. If you change permissions or safeguards after testing, the previous result no longer describes the new configuration; run the relevant cases again.
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Tools that can help structure the evaluation
OWASP’s AI Testing Guide provides a technology-agnostic framework for assessing AI-system security and broader trustworthiness properties. NVIDIA’s garak is an open-source LLM vulnerability scanner that probes for prompt injection and other unwanted behaviors. They can help organize or extend testing, but using a guide or scanner is not proof that a model or application is secure.
OWASP AI Exchange’s prompt-injection testing guidance describes the approach this way: “Testing for resistance against Prompt injection is done by presenting a carefully crafted set of inputs with instructions to achieve unwanted model behaviour (e.g., triggering unwanted actions, offensive outputs, sensitive data disclosure) and evaluating the corresponding risks.” In practice, assess the risk of the observed behavior, not merely whether a probe produced an unexpected answer.
A testing guide can help structure coverage, while a scanner can probe for unwanted behavior. Neither replaces application-specific cases: the test still needs to reflect your legitimate task, untrusted sources, sensitive data and available actions. Treat their outputs as inputs to review and follow-up testing, not as a certification.
How to make the final selection
Prefer the candidate that performs acceptably on your task-matched attacks while continuing to complete legitimate work, under the configuration you intend to deploy. Review the severity and traces of failures, not just an average score. If one candidate appears better only because it had tighter permissions or additional product protections, describe that as a configuration advantage instead of attributing it solely to the model.
Keep the test date, model version, benchmark version where relevant and configuration with the result. A benchmark score is evidence about tested conditions—not a safety certification—and a one-time evaluation cannot establish resistance to attacks that were not included or that emerged later. Re-evaluate when the model, prompts, tools or safeguards change, and keep a stable regression suite alongside new attacks tailored to the current system.
Frequently Asked Questions
How do I test an AI agent against prompt injection?
Define the agent’s legitimate task, untrusted inputs, sensitive data and available actions. Test direct overrides and indirect instructions embedded in the content or tool results it actually processes. Save responses and action traces, then score attack initiation, severity, attacker-goal completion, task completion and overrefusal separately. Repeat with attacks adapted to the model and application.
Which model resists malicious instructions in tool results?
There is no universal ranking supported by a single benchmark. Test the exact model versions in your intended application, with equivalent tools, permissions, safeguards and task-matched tool results. Published evaluations describe their tested conditions, not a guarantee about your configuration.
Is a high prompt-injection benchmark score proof that an AI agent is safe?
No. It is evidence about the model, attack set and configuration tested. NIST CAISI reported that, in one 2025 AgentDojo Workspace evaluation of an upgraded Claude 3.5 Sonnet, attack success ranged from 11% for the strongest baseline attack to 81% for the strongest newly developed attack. The result illustrates why adaptive attacks and system-level controls matter; it is not a general rate for current systems.
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Not by refusal alone. Check whether it took intermediate steps toward the attacker’s goal, whether the goal was completed, and whether it still completed the legitimate task. Track overrefusal separately so a model does not appear better merely because it refuses benign work.
Can I compare provider-published model scores directly?
Only when the evaluation conditions support that comparison. Providers may use different prompts, protections, tools and methods, and one evaluation may measure hierarchy conflicts while another tests extraction or agent actions. Record those differences and avoid treating results as a cross-vendor ranking when configurations are not equivalent.
The Bottom Line
Choose by testing exact model versions in the application and configuration you intend to deploy. Compare equivalent runs across relevant direct and indirect attacks, score both compromise severity and legitimate-task performance, and retest with attacks adapted to the current system. Use model choice alongside controls on data and tools; no benchmark score guarantees safety.
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