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Choose an AI model for prompt-injection resistance by testing the exact model version in the application you plan to deploy—not by relying on a general ranking or one benchmark score. Use attacks that match the application’s untrusted inputs and available tools, measure both attack outcomes and legitimate-task performance, and compare candidates under equivalent configurations. A model’s result is evidence about the conditions tested, not a guarantee that an AI agent is safe.
What prompt-injection resistance means when choosing a model
Prompt injection is an attempt to make an AI system follow instructions that conflict with its intended task or higher-priority instructions. A direct attack can appear in a user’s message and try to override the system’s rules. An indirect attack can be embedded in material the system reads or processes, such as a web page, retrieved document, email, or tool result.
Resistance is not simply whether a model refuses to repeat an unsafe sentence. An agent can reveal sensitive information, call a tool, or move toward an attacker’s objective without explicitly announcing that it has accepted the attack. Conversely, a model that refuses every difficult request may appear resistant while failing at its legitimate job.
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That makes the relevant comparison the behavior of the model inside the intended system: its model version, instructions, data, tools, safeguards, and evaluation method. If the eventual application can read email and send messages, for example, tests should include malicious instructions in email and assess what the agent does with its messaging capability. A text-only benchmark cannot establish how that tool-enabled configuration will behave.
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Decide what the system must protect before testing
Start by describing the application’s legitimate task and its attack surface. This gives you a basis for selecting realistic tests and for deciding which failures matter most. OWASP AI Exchange describes prompt-injection testing as presenting crafted inputs intended to cause unwanted behavior—such as unwanted actions, offensive outputs, or sensitive-data disclosure—and evaluating the associated risks.
Write down the legitimate task
State what a correct result looks like in ordinary, non-adversarial use. Keep this task consistent across candidates. If an assistant is supposed to summarize a document, make clear whether it should summarize the document’s content, ignore instructions embedded in that content, or both. Without a defined benign task, it is difficult to tell security from unnecessary refusal.
Inventory untrusted inputs and sensitive resources
- List the sources the model may read, such as user messages, web pages, retrieved documents, emails, and tool outputs.
- Identify sensitive information available to the system, including data it can retrieve or pass to a tool.
- Record the actions the agent can take, especially actions with consequences outside the conversation.
- Mark which inputs should be treated as content to analyze rather than instructions to obey.
This inventory turns the broad label “prompt injection” into specific risks for the application. A system that only summarizes public content has a different exposure from one that can access private records or perform consequential actions.
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Define the attacker’s objective
For every test, record both the user’s legitimate request and what the attacker is trying to make the system do. Useful objectives include redirecting an action, extracting sensitive information, or bypassing a safeguard. An attack without an explicit goal can produce an ambiguous result: a strange response may look suspicious but may not demonstrate a meaningful compromise.
Build an attack set that matches the application
Use a repeatable set of direct and indirect attacks. OWASP recommends checking that the test set covers the strategies relevant to the threat, rather than assuming a generic collection represents every application. Include only attack paths that make sense for the system, but cover each material path you identify.
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Cover direct and indirect instructions
- Direct override: Put an instruction in the user’s message that attempts to displace higher-priority directions.
- Retrieved-content injection: Place an attacker’s instruction in a document or web page the model is asked to inspect.
- Tool-output injection: Include a malicious instruction in a tool result, such as returned content the agent must summarize or act on.
- Other application inputs: If the system handles email or another untrusted source, test instructions arriving through that route.
The purpose is not to test every imaginable wording. It is to exercise the ways an attacker could deliver instructions through this application and the boundaries the system is supposed to maintain.
Test the consequences that matter
Where relevant, include attempts to redirect actions, disclose sensitive data, and bypass safeguards. A test should establish what the attacker wants, what the legitimate task is, and what evidence would count as a failure. For an agent with tools, that evidence may include a tool call or an intermediate step toward the objective—not just the final message shown to the user.
Keep a stable regression set, then adapt it
A fixed suite makes repeated comparisons possible, but it can become stale. Keep the same baseline cases so you can see whether a change caused a regression, and add new attacks tailored to the current model and application. Record the test date and any benchmark version so results have context. A static score alone may miss attacks developed after the suite was created.
Run a fair comparison between model candidates
Compare candidates under the same harness and permissions wherever possible. A provider’s published evaluation may use product protections, instructions, or other conditions that differ from yours. If two candidates were tested under different conditions, their results cannot be attributed to model choice alone without qualification.
Hold the configuration constant
For each run, record the model name and version, system instructions, tools, data, model settings, safeguards, attack set, and evaluation method. Keep those factors constant across candidates when feasible. If a difference cannot be avoided—for example, a product-specific protection is tied to one candidate—write it down and treat the result as a comparison of configurations, not just models.
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Save the complete trace
Preserve the prompts, untrusted content, tool outputs, model responses, and action traces. The trace helps distinguish a model that resisted the attack from one that began following it but did not complete the final objective. It also makes a result easier to reproduce after a prompt, tool, safeguard, or model changes.
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Run the same test cases in the same manner for each candidate. Note the test date and configuration alongside the outcomes. This does not make a benchmark universal; it makes your own comparison more interpretable and useful when you test again.
Score security and usefulness separately
A useful evaluation does not reduce every outcome to a single “safe” or “unsafe” label. Track attack behavior, its severity, and whether the model still completed the legitimate task. OpenAI’s instruction-hierarchy evaluation, for example, reports both robustness and overrefusal or capability measures; WASP distinguishes starting to follow an attack from completing its goal.
Record four distinct outcomes
- Attack initiation: Did the agent follow the injected instruction at all, or take an intermediate step toward the attacker’s objective?
- Severity: Was the behavior harmless text compliance, a sensitive disclosure, an unauthorized tool call, or another consequential action?
- Goal completion: Did the attacker’s objective succeed, even if the agent’s first response seemed cautious?
- Legitimate-task completion: Did the model still perform the user’s benign task correctly, or did it refuse or derail the task?
Keep these measures separate in the results. A system that begins an unauthorized action but fails to complete it is not equivalent to one that completes a sensitive disclosure. A system that blocks every attack by refusing every document task is not equivalent to one that resists injection while completing the intended work.
Use a severity rubric that reflects your risk
Set severity categories before looking at the results. At minimum, distinguish an unwanted response from an action that exposes protected information or changes something outside the conversation. The categories should reflect the application’s real data and permissions; the same behavior can carry different consequences in different systems. Do not let a low count of severe failures hide frequent lower-level attack initiation, or let a high count of harmless responses obscure a smaller number of consequential failures.
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Choose based on risk tolerance, not a universal winner
There is no single benchmark threshold in the cited evaluations that certifies a candidate for every use. Decide in advance which outcomes are unacceptable for the application, how much legitimate-task failure is tolerable, and what additional controls will be required. A model with a stronger result on one attack set may still behave differently on a different tool, data source, or attack strategy.
What published evaluations can—and cannot—tell you
Published figures can help explain why testing needs to be specific and adaptive. They are not interchangeable scores: evaluations may use different models, tasks, attacks, product protections, and measurement methods. Read each result with the conditions attached to it.
| Evaluation | Reported result | How to interpret it |
|---|---|---|
| NIST CAISI’s 2025 AgentDojo Workspace evaluation of an upgraded Claude 3.5 Sonnet | Attack success was 11% for the strongest baseline attack and 81% for the strongest newly developed attack. | These rates describe that evaluation and its attacks. They are not general rates for current systems or a universal model ranking. |
| OpenAI’s March 2026 instruction-hierarchy report | On System–User Conflict, GPT-5-Mini scored 0.84 and GPT-5 Mini-R scored 0.95. On Developer–User Conflict, the reported scores were 0.83 and 0.95, respectively. | These are provider-reported results for those internal evaluations, not a cross-vendor comparison or a guarantee of behavior in a different application. |
| OpenAI and Anthropic joint evaluation | The report describes distinct model strengths across instruction hierarchy, system-prompt extraction, and jailbreak tests. | The report cautions that deliberately difficult evaluations are not directly representative of real-world behavior. |
NIST CAISI’s January 2025 technical blog 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.” The point is practical: testing only established attacks can miss attacks adapted to a particular system.
Do not read a score from one row as a direct head-to-head verdict against a score from another. The reported evaluations differ in scope and conditions, and even two results from the same provider may measure different conflict types. Use published work to inform your test design, then evaluate the actual configuration you intend to use.
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OWASP’s AI Testing Guide offers 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. These resources can help structure or broaden an evaluation; a scan or framework does not prove that a model or application is secure.
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Use a scanner as one input to the process, not as a substitute for application-specific attacks. A general probe cannot by itself establish whether the system’s particular documents, permissions, tool outputs, and safeguards have been tested. Preserve the test cases and traces that matter to your deployment so results can be compared after changes.
Turn model choice into layered risk reduction
Model selection is one part of reducing prompt-injection risk. AgentDojo and WASP findings show that model and agent behavior can remain vulnerable under tested conditions. A favorable benchmark result therefore does not replace controls on tools, data access, or sensitive actions.
- Limit permissions: Give the agent access only to the tools and data required for its legitimate task.
- Protect consequential actions: Apply controls around actions that could cause material effects, rather than treating a model response as the only line of defense.
- Keep untrusted content in scope: Test how the application handles instructions embedded in the material it reads and in tool results.
- Re-evaluate meaningful changes: Rerun the suite after changing the model, prompt, tools, safeguards, or other parts of the deployment that affect behavior.
These controls address different parts of the risk. Choosing a model based on relevant tests helps reduce the chance of unwanted behavior; permissions and safeguards limit what that behavior can accomplish.
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- Define the job and boundaries. Describe the legitimate task, untrusted inputs, sensitive data, tools, and actions with material consequences.
- Write a baseline attack set. Include relevant direct overrides and indirect injections through retrieved content or tool output. Add data-disclosure, action-redirection, or safeguard-bypass attempts where applicable.
- Run every candidate under comparable conditions. Use the same harness and permissions, and save prompts, tool outputs, responses, and action traces. Record any configuration differences.
- Score different outcomes separately. Track attack initiation, severity, attacker-goal completion, legitimate-task completion, and overrefusal.
- Adapt the tests. Add attacks designed for the current model and application, then rerun them alongside the stable baseline suite.
- Make a risk-based decision. Select a configuration that meets the application’s requirements, apply controls around high-impact actions, and avoid presenting one score as a safety certification.
- Retest after changes. Repeat the evaluation when the model, prompts, tools, or safeguards change, and retain dates and versions for comparison.
Common selection mistakes
Choosing from a general ranking
A ranking may not test the attack path, tool access, or legitimate task your application uses. Treat it as context, not a substitute for testing the deployed configuration.
Counting only final harmful outputs
An agent may take intermediate steps toward an attacker’s goal or make an unauthorized tool call without producing an obvious final disclosure. Inspect action traces and score initiation separately from goal completion.
Rewarding blanket refusal
Resistance should not be measured without checking whether the benign task still succeeds. Track overrefusal and legitimate-task completion alongside attack outcomes.
Comparing unlike configurations
Different prompts, tools, permissions, safeguards, or evaluation methods can explain a result that appears to be a model difference. Hold them constant where possible; otherwise report the mismatch and avoid attributing the whole outcome to the model.
Assuming a fixed suite stays effective
Static attacks can lose relevance as systems change or attackers adapt. Preserve a regression suite, add system-specific red-team cases, and rerun after meaningful deployment changes.
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