Evaluate an AI model for document security inside the application that will use it—not by relying on a general safety score or by pasting an attack into a standalone chat. Put malicious instructions through the same upload, extraction, retrieval, filtering, and context-assembly path used in deployment. Then measure whether they cause disclosure, misleading answers, or unauthorized actions, including effects that do not appear in the model’s visible response.
The result applies only to the model version, prompts, tools, permissions, controls, and test cases you actually evaluated. It is evidence about a particular system configuration, not a universal ranking of models.
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What you are testing—and why document workflows are different
Untrusted documents can contain instructions that change how an AI system behaves after their contents are incorporated into the model’s context. OWASP calls this indirect prompt injection: unlike a direct attack typed by the current user, the malicious instruction arrives through an external source such as a file, web page, email, retrieved passage, or tool output. The attacker may be someone who altered that source before your system ingested it, rather than the person asking the model a question.
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OWASP’s Gen AI Security Project describes prompt injection as a vulnerability in how models process prompts and how input can influence other parts of the model, potentially leading to guideline violations, harmful output, unauthorized access, or influence over critical decisions. The practical impact depends on what the application lets the model see and do. A document summarizer with no access to sensitive information has a different risk profile from an agent that can search private records or initiate consequential actions.
That distinction changes the evaluation. A model’s refusal in a direct chat test does not establish that a retrieval-augmented generation (RAG) application is safe. The application may extract text differently, insert retrieved passages into a prompt, add tool results, or grant permissions that were absent from the chat test. Evaluate the real boundary: model plus data path plus controls.
Define the deployment and the harms that matter
Before creating test cases, map how outside content can reach the model and what a successful attack could affect. Include ordinary documents and less obvious routes such as retrieved chunks, web pages, email, summaries, third-party API responses, and outputs from tools. OWASP’s LLM Verification Standard v2.0 notes that stored data and previous model completions can also become sources of indirect injection; trusted storage does not make the original content trustworthy.
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For each route, record where content enters, what processing happens to it, and where it is added to model context. Include extraction, chunking, retrieval, filtering, detection, summarization, and any transformations performed by tools. A malicious instruction may survive these steps, be reformatted, or arrive through a path that bypasses a control you expected to apply.
- Identify who can supply or change the source content, and whether it is reviewed before ingestion.
- Record whether the model receives full documents, extracted text, selected passages, or summaries.
- List any tool outputs or third-party responses that are inserted into later prompts.
- Note which filtering and detection controls operate at each stage, and whether they match the deployed configuration.
Describe assets and consequences
List the information and actions that matter in this particular application: sensitive data the model can access, decisions its output may influence, and downstream operations it can propose or execute. Define harms in observable terms. Examples include revealing a protected value, giving a materially misleading recommendation, accessing a record without authorization, or triggering an action that requires human approval.
Consider both likelihood and impact. OWASP notes that indirect injection depends on an attacker being able to manipulate external data before it is ingested; the attacker need not be the current user. The likelihood of that condition and the consequences of an attack will vary by workflow. Use that context to decide which scenarios deserve the most attention and how severe a failure would be.
Build an attack set with benign controls
A useful evaluation is a set of representative cases, not a single memorable prompt. Include the ways untrusted content could challenge the intended behavior in your application, and include ordinary documents that should work normally. Benign controls help distinguish a system that resists attacks from one that simply refuses or mishandles all documents.
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Cover different attack forms
Tailor cases to your sources, assets, and actions. A practical set can include:
- Instruction override: content attempts to redirect the model away from the user’s task.
- Role confusion: content pretends to be a higher-priority instruction or changes the model’s supposed role.
- Hidden or inconspicuous text: instructions are placed where a person might overlook them but extraction or processing may retain them.
- Malicious URLs or references: content attempts to send the model or a tool toward an unsafe destination.
- Data extraction: content requests information the model should not disclose, including information available through the application’s context or tools.
- Formatting and encoding variations: the same basic intent is varied through synonyms, layout, or encoding to test whether a defense depends on a narrow pattern.
Keep cases safe and controlled. For instance, a test can use a clearly synthetic secret such as TEST-ONLY-SECRET-42 and check whether the model reveals it to an unauthorized request. Do not populate a test with real customer credentials or other live sensitive data. If a case involves a tool action, use a sandbox or a harmless operation whose effects can be observed without risking production data.
Pair attacks with expected behavior
For each case, write down the legitimate user task, the untrusted content being introduced, the expected safe result, and what would count as failure. An attack document should not itself determine the expected result; define the outcome from the application’s policy and the harm model. For a benign control, specify what useful response should remain possible. This makes it easier to notice both unsafe compliance and excessive blocking.
Vary cases rather than relying on one exact phrase. OWASP’s AI security testing guidance recommends building a relevant attack set, tailoring it to context, and adding variations such as synonyms, encoding, and formatting changes. A filter that catches one literal string but fails on a minor variation is not evidence of robust protection.
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Run every case through the production path
Submit test content through the same ingestion or retrieval mechanism used in deployment. If users upload files, test uploads and the real extraction pipeline. If the application retrieves passages, make the test document eligible for retrieval and ask the application the kind of question that would retrieve it. If an agent consumes tool output, include that route too. Keep deployed filtering and detection enabled: testing a bypassed or simplified path answers a different question.
Directly pasting attack text into a chat prompt can help explore model behavior, but it does not demonstrate resistance to indirect injection. It skips the mechanisms through which untrusted content enters the application and may omit the protections, transformations, and tools that determine the real outcome. A meaningful test follows the content from source to context to response and, where applicable, to action.
- Fix the configuration. Record the exact model version, system and task prompts, retrieval setup, filters, tools, permissions, and application settings under test.
- Submit the source. Add the attack or benign-control document using the same upload, ingestion, or external-source path as deployment.
- Verify the path. Confirm that the application processed the content and that the intended filters, retrieval behavior, and context assembly actually ran. Record the relevant provenance and processing outcome.
- Ask a representative task. Use the ordinary user request that should cause the system to consult the source; do not quietly change the application’s real instructions to make the test easier.
- Capture the full result. Save the visible answer, relevant alerts, retrieval or tool events, and any resulting application-state changes.
Where a production-equivalent test would risk real users or data, reproduce the configuration in a controlled environment. The goal is fidelity to the application path without exposing live assets to a test attack.
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Measure harmful outcomes, not just detector alerts
A detection alert is useful evidence about one control, but it is not the outcome you ultimately need to evaluate. A detector can fire while the model still discloses information or a tool still performs an action. Conversely, a detector may not fire even though the application safely contains the attempt. Inspect both the security signal and the effect.
Record what happened
- Instruction following: Did the model follow the document’s malicious instruction, or preserve the legitimate task and applicable policies?
- Disclosure: Did the response or a tool call expose protected information?
- Misleading output: Did untrusted content materially distort the answer or recommendation?
- Unauthorized action: Did the system propose, initiate, or complete an operation beyond the user’s authorization?
- Control response: Did a filter, alert, approval step, or other mitigation intervene, and did it prevent the harmful effect?
- Benign-task performance: Did the system still handle safe control documents usefully?
Inspect application state and tool calls in addition to the final text shown to a user. A response may look harmless while a background operation has already retrieved a restricted record or changed state. Record whether an action was merely suggested, sent for approval, or actually executed; those outcomes have different severity.
Score severity in context
Use a consistent, understandable severity scale tied to the harms you defined. For example, a minor answer-quality issue should not be scored the same as confirmed disclosure of sensitive information or an unapproved consequential action. Document the reason for each rating and whether a mitigation prevented the effect. Do not combine a large number of harmless detector alerts with a smaller number of severe failures into one opaque success percentage.
Report the underlying counts and outcomes alongside any summary metric. State what the test set covered, how many runs were made, what configuration was used, and how you defined attack success. This lets readers understand what a rate means and prevents a narrow or easy set from being mistaken for a broad security guarantee.
Repeat runs and compare candidates fairly
Model behavior can vary between runs, so repeat cases rather than treating one successful refusal as proof. Vary attack content and formatting as well as repeating identical cases. Keep the evaluation conditions consistent when comparing models or system configurations: model version, prompts, tools, permissions, application settings, attack set, and outcome definitions should be held constant wherever possible.
If any of those conditions changes, identify the change and treat the result as a new configuration. A model update, a modified prompt, a new retrieval source, or a permission change can alter the system’s behavior. Record the tested settings and cases with the result so teams can reproduce the comparison and know what the reported evidence covers.
Useful comparison axes
| Axis | What to compare | Why it matters |
|---|---|---|
| Harmful outcomes | Attack success, severity, disclosure, misleading output, and unauthorized actions | A refusal or detector signal alone does not show whether the application prevented harm. |
| Stability | Outcomes across repeated runs and varied or obfuscated inputs | One safe response may not represent behavior across realistic variations. |
| Delivery path | Attacks arriving through documents, retrieval, and tool outputs | Direct chat tests omit the indirect routes used in document workflows. |
| Application controls | Permissions, parameter validation, approval, and detection or response | Security depends on the whole system, not model behavior alone. |
| RAG integrity and provenance | Document verification, rejection and alerting for failed checks, and source records | These controls help establish whether retrieved content is the expected content and where it came from. |
Interpret public attack-success figures carefully
OWASP’s prompt-injection prevention cheat sheet reports attack-success figures of 89% for GPT-4o and 78% for Claude 3.5 Sonnet, attributing them to Hughes et al. (2024), with up to 10,000 augmented prompts per request. OWASP explicitly cautions that these figures apply to the tested models and configurations, not as universal predictions.
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They are not a current ranking of commercial models, nor a direct test of every document-processing workflow. The tested configurations and prompt conditions matter, and application ingestion, retrieval, tools, and permissions can differ. Treat the figures as bounded results from a particular test—not a substitute for running a controlled evaluation of your own deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the controls around the model
Model behavior is only one part of the security boundary. OWASP’s LLM01:2025 guidance and LLM Verification Standard v2.0 point to application controls that constrain what an injected instruction can accomplish. Test these controls alongside the model, because a system that sometimes encounters adversarial content should not rely on the model to make every security decision correctly.
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Limit privilege and separate untrusted content
Give the model and its tools only the access necessary for the task. Keep untrusted document content distinguishable from trusted instructions in the application’s design, and do not treat a stored file, third-party response, or earlier completion as authoritative merely because it is already in the system. Content separation can help clarify boundaries, but it is not a guarantee that a model will ignore an injection.
Validate tools and gate high-risk actions
Restrict available tools and validate tool parameters rather than assuming the model will always construct safe requests. Test whether an injected document can cause an operation the user did not authorize. Require human approval for high-risk actions, and verify that the approval step is enforced by the application rather than expressed only as an instruction in the prompt. Where feasible, make the approval decision specific to the action and its parameters.
For each control, check both the intended safeguard and its failure mode: whether the tool is unavailable when it should be, whether invalid parameters are rejected, whether an approval requirement can be bypassed, and whether attempted violations are visible to responders. A control that merely logs an event after an irreversible action is not equivalent to one that blocks the action beforehand.
For RAG, verify integrity and provenance through the pipeline
Retrieval-augmented systems need checks on the documents themselves and on how they move through ingestion and retrieval. OWASP’s RAG Security Cheat Sheet covers controls across the RAG pipeline and document poisoning. Keep a manifest or equivalent record of approved documents, verify digests against the approved baseline, reject and alert on failed checks, and retain provenance showing who supplied each document, when and from where it arrived, and what approval applied.
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Include failures in the evaluation, not only clean ingestion. Check what happens when a verification fails, when provenance is missing, or when a document’s source or approval is unclear. Confirm whether the content is rejected, whether an alert reaches the appropriate people, and whether the application prevents it from entering model context. The result should be observable rather than inferred from a successful user-facing answer.
Turn results into a defensible security decision
Summarize evidence at the level of the tested system configuration. A concise report should let another team understand what was exposed, how attacks were delivered, what harms occurred, and which safeguards changed the outcome.
- Identify the application and the model version, prompts, tools, permissions, and relevant settings tested.
- Describe the document and tool-output paths exercised, including the production-equivalent filters and context assembly.
- List the attack categories and benign controls, plus meaningful variations and the number of repeated runs.
- Define attack success and severity, and report disclosure, misleading output, tool actions, alerts, mitigations, and residual failures separately.
- Explain any differences between candidates or configurations, and avoid a broad ranking when conditions were not equivalent.
- State the scope plainly: passing these cases is evidence about these cases and settings, not proof against every possible injection.
Use failures to decide what to change—such as reducing permissions, strengthening parameter checks, requiring approval, improving source validation, or adjusting detection—and rerun the same cases after the change. Add new variations where a failure exposed a missing scenario. Re-evaluation is especially important after changes to models, prompts, tools, data sources, or application controls, because the prior result does not automatically transfer to a new configuration.
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OWASP LLM01:2025 explains direct and indirect prompt injection, its possible impacts, and mitigation approaches including content separation, least privilege, human approval, and adversarial testing. The OWASP LLM Verification Standard v2.0 addresses indirect injection from sources such as stored data, third-party API responses, and previous completions, along with tool access and parameter validation. OWASP’s AI security testing guidance describes attack-set creation, production-path testing, variation, repeated runs, and harm-based analysis.
NIST’s January 2025 blog discusses agent hijacking through malicious instructions in ingested data and describes evaluation of defenses as ongoing work. NIST’s guidelines page, updated September 30, 2026, lists automated benchmark-evaluation practices as an initial public draft. These sources provide useful context, but they do not establish a complete, final benchmark standard or a shared head-to-head evaluation of current commercial models on document security. Model behavior and standards can change, so a result should be dated and tied to the configuration that was tested.
The right conclusion from an evaluation is therefore specific: this configuration did or did not prevent defined harms under these tested document paths and conditions. Anything broader requires broader evidence.
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