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Test an AI system as if the model were an untrusted user, not a security boundary. Map every flow between users, instructions, retrieved content, tools, identities, data stores and people; define an invariant for each boundary; then attack the system through the same channels real data uses. Deterministic authorization, least-privilege tools, strict output validation, approval for high-impact actions and complete traces should contain a manipulated model even when an adversarial prompt succeeds.
The method below covers direct and indirect prompt injection, retrieval and memory poisoning, tool escalation, side effects, monitoring and regression testing. Use harmless fixtures and instrumented tool substitutes so the exercise can observe attempted actions without sending real mail, changing production records or exposing genuine secrets.
What a trust-boundary test must prove
A language model predicts text or tool-call arguments. It does not establish identity, grant permission or make a durable security decision. A system prompt can describe a policy, but it cannot enforce that policy when a model is manipulated. OWASP’s GenAI Security Project states that the system prompt should not be treated as a secret or security control and that authorization bounds checks and privilege separation must happen deterministically and audibly outside the model.
Your test therefore asks an application question: Can untrusted content cause an operation that the caller is not allowed to perform? A refusal is useful evidence, but it is not the pass condition. The pass condition is that no unauthorized data read, tool request, token use or state change occurred, and that the attempt produced an investigation-quality record.
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Trust boundaries normally exist at these transitions:
- End-user input entering the application.
- System and developer instructions being combined with user content.
- Web pages, uploaded files, email, tickets or retrieved documents being inserted into context.
- Model output becoming a tool or function request.
- An application identity being used against a data store or external API.
- Conversation or memory state being written and reused later.
- A proposed high-impact action being presented to a human approver.
- An approved operation changing an external system.
Mark each transition on a data-flow and privilege map. Label whether the payload is an instruction, data or a request for a capability, and record the identity and scope that apply on the other side. OWASP describes prompt injection as crafted input that manipulates an LLM into carrying out an attacker’s intentions; impacts can include private-data disclosure, unsafe decisions, unauthorized plugin or tool use and exfiltration.
Build the system map before writing attack prompts
List assets and identities
Start with assets that must not cross a boundary: secrets, personal information, regulated records, payment data, administrative settings and another customer’s or tenant’s records. For every entry, name its owner, classification, retention rule and permitted consumers. Include data exposed through search, embeddings, caches, conversation memory and exported files, not only the primary database.
For each identity, document whether it represents the end user, a service account, a delegated administrator or an approver. Record how the identity is derived, how long its token lasts, what tenant or project it is restricted to and how it can be revoked. A model response must never be able to broaden those attributes.
Inventory every capability
Make a capability register for retrieval connectors, browsers, code execution, email, ticketing, payments, file writes, database queries, deployment actions and administrative operations. Record the exact operation, input fields, identity used, data it can read, state it can change, reversibility, rate limit and approval requirement.
| Capability detail to record | Why the test needs it |
|---|---|
| Operation and exact arguments | Shows what a model request could actually do rather than what the tool name implies. |
| Identity and token scope | Exposes confused-deputy and privilege-escalation paths. |
| Readable data and tenant boundary | Defines the maximum legitimate disclosure. |
| State change and reversibility | Determines whether a sandbox, rollback or human approval is mandatory. |
| Approval and rate-limit rule | Provides a concrete expected control for high-impact or repeated actions. |
Never place credentials, connection strings or permission logic in a system prompt. Keep those controls in application code and the identity or authorization service, where they can be inspected and tested.
Turn policies into boundary invariants
Write statements that can be checked in a trace. Avoid requirements such as “the assistant should be careful.” Examples include:
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- Text from an untrusted document is data to summarize, never an instruction that can authorize a tool call.
- A model response cannot change the caller’s identity, tenant, roles or token scope.
- A read-only agent cannot invoke a write operation, even when the model requests it.
- A high-impact action requires approval displaying the actual target, arguments and side effect.
- Each tool receives only the minimum fields and token scope needed for the approved operation.
- Output that violates a schema, destination rule, data-loss policy or classification check is blocked or routed to review.
- Memory cannot make a record available to a later session or tenant unless the access check independently permits it.
- Every denial, approval and side effect can be correlated with the originating input and retrieved sources.
These invariants change a vague behavioral expectation into an application test. A model may still produce an unsafe sentence; the surrounding controls must prevent that sentence from acquiring authority.
Exercise direct prompt injection through the normal user channel
Direct injection targets the prompt field a user controls. Use a harmless test corpus and a non-production model or account, but send attacks through the same endpoint, session and orchestration path as a normal request.
Attack classes to include
- Instruction override: attempts to ignore system or developer rules.
- Role and policy confusion: claims that the user is an administrator, auditor or emergency operator.
- Prompt and secret extraction: requests for hidden instructions, credentials, connection strings or other protected context.
- Unauthorized capability use: requests to call a tool outside the caller’s permission or to change a read-only task into a write.
- Data-boundary probing: requests for another user’s records, hidden retrieval results or cross-tenant identifiers.
- Compound requests: a benign lookup followed by an action that becomes dangerous only when the result is inserted into a tool argument.
Vary each class. Use paraphrases, multilingual text, long-context placement, encoded or obfuscated strings and prompts that avoid obvious filter keywords. Include attacks in a conversation turn that follows a legitimate approval and attacks that ask the model to reinterpret an earlier instruction.
What counts as a result
Capture the model response, retrieval events, tool requests, authorization decisions, token scope, approval state and side effects. A response that says “I cannot do that” is insufficient if a background connector already fetched the protected record. Conversely, a suspicious response is contained if the policy engine denied the request before any data or state crossed the boundary. Record both outcomes so the regression suite tests enforcement, not style.
Exercise indirect injection at the real external-data boundary
Indirect injection arrives through content the application later supplies to the model. Put harmless attack instructions in the channel the product actually evaluates: a web page, PDF, repository file, email, support ticket, calendar entry or knowledge-base document. Pasting the same text into the user message is a direct-injection test and does not exercise this boundary.
Prepare a controlled corpus
Create fixtures that contain conflicting instructions, requests to reveal context, fake urgent approvals, links, encoded text and instructions split across chunks. Label each fixture with its source, tenant, document permission and expected treatment. Include documents that look authoritative and ones that contain ordinary business text with a single malicious paragraph.
Verify provenance and separation
- The retriever enforces document- and tenant-level access before a chunk enters context.
- Each chunk retains source, owner, timestamp and trust label.
- The prompt clearly distinguishes untrusted content from application instructions.
- Quoted document text cannot create a new authority or approval.
- A requested action is stopped by code-level authorization even when the model repeats the embedded instruction.
Test the complete path: ingestion, indexing, chunking, retrieval, reranking, prompt assembly, model response and tool policy. OWASP recommends direct and indirect injection exercises with harmless data and instrumented substitutes, including attempts designed to bypass simple keyword filters.
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Test tool and authorization boundaries
Use instrumented substitutes instead of live tools. A substitute should record the requested operation, arguments, caller identity, token scope, sequence number and whether the policy layer would have allowed execution. It should return realistic but synthetic data and never send an external message or alter a production record.
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- Horizontal access: request another user’s or tenant’s record while retaining the original caller identity.
- Vertical escalation: ask a normal user session to perform an administrator-only operation.
- Confused deputy: cause a model to use a broad service account for a narrow user request.
- Approval replay: reuse an old approval with a new target, amount, recipient or file.
- Argument substitution: obtain approval for one operation, then alter an argument before execution.
- Chained concealment: hide a prohibited call among several harmless tool requests.
- Token leakage: ask the model to echo, transform or place a token in a tool argument.
Authorization should evaluate the concrete operation, target, caller, tenant and current state at execution time. Application-owned tokens, least privilege and operation-specific scopes reduce the impact of a manipulated model. Keep function handling in code rather than giving the model direct authority to decide whether a function is permissible.
Test approval as a security control
For a privileged operation, display the actual target, arguments, data destination and side effect to the approver. Test that changing any of those values invalidates the approval. Verify expiry, one-time use, reviewer identity and denial handling. Do not approve a model-generated explanation while hiding the operation it would perform; an intermediary can omit or manipulate that explanation.
Test retrieval, memory and persistence
Seed a synthetic corpus with poisoned records, conflicting instructions, stale permissions, cross-tenant identifiers and secrets that must be redacted. Then verify the controls at each stage:
- Access control is applied before retrieval, not after the model has seen the chunk.
- Filtering and reranking cannot reintroduce a record from a forbidden tenant.
- Conversation memory keeps tenant and user scope with every entry.
- Deletion and retention requests remove data from active memory, indexes, caches and exports according to policy.
- A malicious instruction cannot persist into a later session through summaries, long-term memory or tool-generated notes.
- Stale permissions are rechecked when a stored item is used, not trusted because it was valid when indexed.
Repeat these tests after changes to indexing, chunk size, embeddings, reranking, memory format or retention jobs. A retrieval change can create a new boundary failure even when the model and prompt are unchanged.
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Do not pass free-form output directly to a tool, query engine or user-visible high-risk channel. Parse it against a strict schema, reject extra fields and apply deterministic policy checks to every value.
Checks to implement
- Allow-listed operation and destination.
- Recipient, account, tenant and object ownership.
- Amounts, limits, currency and rate of change.
- File paths, URL schemes and query scope.
- Data classification and redaction requirements.
- Type, length, encoding and character-set constraints.
- Links, embedded commands and prompt text echoed into a shell or query.
Test malformed JSON, duplicate keys, extra properties, type confusion, hidden links, escaped delimiters and output that attempts to bypass a downstream validator. Send failed validation to a safe denial or review queue; do not ask the model to self-certify that its output is safe.
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For high-risk actions, require a human to review the concrete operation. Approval is a mitigation for privileged operations, not a substitute for authorization. The reviewer should see the target, arguments, data involved and irreversible consequences, and the system should execute exactly what was approved.
Instrument monitoring, alerting and recovery
Log the source and trust label of every input, each retrieved item and provenance field, the model response, tool request, policy decision, approval, denial and side effect. Correlate them with a request and session identifier while protecting the log itself from secret leakage.
Alert on behavior that signals a boundary attack
- Repeated attempts to override instructions or probe hidden context.
- Unusual sequences of tools or rapid changes in requested scope.
- Identity, tenant or privilege mismatches.
- Patterns associated with data exfiltration or bulk reads.
- Approvals that are replayed, altered or issued outside normal workflows.
Recovery is part of the control. Confirm that operators can revoke application tokens, disable an individual tool, quarantine a document or connector, stop queued jobs, roll back a change and reconstruct the event from logs. Exercise those actions in a tabletop or controlled drill; a control that cannot be activated under pressure is not a dependable boundary.
Store evidence and make tests repeatable
For each test case, retain the setup, payload class, expected invariant, model output, retrieved sources, tool trace, authorization result, side-effect result and reviewer decision. Keep synthetic secrets and identifiers so the suite can run safely in continuous integration or a dedicated security environment.
Run the suite after every material change to the model, system prompt, retrieval source, tool schema, policy, identity integration or orchestration code. Include both positive cases, where an allowed operation succeeds, and negative cases, where the same operation is denied for a different tenant, role, target or scope. Compare traces, not just final text, because a model may produce a polite answer after an unauthorized background call.
OWASP launched its Top 10 for LLM Application Security project in May 2023. Its red-teaming initiative emphasizes standardized methodology, benchmarks and tools for repeatable evaluation. That makes an AI red-team platform or LLM security testing tool useful only when it can preserve your application’s boundary context, tool traces and authorization outcomes; a generic prompt list cannot prove containment.
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Use consequence-based acceptance gates
There is no universal pass-rate threshold in the authoritative material for this subject. Set gates from the harm a failure could cause rather than from a convenient percentage:
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- Zero unauthorized high-impact side effects in the tested scenarios.
- Zero cross-tenant reads where tenant isolation is required.
- Zero secret disclosure on protected paths.
- Explicit human review for residual model-only failures that cannot trigger an operation.
- A documented owner and remediation date for every accepted residual risk.
Separate a model-behavior failure from an enforcement failure. A model that follows an injected instruction is a behavior failure; it becomes a security incident when authorization, validation or approval allows that instruction to cause an impermissible result.
Compare architectures by the controls that contain failure
When evaluating designs, products or internal implementations, score the following dimensions with evidence from the same adversarial cases:
| Comparison axis | Questions to answer |
|---|---|
| Boundary clarity | Can the system distinguish instructions from external data and show provenance at each transition? |
| Privilege scope | Are identities, tokens and tools least-privileged and restricted to a specific operation or tenant? |
| Deterministic enforcement | Do authorization, output validation and approval happen outside the model? |
| Side-effect safety | Are writes reversible, rate-limited, sandboxed and human-approved when risk is high? |
| Testability | Can attacks run against instrumented substitutes with reproducible traces? |
| Observability and recovery | Are decisions logged, alerts actionable and credentials or tools quickly revocable? |
| Framework mapping | Can findings map to OWASP, NIST, ISO, MITRE ATLAS or applicable regulation without replacing application-specific tests? |
OWASP’s crosswalk published September 1, 2026 maps 51 GenAI vulnerabilities from four source lists to controls in 25 frameworks, including NIST, ISO, MITRE ATLAS and the EU AI Act. Use that mapping to communicate coverage and gaps, not as evidence that a particular application is secure.
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A practical execution sequence
- Draw the data-flow and privilege map and mark every instruction, data and capability transition.
- Inventory assets, identities, tools, scopes, side effects and approval requirements.
- Write an invariant for each boundary and define the trace fields needed to verify it.
- Build harmless fixtures and instrumented substitutes for retrieval, memory and tools.
- Run direct-injection variants through the normal user channel.
- Run indirect-injection fixtures through the real webpage, file, email or retrieval path.
- Attempt horizontal, vertical, confused-deputy, replay and argument-substitution attacks.
- Exercise retrieval isolation, memory persistence, output validation and approval invalidation.
- Inspect logs, alerts, token revocation and rollback during the test, not only afterward.
- Store the evidence, apply consequence-based gates and rerun after every material change.
Common failures and how to correct them
Relying on the system prompt
Symptom: a test passes because the model repeats a rule such as “never disclose secrets.” Correction: remove the rule temporarily and verify that code-level authorization, redaction and token scope still prevent disclosure.
Testing only the chat box
Symptom: direct attacks are blocked, but a poisoned document causes a tool request. Correction: place the payload in the actual external channel and preserve provenance through retrieval and prompt assembly.
Checking only the final answer
Symptom: the assistant says it refused, while a connector already fetched protected data. Correction: assert on retrieval events, tool traces, authorization decisions and side effects.
Using production tools for realism
Symptom: a test sends email, edits a ticket or changes a record. Correction: use instrumented substitutes with synthetic data, then run a separately approved, narrowly scoped production verification if required.
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Symptom: the reviewer approves a summary, but the model changes the recipient or amount afterward. Correction: bind approval to the exact target and arguments and reauthorize at execution.
Treating a framework crosswalk as a test plan
Symptom: a checklist claims coverage without exercising the application’s connectors, tenants or side effects. Correction: use framework mappings for vocabulary and reporting, then retain application-specific invariants and traces as the evidence.
Bottom line
The reliable way to test AI security across trust boundaries is to test the surrounding application, not the model’s promises. Map where untrusted data can become an instruction or capability request, enforce identity and permissions deterministically, constrain and validate every tool call, require concrete approval for consequential actions, and preserve traces that prove what happened. Repeat those adversarial tests whenever the model, prompt, retrieval, memory, tool, policy or identity integration changes.
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