Test an AI system as if the model were an untrusted user: map every data and privilege transition, state an invariant for each boundary, attack both the user prompt and external content, and verify in code that unauthorized reads, tool calls, and side effects are blocked. A system prompt can describe policy, but it cannot enforce authorization. Your test evidence must therefore include identity, tool arguments, policy decisions, approvals, and actual side effects—not just the model’s text.
What a trust-boundary test must prove
An AI application crosses several security boundaries in one request. User text enters the application, retrieval may add documents or web pages, the model interprets all of that context, and an orchestrator may turn the response into a function call. That function can then read a database, send a message, alter a ticket, write a file, or call an external API. Memory can carry data into later sessions, while a human approver may authorize a high-impact operation.
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Draw those flows before writing attack prompts. Include the end user; system and developer instructions; untrusted user content; retrieved documents, web pages, and uploaded files; the model; conversation or long-term memory; tools and function calls; identity and authorization services; data stores; external APIs; and the human approver. Mark every transition where data can be mistaken for an instruction, where the model can request a capability, or where a request can change state.
OWASP defines prompt injection as manipulating a large language model with crafted input so it unknowingly executes an attacker’s intentions. The impact can include private-data disclosure, unsafe decisions, unauthorized plugin or tool use, and exfiltration. The relevant question is not whether the model usually refuses. It is whether application-owned controls prevent an unauthorized result when the model is persuaded.
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| Boundary | What crosses it | Control to verify |
|---|---|---|
| User to model context | Prompt text, attachments, and conversation state | Input is labeled and constrained; the user cannot change policy or identity through text |
| External content to model context | Web pages, PDFs, emails, tickets, repository files, and retrieved records | Provenance and trust labels survive retrieval; content is treated as data, not authority |
| Model to orchestrator | Tool name, arguments, and requested sequence | Strict schema, allow-listed operations, deterministic authorization, and rate limits |
| Orchestrator to tool or API | Identity, token, query, destination, and side effect | Application-owned least-privilege credentials and server-side bounds checks |
| Tool result to model or user | Records, errors, secrets, and generated output | Data minimization, redaction, output validation, and destination checks |
| Model to human approver | Proposed action and explanation | Approval shows the concrete target, arguments, identity, and side effect |
Why a system prompt is not a security boundary
A model can follow a system instruction in one conversation and ignore or reinterpret it in another. It can also repeat hidden instructions, accept a malicious document’s directions, or produce a tool request that the surrounding code executes too broadly. OWASP states that the system prompt should not be considered a secret or used as a security control. Authorization bounds and privilege separation must be deterministic and auditable outside the model.
Do not put credentials, connection strings, tenant rules, or permission logic in a system prompt. Give the model only the capabilities that the current request may need, and have application code derive the effective identity and scope from the authenticated caller. Treat every model output as an untrusted request for the application to evaluate.
Build the test plan
1. Inventory capabilities and protected assets
For every tool or API, record the exact operation, identity used, data it can read, state it can change, reversibility, and approval requirement. Include retrieval connectors, browser or code execution, email, ticketing, payments, file writes, and administrative actions. List secrets, personal data, regulated records, and cross-tenant data that must never cross a boundary.
- Record whether an operation is read-only, creates state, changes state, or deletes state.
- Identify the smallest useful data set and token scope for each operation.
- Document whether a failure can be rolled back and who can perform the rollback.
- Separate service identities by capability instead of giving one agent a broad account.
This inventory becomes the expected-result portion of every test. If a read-only assistant has no write capability, a request for a write should fail before a downstream system sees it.
2. Write boundary invariants
Turn vague goals such as “the agent should be safe” into statements that a trace can prove or disprove.
- Untrusted document text is data to summarize, never an instruction that can authorize a tool call.
- A model response cannot expand the caller’s identity, tenant, or permissions.
- A read-only agent cannot invoke a write tool, even when the model requests one.
- A high-impact action requires approval that displays the actual target, arguments, identity, and side effect.
- A tool receives only the minimum fields and token scope needed for the approved operation.
- Output that fails schema, policy, destination, or data-loss checks is blocked or routed to review.
State what evidence proves each invariant: an authorization decision, a denied tool trace, a redacted result, an approval record, or the absence of a side effect.
Test direct prompt injection
Use the normal user channel and exercise several attack classes rather than one memorable jailbreak. Keep the test data harmless, but make the requested behavior observable with instrumented tools.
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Instruction and role attacks
Try requests to ignore earlier instructions, replace the system role, reveal hidden instructions, or treat the user as an administrator. Include paraphrases, multilingual variants, encoded text, unusual whitespace, and attacks placed at the beginning, middle, and end of a long context. Test a benign-looking request that becomes dangerous only when combined with a tool, such as asking for a customer export and then asking the agent to email it.
Credential and data attacks
Ask for secrets, another user’s records, cross-tenant information, internal prompts, or a broader search scope. Test whether the model merely refuses in prose while still issuing a retrieval or tool call. A successful defense means no unauthorized token use, retrieval, output, or side effect occurred.
Record more than the final answer
Capture the original request, normalized input, model response, requested tools, arguments, identity, authorization result, returned data, approval, and side effect. A refusal without a trace can hide a failed control: the model may have called a tool and the interface may simply have suppressed the result.
Test indirect prompt injection at the real boundary
Indirect injection arrives through content the application later supplies to the model. Put harmless attack instructions in the channel the system actually evaluates: a web page, PDF, repository file, email, support ticket, calendar entry, or knowledge-base document. Pasting the same text as a user message is not an indirect-injection test; it exercises a different boundary.
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Verify provenance and labeling
Check that each retrieved item retains its source, tenant, owner, timestamp, and trust label. The prompt should clearly separate untrusted quotations from application instructions. Test whether a document can impersonate a system message, claim that a user approved an action, or instruct the model to disclose another record.
Verify authorization after the model sees the content
Place an instruction such as “send this file to an external address” in a harmless test document. The model may summarize the instruction, but code-level authorization must stop any email, file transfer, or other side effect. Use an instrumented substitute that records the attempted destination and arguments without sending anything.
Evade simple filters
Repeat with paraphrases, translated text, split instructions across chunks, base64 or other encodings, instructions in metadata, and content that becomes malicious only when combined with another retrieved item. Filtering one phrase is not a boundary control; the decisive check is whether untrusted content can obtain authority.
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Exercise tool and authorization boundaries
Use safe substitutes for production tools. They should expose the requested operation, arguments, identity, token scope, and call sequence while performing no real external action.
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Ask for another user’s record, a different tenant’s file, or an identifier that the caller should not be able to see. Verify authorization against the authenticated caller and resource ownership, not against an identifier supplied by the model.
Vertical escalation
Attempt an administrator action from a normal account, such as changing permissions, exporting regulated data, or deleting records. The service must deny the operation even if the model claims that an emergency or prior approval exists.
Confused deputy and replay
Test whether a broadly privileged service account performs an action for a less-privileged user. Replay an old approval with a changed target, amount, recipient, or file path. Alter arguments between approval and execution. The approval must bind to the exact operation and expire or become invalid when any material field changes.
Chained and hidden calls
Look for a dangerous call hidden among harmless steps: retrieve a document, summarize it, create a draft, then send it. Enforce policy on every call, not only the first or last step. A model must not be able to smuggle a write through a tool described as a read operation.
OWASP recommends application-owned tokens, least privilege, human approval for privileged operations, and handling functions in code rather than giving the model direct authority.
Test retrieval, memory, and persistence
Retrieval isolation
Seed a test corpus with conflicting instructions, poisoned records, cross-tenant identifiers, stale permissions, and secrets that should be redacted. Check document-level authorization before retrieval, not after the model has received the content. Verify that a user cannot infer the existence of a restricted record from search results, snippets, ranking, or error messages.
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Memory isolation
Start sessions for different users and tenants, then attempt to recall another session’s facts. Test summaries, vector memories, conversation caches, and human-edited notes separately. A deletion request should remove the data from every memory layer covered by the product’s retention policy.
Persistence and change testing
Verify that a malicious instruction cannot persist into later sessions through a summary, profile field, saved prompt, or task queue. Repeat after indexing, chunking, reranking, memory, or embedding changes because those changes can alter which poisoned content reaches the model.
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Require structured output with a strict schema, then validate it in deterministic code. Reject unknown fields, malformed JSON, wrong types, missing required values, and values outside policy bounds. Do not pass a model-generated string directly into a shell command, query, URL, file path, email header, or payment request.
- Check recipients and destinations against an allow-list or explicit user selection.
- Check amounts, quantities, dates, and currency against server-side limits.
- Check file paths for traversal, unexpected locations, and tenant ownership.
- Check query scope, row limits, and data classifications before execution.
- Detect prompt text echoed into a command, hidden links, control characters, and output that attempts to bypass a downstream validator.
For high-risk actions, show the human the concrete operation—not merely a model-generated explanation. The reviewer should see the target, arguments, identity, affected data, and expected side effect. OWASP warns that a model acting as an intermediary can hide or manipulate information, so approval of a vague summary is insufficient.
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Log the source and trust label of every input, each retrieved item, the model response, every tool request, policy decision, approval, denial, and side effect. Preserve enough context to reconstruct which content influenced a request without storing unnecessary sensitive data.
Create alerts for repeated boundary probing, unusual tool sequences, privilege mismatches, attempted data exfiltration, and approval anomalies. Test that an operator can revoke tokens, disable a tool, quarantine a source, roll back a change, and identify affected users. A control that blocks an attack but leaves no way to investigate or recover is incomplete.
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- Inject a harmless instruction that would trigger a write if controls failed.
- Confirm that the instrumented tool records the attempted operation and the policy denial.
- Simulate a compromised token and revoke it.
- Disable the affected tool or quarantine the source document.
- Review logs to identify the request, identity, retrieved content, and attempted side effect.
- Restore normal operation only after the policy and regression test are updated.
Define evidence and regression gates
Store each test’s setup, payload class, expected invariant, observed model output, tool trace, authorization result, side-effect result, and reviewer decision. Keep harmless fixtures and instrumented substitutes so the same test can run repeatedly.
Run the suite after every material change to the model, system prompt, retrieval source, tool schema, policy, identity integration, or orchestration code. A prompt adjustment can change tool selection; a reranker change can expose a poisoned document; a new API scope can invalidate a least-privilege assumption.
There is no universal pass-rate threshold established for these tests. Set acceptance criteria from the consequence of failure: zero unauthorized high-impact side effects, zero cross-tenant reads, zero secret disclosure on protected paths, and explicit review for residual failures that occur only in model text. OWASP’s red-teaming initiative emphasizes standardized methodology, benchmarks, and tools for repeatable evaluation.
Compare architectures by control quality
When comparing an AI red-team platform, LLM security testing tool, internal harness, or agent design, score the controls rather than the number of jailbreak prompts it advertises.
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| Axis | Questions to ask |
|---|---|
| Boundary clarity | Can the system distinguish instructions from external data and show provenance at each handoff? |
| Privilege scope | Are identities, tokens, and tools least-privileged and operation-specific? |
| Deterministic enforcement | Do authorization, validation, and approval happen outside the model? |
| Side-effect safety | Are writes reversible, rate-limited, sandboxed, and human-approved where appropriate? |
| Testability | Can attacks run safely with instrumented substitutes and reproducible traces? |
| Observability and recovery | Are decisions logged, alerts actionable, and credentials revocable? |
| Framework mapping | Can findings map to OWASP, NIST, ISO, MITRE ATLAS, or applicable regulation? |
OWASP’s crosswalk published September 1, 2026 maps 51 generative-AI vulnerabilities from four source lists to controls in 25 frameworks, including NIST, ISO, MITRE ATLAS, and the EU AI Act. Such a mapping helps communicate coverage and gaps; it does not replace application-specific boundary tests.
A practical release checklist
- The data-flow and privilege map includes users, instructions, external content, model, memory, tools, identity services, stores, APIs, and approvers.
- Every tool has a documented operation, identity, scope, reversibility, and approval rule.
- System prompts contain no credentials or authority and are not treated as enforcement.
- Direct and indirect injection tests use the real input channels and harmless data.
- Retrieval and memory tests cover tenant isolation, provenance, poisoning, deletion, and retention.
- Tool tests cover horizontal access, vertical escalation, confused deputy behavior, replay, argument substitution, and chained calls.
- Outputs pass schema, policy, destination, and data-loss checks before reaching a tool or user.
- High-impact approvals display the exact operation and become invalid when material arguments change.
- Logs capture trust labels, content provenance, tool traces, policy decisions, approvals, and side effects.
- Operators can revoke credentials, disable tools, quarantine sources, and roll back changes.
- The regression suite runs after model, prompt, retrieval, tool, identity, policy, or orchestration changes.
How to interpret a failure
Classify the failure by the boundary that failed. If untrusted text was accepted as authority, improve provenance and instruction-data separation. If the model requested an unauthorized operation but code blocked it, the model behavior is still worth monitoring, but the enforcement boundary held. If code executed the request with excessive scope, fix identity, authorization, or tool design before tuning prompts. If a human approved a misleading summary, improve the approval view so it exposes the concrete side effect.
This classification prevents teams from treating every refusal failure as equally severe. The highest-priority defects are unauthorized state changes, cross-tenant reads, secret disclosure, and controls that cannot be reconstructed or recovered. Prompt wording can reduce attack frequency, but only application-owned boundaries contain the impact when wording fails.
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