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Prompt injection is a vulnerability in how an application supplies context to an AI model and controls what the model can do with it. A user message, retrieved document, webpage, or API response can steer the model toward an unintended answer or tool call. The practical defense is not a magic prompt or filter: test every content path and enforce permissions in the code that executes actions.
What is prompt injection?
OWASP defines prompt injection as an attack in which input alters a large language model’s behavior or output in unintended ways. It describes two main delivery paths in its LLM01:2025 Prompt Injection guidance:
- Direct injection: an attacker puts instructions in user-controlled input, such as a chat message or an API request field.
- Indirect injection: instructions arrive inside external content the model reads, such as a file, webpage, email, retrieved document, API response, or tool result.
Indirect instructions may be hard for a person to notice, yet still be parsed by a model. The security issue is not limited to unusual wording: the application has placed untrusted content in a position where it can influence model behavior.
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Prompt injection is therefore an application security problem as well as a model behavior problem. The model may interpret content unpredictably, but the application determines what information enters its context, what credentials and tools are available, and whether a proposed action is authorized.
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Where should an API team look for exposure?
Trace both sides of the interaction: every source of model context and every capability the model can influence. A chat box is only one possible entry point.
Map the content paths
List each source that can become model context, including:
- Request fields and user messages
- Conversation history and persisted memory
- Retrieved documents and search results
- Fetched webpages, uploaded files, and email bodies
- Responses from internal or external APIs
- Tool results returned to the model
- Images or other modalities, if the application processes them
Record which sources are trusted, user-controlled, or externally controlled. Content labels and delimiters can help organize context, but they do not make untrusted data safe or enforce a security boundary.
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Map the authority paths
For each tool or downstream system, identify the service identity used, its permissions, the resources it can reach, and whether it can cause side effects. Include destinations that receive model output, such as databases, HTML renderers, workflow systems, or other services. Risk increases when a model can reach sensitive data or initiate consequential actions.
Can an injected document or API response make an agent call a tool?
It can influence an agent to propose a tool call if that content is included in the model’s context. Whether the call actually runs depends on the application’s tool-execution design. If the application automatically executes model-proposed calls with broad credentials, the content may contribute to an unauthorized action. If execution code independently checks identity, scope, arguments, and approval, it can deny a call even when the model proposes it.
Possible consequences include disclosure of sensitive information, manipulated answers, unauthorized function use, commands sent to connected systems, or interference with critical decisions. Multimodal inputs can add exposure when instructions are hidden in images or other formats. The impact depends on the data and actions available to the application; an unsafe answer and an executed high-impact operation are not equivalent outcomes.
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Do not treat a model’s refusal as authorization. Keep access decisions outside the model and re-check them at the point where a tool call or other action is executed.
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Test the trust boundaries and observable effects, not just whether the model repeats or refuses a suspicious phrase. OWASP recommends regular penetration testing and breach simulations that treat the model as an untrusted user; its AI Agent Security Cheat Sheet also provides categories for structuring agent abuse cases.
- Document the boundaries. List trusted instructions, user inputs, fetched or retrieved content, tool and API outputs, memory, model output, and downstream actions. For every action, record which role or service identity executes it and what it is allowed to access.
- Specify test cases before running them. For each case, record the attacker-controlled channel, intended security violation, required context, expected safe behavior, and observable result. Use test accounts and harmless dummy secrets rather than live credentials or customer data.
- Exercise direct and indirect delivery. Put adversarial instructions in user input, then test equivalent instructions in each supported external channel: for example, a retrieved document, webpage, file, email, API response, or tool result. A chat-only test does not exercise the retrieval or tool-output boundary.
- Substitute instrumented tools for real side effects. Use sandbox integrations that record whether a call was proposed and whether it executed. Verify the execution layer checks the user identity, resource scope, and each parameter; test both allowed and prohibited actions.
- Check effects across every observable channel. Determine whether dummy data appears in a response, tool call, log, markup, or another instrumented destination. Verify prohibited calls are denied and that the intended benign task still works. A clean user-facing answer cannot show that no data escaped through another path.
- Vary the attack form. Test relevant obfuscated, split, multilingual, and keyword-free variants. OWASP’s prompt-injection guidance specifically cautions against testing only attacks that contain a filter’s known keywords.
- Retest and retain evidence. Run the suite before deployment and after material changes to prompts, tools, memory, retrieval, policies, or model providers. Record the tested version and configuration, case, expected and observed result, approval, denial or timeout behavior, and any accepted residual risk.
OWASP’s agent checklist includes prompt override, tool misuse, privilege escalation, memory poisoning, data exfiltration, recursive tool abuse, approval bypass, and multi-agent chaining. Use these as prompts for application-specific cases, not as a universal benchmark.
What should a useful test case contain?
A compact case record makes results reproducible and helps distinguish model behavior from application enforcement. For example:
| Field | What to record |
|---|---|
| Entry channel | The exact user field, retrieved source, tool output, or memory path containing the test content |
| Security objective | The specific prohibited disclosure or action the case attempts to trigger |
| Setup | Test identity, relevant permissions, dummy data, and required application state |
| Expected behavior | What the application should return, deny, require approval for, or leave unchanged |
| Observed evidence | Response, proposed and executed tool calls, authorization decision, destination effects, and relevant logs |
| Configuration | Model/provider, prompt and policy version, tools, retrieval and memory settings, and test date |
Keep fixtures free of real secrets and unnecessary personal data. A finite suite passing means only that those cases produced their expected outcomes under the recorded configuration; it does not establish that the application is secure against other inputs or changed conditions.
Which controls should accompany testing?
- Use least privilege. Give the model-backed application its own credentials, with only the scopes it needs. Keep tools narrow and read-only when that meets the product requirement.
- Authorize at execution time. In ordinary application code, check the caller, resource, operation, and argument values for every proposed tool call. Require specific human approval for high-risk actions, tied to the exact action being approved.
- Keep untrusted content distinguishable. Separate and label it in context, but do not rely on labels, delimiters, or system-prompt wording to enforce permissions.
- Secure each output destination. Treat generated content as untrusted: safely render HTML, use parameterized database queries, and re-authorize operations in connected systems. A keyword scan of generated text does not establish that downstream use is safe.
- Monitor with restraint. Observe security-relevant decisions and tool activity without logging credentials, secrets, or more sensitive content than operations require.
- Layer guardrails. Input filters and separate guardrail models can support the design, but they do not replace authorization, least privilege, or human approval.
OWASP says retrieval-augmented generation and fine-tuning do not fully mitigate prompt injection, and that foolproof prevention within the model is unclear. A system prompt, delimiter, or filter may reduce some risks, but none should be treated as proof of immunity. See OWASP’s prompt-injection guidance and prevention cheat sheet for its defense-in-depth recommendations.
How should teams interpret a passing test suite?
Use passing tests as release evidence about the cases and configuration actually exercised, not as a security guarantee. A change to a prompt, model provider, retrieval pipeline, memory behavior, tool permission, or approval flow can alter the boundary and should trigger relevant regression tests.
The most important result is whether the application kept authority where it belongs: in deterministic execution checks, scoped credentials, and explicit approval for consequential actions. Model behavior can be one signal, but the test should verify what the system did, not only what it said.
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