Validate agent inputs at every trust boundary—not just in the chat box. Treat user text, retrieved documents, tool results, memory, uploads, and messages from other agents as untrusted; check tool arguments and authorization before execution; and validate returned data before it re-enters the model or reaches a user.
What counts as an agent input?
An agent can be influenced by far more than a user’s latest message. Any content that reaches its reasoning, planning, tool parameters, or memory is an input worth accounting for. OWASP’s LLM Prompt Injection Prevention Cheat Sheet and AI Agent Security Cheat Sheet emphasize treating external content as untrusted.
- Direct input: chat messages, API fields, and form values.
- Retrieved or uploaded content: web pages, search results, documents, images, audio, or video.
- Tool and service responses: API results, database records, errors, and logs.
- Persistent or shared context: memory reads and messages from other agents.
Map each source to what it can affect: a displayed answer, a plan, a parameter, or a state-changing action. A document that cannot invoke a tool directly may still try to influence the model into doing so.
How to build an input-validation pipeline
1. Map sources and trust boundaries
List every route into the agent, including retrieval, file parsing, memory, and inter-agent communication. Record which components can change data and which can authorize or execute an action. This inventory helps prevent the common mistake of validating only the user interface while leaving retrieved text or tool output unchecked.
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2. Normalize data and define strict schemas
Canonicalize encodings and representations before checking values. For each tool, specify required fields, exact types, allowed values, length and range limits, and whether unknown fields are rejected. Add cross-field rules where combinations matter. For example, a transfer’s destination and amount may each be valid individually but invalid together under a business rule.
Reject oversized content rather than truncating it unpredictably: truncation can remove context that changes meaning. For images, audio, and video, account for instructions embedded in the original media, not only text extracted from it. The OWASP AI Security Verification Standard (AISVS) 1.0 includes controls for input normalization, limits, representation handling, multimodal input, and tool schemas.
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3. Keep instructions separate from data
Make the instruction hierarchy explicit: retrieved pages, files, emails, and tool results are data to analyze, not authority to override system policy or the user’s request. Label untrusted content clearly and preserve its boundaries when placing it in context. Pattern matching can help screen suspicious text, but it cannot reliably stop indirect prompt injection on its own; see the OWASP prevention guidance.
4. Mediate every tool call before dispatch
Put deterministic checks in the execution path rather than asking the model to police itself. Before a tool runs, verify the tool is allowed, the current user or session is authorized, the arguments match the schema, business and state-dependent rules pass, and the action still serves the user’s original task. High-impact actions may require approval or an additional confirmation.
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AWS’s Agentic AI Lens guidance, AGENTSEC02-BP02, recommends validating every tool parameter against a defined schema before execution and sanitizing tool outputs before returning them to the agent.
5. Limit the impact of a missed check
Validation reduces risk but does not prove that an input is safe or that an action is appropriate. Use least-privilege identities and isolation so a mistaken or manipulated call has limited reach. Scope network and filesystem access; set time, memory, concurrency, and output limits; and fail closed when approval, policy, or audit checks fail for consequential operations. OWASP’s Cornucopia AAI8 threat-model card treats tool execution as a high-risk action requiring defense in depth.
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6. Validate return traffic and errors
Check tool responses against expected output schemas and enforce size limits before returning them to the model. Sanitize content before it is shown to a user or fed back into context. Bound or paginate large results and record when truncation occurs. Return structured, safe errors rather than stack traces, credentials, or internal infrastructure details.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which validation layer should handle each decision?
These controls complement one another; none is a complete substitute for the others.
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| Layer | What it can check | What it cannot guarantee |
|---|---|---|
| Constrained model or tool schema | Reduces malformed parameter shapes during generation. | Cannot establish all external-state facts or replace authorization and application checks. |
| Application schema validation | Deterministically checks types, values, ranges, lengths, and field relationships just before tool logic. | May not be the right place to manage separately governed business policy. |
| Gateway or policy authorization | Enforces permissions and business rules independently of generated text and tool code. | Depends on accurate identity, action, and resource context. |
| Prompt-injection classifier or guardrail model | Can screen untrusted content and proposed actions for semantic attack patterns. | Adds latency and cost and can itself be susceptible to injection; it is not a sole control. |
| Sandbox and least privilege | Limits potential damage if another check misses a problem. | Does not prove an input is safe or that an action matches the user’s intent. |
Pair each consequential action with both a deterministic constraint and a damage limit. For example, a database update can require schema and authorization checks, a database role scoped to permitted records, and confirmation for destructive changes.
How to test and monitor the pipeline
Test security cases alongside ordinary requests so controls do not silently block legitimate work. Include prompt overrides, indirect instructions in retrieved documents, malformed or oversized parameters, unauthorized tools, memory poisoning, attempted data exfiltration, and recursive or resource-exhausting calls. Include multimodal inputs where the agent accepts them.
Repeat tests after material changes to prompts, tools, retrieval, memory, policies, or model providers. Review validation failures and anomalies, while keeping logs useful without exposing sensitive content. AWS and OWASP provide implementation and threat-model guidance, but do not publish a directly applicable effectiveness percentage for these validation controls; avoid treating an unmeasured reduction as a guaranteed result.
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