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What counts as an agent input?
An agent can be influenced by more than a user’s typed request. Map every route into its context and every source that can shape a plan, parameter, response, or state change.
- Direct input: prompts sent through a user interface or API.
- Retrieved content: web pages, search results, documents, and other RAG sources.
- Tool and API responses: including errors and data returned by external services.
- Stored context: memory reads, conversation history, and other persisted state.
- Uploads and multimodal data: parsed files, images, audio, or video.
- Other agents: messages or proposed actions passed between agents.
Record what each source can affect. Content that can alter a response is a risk; content that can influence a tool parameter or state-changing action needs controls at the execution boundary as well. External content should be handled as data, not as a new source of authority. OWASP’s AI Agent Security Cheat Sheet and AISVS 1.0 address these trust and input-handling concerns.
How should inputs be normalized and constrained?
Define accepted representations before validation. Canonicalize encodings and formats so equivalent values cannot bypass checks, then apply explicit limits and rejection behavior.
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- Specify required fields, strict types, allowed values or enums, numeric ranges, and maximum lengths.
- Reject unexpected fields where the tool contract does not permit them.
- Set size limits for requests, files, and extracted content. Reject oversize content rather than truncating it unpredictably; truncation can change meaning.
- For images, audio, and video, account for embedded or hidden instructions. Checking only extracted text does not cover every way multimodal content may affect the model.
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How do you validate tool calls before execution?
Put deterministic checks in the application path that dispatches a tool. A model-generated call is a proposal, not proof that the user is authorized to perform the action or that its arguments are safe.
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- Check the tool and identity. Confirm the tool is allowlisted and that the current user or session may invoke it.
- Validate the full argument schema. Check required fields, types, allowed values, length and range limits, and whether fields are valid together.
- Apply business and state rules. Check facts that depend on current account state, resources, or policy; a schema alone cannot establish them.
- Check intent and impact. Confirm the action still serves the user’s original task. Require confirmation or step-up approval for consequential or destructive operations.
- Dispatch only after all checks pass. Enforce authorization and policy in the execution path, not solely in the prompt or the model’s own reasoning.
A database update, for example, needs argument and authorization checks, plus a database role scoped to the permitted records; a destructive change may also require explicit confirmation. AWS’s Agentic AI Lens guidance AGENTSEC02-BP02 recommends validating tool parameters against a defined schema and sanitizing tool outputs before returning them to the agent.
Which validation layer should enforce each rule?
Use complementary controls because they answer different questions. No single layer establishes that an action is safe, authorized, and appropriate.
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| Control | What it can decide | What it cannot guarantee |
|---|---|---|
| Constrained model or tool schema | Can prevent many malformed argument shapes during generation. | Cannot replace application validation, authorization, or checks against external state. |
| Application schema validation | Can deterministically check types, values, ranges, lengths, and relationships between fields before tool logic runs. | May not be the right place to manage separately maintained business policy. |
| Gateway or policy authorization | Can independently enforce permissions and business rules apart from generated text and tool code. | Needs accurate identity, action, and resource context. |
| Prompt-injection classifier or guardrail model | Can screen content or proposed actions for semantic attack patterns. | Adds latency and cost and can itself be attacked; it is not a sole enforcement control. |
| Sandboxing and least privilege | Can limit the impact of a missed check through restricted permissions and execution boundaries. | Does not prove that an input is safe or that an action matches the user’s intent. |
OWASP’s Cornucopia AAI8 treats tool execution as a high-risk agent action. Pair a deterministic constraint with a damage limit for each action rather than relying on a single model-side filter.
How should tools be isolated, and what happens on failure?
Assume that some validation may miss an unsafe request. Restrict each tool to the minimum permissions it needs, and isolate execution so a failure cannot freely reach unrelated systems.
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- Use least-privilege identities and narrowly scoped network and filesystem access.
- Set timeouts and limits for memory, concurrency, and output size.
- Bound or paginate large results, and record when output has been truncated.
- Return structured, sanitized errors. Do not expose stack traces, credentials, or internal infrastructure details to the agent or user.
- Fail closed when approval, policy enforcement, or required audit controls are unavailable for a consequential operation.
These controls limit damage; they do not make an unsafe input trustworthy. AWS’s AGENTSEC02-BP02 guidance discusses execution constraints, error handling, and validation around tool use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should tool outputs and generated responses be checked?
Validation must cover return traffic as well as incoming requests. A tool response can contain hostile text, malformed data, or content too large to safely feed into the next reasoning step.
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- Validate tool responses against the expected output schema and size limits.
- Sanitize returned content and errors before placing them back in agent context.
- Validate generated output before displaying it or passing it to another system.
- Keep secrets and internal diagnostics out of user-facing failures.
Do not assume that a trusted tool makes every value it returns safe to show or reuse. OWASP’s AI Agent Security Cheat Sheet covers output validation, while AWS’s agent security guidance addresses sanitizing tool outputs before they return to the agent.
How can you test the validation pipeline?
Test the complete path from input to execution and back, including whether valid work still succeeds. Include cases such as:
- Direct attempts to override instructions and indirect instructions embedded in retrieved documents.
- Malformed, out-of-range, unexpected, and oversized tool parameters.
- Calls to unauthorized tools or actions that exceed the user’s permissions.
- Memory poisoning, attempts to exfiltrate data, and recursive or resource-exhausting calls.
- Image, audio, and other multimodal inputs that may contain embedded instructions.
- Benign control cases that should pass, to detect overblocking of ordinary tasks.
Review validation failures and unusual activity without logging sensitive data unnecessarily. Repeat tests after material changes to prompts, tools, memory, retrieval, policies, or model providers. OWASP’s agent security guidance and AWS’s Agentic AI Lens provide implementation and testing context. Neither the cited OWASP nor AWS guidance supplies a directly applicable measured percentage for how much these controls reduce attacks, so no effectiveness rate should be inferred from them.
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