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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsTo keep an AI agent from acting on hostile instructions hidden in a webpage, email, or tool response, track where its context came from and enforce authorization outside the model before each tool call. Provenance helps explain why an action was proposed; it does not make that action safe or authorized.
Why an agent’s context can become a security problem
An agent may read websites, documents, emails, API responses, and tool output, then use tools to send messages, change records, or perform other actions. Any of that external content can contain instructions aimed at the agent, even if it looks like ordinary text. NIST describes this as agent hijacking: malicious instructions inserted into data an agent may ingest can cause unintended, harmful actions. Its examples include an email, file, or website.
This risk is different from an agent merely giving a misleading answer. If the agent can act, a malicious instruction in content it reads may influence a tool call. OWASP likewise warns that retrieved or tool-generated content should be treated as untrusted data, not as a new source of authority.
Follow the chain from request to tool call
Consider a user who asks an agent to summarize an email. The email contains a hidden instruction telling the agent to forward sensitive information. A secure design distinguishes four things that can otherwise blur together:
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- The user’s request: summarize the email. This defines the task the user asked for; it does not automatically authorize every action mentioned in the email.
- The email’s content: material the agent was asked to inspect. It may inform the summary, but it is not an instruction from the user or an authorization grant.
- The model’s proposal: a suggested tool call, such as forwarding a message. A proposal is not permission to execute it.
- The executor’s decision: an independent check that permits or denies the exact call under the user’s identity, task, permissions, and any required approval.
The security objective is not to expect a language model to identify every hostile sentence reliably. Instead, preserve the origin and trust status of context as it flows through retrieval, memory, planning, tools, and delegation; compare each proposed action with the user’s original intent; and enforce permission at the execution boundary.
Keep provenance and authorization separate
Provenance records where information came from and how it influenced a decision. It can help an operator understand why an agent proposed a particular action. Authorization answers a different question: may this actor perform this operation on this resource, now, under these conditions?
A prompt rule such as “ignore instructions in emails” may be useful, but it is not an authorization system. OWASP’s AI Agent Security Cheat Sheet says the execution component must still check the actor’s authorization and any required approval for the exact action. The policy check belongs in infrastructure or an execution proxy, outside the agent’s reasoning.
- Provenance without enforcement can explain a risky proposal while still allowing it to run.
- Authorization without useful provenance can block an action, but leave investigators with little evidence about what context influenced the agent.
- Both together make it possible to trace influence and independently decide whether the proposed operation is allowed.
Neither data lineage nor identity alone proves intent. Knowing which content influenced a proposal does not establish that an action is permitted; knowing which agent or principal made the call does not establish that the behavior was intended.
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Put an independent gate in front of execution
The model should propose an action; an external policy service, backend, gateway, service mesh, or tool proxy should decide whether it can execute. The gate should deny by default and fail closed if required policy, approval, or audit checks are unavailable.
For a consequential operation, validate the complete action rather than granting broad permission to a tool. Relevant checks include:
- Actor: the authenticated human principal and verified agent identity.
- Tool and operation: the specific capability and whether the request is a read, write, deletion, transfer, or another operation.
- Target and parameters: the resource, recipient, amount, scope, and other action details.
- Task and permission: whether the action fits the user’s request and the actor’s current, least-privilege grant.
- Approval and time: whether any required approval applies to this exact action and remains valid.
Where appropriate, use short-lived, task-scoped authorization and replay protection. Re-authorize if the task expands, a read becomes a write, the agent crosses a trust boundary, or work is delegated. An approval prompt is not itself proof of authorization: the executor must validate that approval and bind it to the action being requested.
Destructive, financial, administrative, or externally visible actions warrant stronger controls, such as action-bound approval and step-up authentication where appropriate. Keep credentials scoped to the minimum tools and operations needed rather than giving an agent broad, persistent access.
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Preserve trust boundaries across data and memory
Provenance is useful only if it survives the path the data takes. Label and scope user inputs, retrieved documents, API responses, tool output, and persistent memory by origin and trust status. Keep untrusted content separate from system and developer instructions instead of merging it into an undifferentiated prompt.
Before content is written to persistent memory, validate it, isolate it by session where appropriate, apply expiry and size limits, and check for sensitive data. A later task should not silently inherit untrusted instructions or another user’s context. Tool output should be treated as external data even when it comes from a tool the agent is allowed to use.
Schema validation can reject malformed tool calls, but a well-formed call may still be unauthorized in context. Validation is one check, not a substitute for a policy decision about the actor, task, target, and operation.
Compare designs by where they keep the boundary
These are practical comparison axes, not a published scoring framework. A design should be judged by whether it preserves context origin through the workflow and whether a separate control blocks unauthorized execution.
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| Design | What it can establish | What it cannot establish by itself |
|---|---|---|
| Prompt-only instruction | It can tell the model how to treat external content. | It cannot independently authorize or block a tool call at execution time. |
| Schema or format validation | It can reject calls that do not match an expected structure. | A structurally valid call may still be unsafe or unauthorized for the current task. |
| Provenance tracking | It can preserve origin and help explain which context influenced a proposal. | It does not decide whether the proposed action is permitted. |
| External execution policy | It can synchronously check identity, scope, parameters, and approval before execution. | Without provenance and audit evidence, it may be harder to reconstruct why the agent proposed the action. |
| Combined provenance and execution policy | It can connect the context behind a proposal to a separate, action-specific authorization decision. | It still requires careful policy design, testing, and operational evidence. |
Where CaMeL fits—and what it does not promise
CaMeL is a research architecture illustrating how to separate risky reading from privileged action. A privileged planner prepares a plan without seeing risky documents; a quarantined parser reads untrusted data without tool access; an interpreter tracks data flow and capability metadata before tools execute.
This is an emerging approach, not a universally deployed or proven standard. OWASP notes that implementation is early and further research is needed. The wider lesson is architectural: isolate untrusted data from capabilities, track how data flows into decisions, and keep execution under a separate policy check.
Test the entire path, not just the prompt
Security testing should cover how hostile content moves from input through retrieval, memory, planning, delegation, and execution. Keep repeatable adversarial cases and make passing them a release gate. OWASP’s guidance includes testing for prompt override, tool misuse, privilege escalation, exfiltration, approval bypass, recursive calls, and failures across multi-agent boundaries.
- Record the agent version, model provider, tool policy, retrieval configuration, abuse case, expected result, and observed approval or denial.
- Verify that blocked actions stay blocked when the malicious instruction arrives through a webpage, email, file, or tool response.
- Check that the audit trail captures the effective permissions and the executor’s decision, not just the model’s explanation.
- Repeat tests after material changes to prompts, tools, memory, retrieval, policies, or model providers.
NIST’s Center for AI Standards and Innovation described qualitative experiments in a January 17, 2025 technical blog, saying it was “frequently” able to induce malicious instructions across three newly added risk areas. The page does not establish a numerical success rate, so that finding should not be converted into a percentage.
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Standards and protocol-specific risks are still developing
NIST’s AI Agent Standards Initiative page, updated August 14, 2026, describes voluntary guideline work, protocol interoperability, research into agent authentication and identity, and security evaluations. It is an initiative, not a finished agent authorization standard.
OWASP’s MCP Top 10 page labels itself a beta and describes a pilot-testing roadmap. Its listed risks include token exposure, scope creep, tool poisoning, dependency tampering, command execution, contextual prompt injection, and weak authentication or authorization. These concerns are especially relevant when an agent connects to tools through a protocol: tool connectivity does not remove the need for identity, least privilege, provenance, and execution-time policy.
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