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You cannot guarantee that an AI agent will never follow a prompt injection. You can, however, design the agent so a successful injection cannot cause an unacceptable result. Keep webpages, emails, documents, records, API responses, memory and tool output untrusted; enforce authorization and argument checks in application code; give tools the minimum permissions; isolate execution and memory; and require explicit approval immediately before consequential actions.
This guide shows how to apply those controls to agents that browse, read mail, call APIs, run code or change external systems. It also explains what prompt filtering and stronger system prompts can and cannot do, and how to test the remaining risk.
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What is prompt injection, and why can it make an agent unsafe?
Prompt injection is an instruction-confusion attack. Malicious text is placed in input so a model treats the attacker’s instructions as higher-priority guidance than the user’s task. A direct injection comes from the person interacting with the agent. An indirect injection is hidden in content the agent retrieves or processes, such as a webpage, email, PDF, support ticket, calendar entry or database row. OWASP lists prompt injection as LLM01:25 in its 2025 Top 10 for Large Language Model and Generative AI Applications.
Agents increase the consequence because they can do more than generate text. A summarizer with no tools may produce a bad summary. An agent with mailbox, filesystem, calendar, payment or API write access can send a message, expose confidential data, delete records or make an external change. The practical risk is therefore determined less by the model’s conversational behavior than by the capabilities connected to it.
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OpenAI describes the basic pattern as a third party misleading the model by injecting instructions into the conversation context. Because instructions and data share that context, no single delimiter, system prompt or filter creates a complete security boundary.
Can prompt injection be fully prevented?
No. Official guidance from OWASP says no mitigation eliminates prompt injection entirely. Treat eventual compromise as a design assumption: if the model follows hostile text, the surrounding system should still prevent unauthorized or irreversible effects.
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How do I define a safe action contract before building the agent?
Start with a written contract that describes the exact job and the boundaries around it. Avoid a direction such as “review my email and take whatever action is needed”; it gives hidden content too much room to redefine the task.
Specify the purpose and allowed inputs
- State the user-visible purpose in one narrow sentence, such as “classify incoming support messages and draft replies for approval.”
- List the permitted sources, for example one mailbox folder or a defined set of public documentation sites.
- Identify data the agent must not access, including unrelated mailboxes, private customer records or another tenant’s memory.
- Define how long retrieved content and intermediate results may be retained.
Specify tools and prohibited actions
- Name each allowed tool and the operations it supports.
- Mark operations as read-only, reversible or consequential.
- Prohibit actions outside the task, such as changing account permissions, uploading secrets, disabling logging or contacting an unapproved destination.
- State which operations require a human decision and what evidence must be shown before approval.
Put this contract in application configuration and authorization policy, not only in a prompt. A model can propose an action, but code must decide whether that action is permitted.
How should retrieved content and memory be isolated from instructions?
Assume every external source is hostile input by default. That includes content returned by a trusted service: a compromised page, poisoned document, malicious calendar invite or manipulated API field can carry an indirect injection.
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Preserve provenance and delimit data
Record the source, tenant, timestamp and retrieval operation for each item. Pass content to the model in a clearly identified data field rather than concatenating it with policy text. Parsing HTML, extracting text from a PDF or converting an email to a structured record can reduce accidental control characters, but parsing is not authorization; the resulting fields remain untrusted.
Labels such as “UNTRUSTED WEBPAGE” help the model distinguish roles, but labels alone are not a security boundary. Enforce the separation in the workflow and in code. For example, a webpage extraction step should be able to return text and links, not silently add a new tool or alter the agent’s system policy.
Keep memory and tenants separate
Use separate storage namespaces and credentials for users, organizations and sessions. Do not let an agent write arbitrary instructions into durable memory and then treat those instructions as trusted policy on a later run. Store facts with provenance and an expiration or review state; keep executable instructions in controlled configuration.
When a task ends, discard temporary content that is not needed. Never place API keys, session cookies or other secrets in prompts or long-lived conversational memory. If a model needs to refer to a secret, have a protected service perform the operation without revealing the secret value to the model.
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Give an agent only the access required for its current task. Prefer read-only credentials, narrow API scopes and short-lived, task-specific identities. Separate components so a compromised browsing or planning step cannot directly reach unrelated systems.
| Design choice | Safer implementation | Risk if omitted |
|---|---|---|
| Credential scope | Read-only token for one mailbox folder or API resource | A confused agent can alter or enumerate unrelated data |
| Tool exposure | Explicit per-agent allowlist of named operations | Hidden content can steer the model toward powerful or unknown tools |
| Identity | Short-lived service identity tied to user, tenant and task | Stolen or misused credentials remain useful beyond the job |
| Network access | Sandbox with approved destinations and blocked internal networks | Browser or code execution can probe systems or exfiltrate data |
| Reversibility | Draft, queue or stage changes before committing them | One mistaken call creates an immediate, hard-to-undo effect |
| Limits | Bound retries, runtime, spend, records processed and chain length | An injection can cause loops, bulk actions or unexpected cost |
Ask what happens if the agent is fully manipulated. Design that outcome to be harmless or reversible wherever possible. For example, a mail assistant can create a draft in a review folder instead of sending; a data-maintenance agent can produce a change set instead of deleting rows.
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How do I authorize every tool call outside the model?
Authorization belongs at the tool boundary. The component that actually invokes an API or executes a command must independently verify the caller, operation and arguments. Do not use the model’s explanation, a confidence score or a keyword filter as permission.
- Authenticate the requesting context. Bind the call to the signed-in user, tenant, session and task. Reject missing or expired context.
- Check the tool allowlist. Fail closed if the requested tool or operation is not explicitly enabled for this agent.
- Validate the schema. Parse arguments with a strict schema, reject unknown fields and enforce types, lengths and formats.
- Apply policy to values. Check destinations, recipients, file paths, record identifiers, data classifications and amounts against deterministic rules.
- Check approval state. Require a fresh approval token for operations classified as consequential. Bind that approval to the exact arguments, destination and expiry time.
- Enforce rate, cost and volume limits. Stop excessive retries, records, messages, spend or execution time even when each individual call looks valid.
- Log the decision and execute with scoped credentials. Record the policy result and an audit identifier without unnecessarily storing secrets.
Use structured outputs between planning and execution. A planner may return an object containing an operation and proposed fields; an executor should accept only fields that pass validation. Free-form text should never be interpolated directly into shell commands, database queries, URLs or recipient lists.
When should a human approve an agent action?
Require explicit confirmation immediately before a side effect that sends, buys, deletes, publishes, grants access, modifies an external record or discloses sensitive information. Approval should guard the real execution step, not merely the plan several model calls earlier.
Show the exact proposal
The approval screen should identify the action, arguments, destination, account or identity used, data to be shared, expected cost and any irreversible consequence. “The agent wants to proceed” is insufficient. A person should be able to see the exact recipient list, file path, purchase total or records affected.
Bind and expire approval
Issue approval for a specific operation and parameter set. If the agent changes a recipient, amount, destination or payload, require approval again. Expire approvals quickly and invalidate them when the session, user or task changes.
Use safer defaults
Keep tools that can affect external systems in approval-required mode by default. Drafting, simulation and dry-run modes let a user inspect results without granting execution authority. Approval is a security control, not a rubber stamp: make rejection easy and do not pressure the reviewer with vague explanations.
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How should I sandbox browsing, code execution and external connectors?
Run browser automation, downloaded files and generated code in an isolated environment. The sandbox should have only the network routes, filesystem paths and runtime privileges needed for the task. Keep production credentials and internal service endpoints outside that environment.
- Use separate processes or containers for untrusted parsing and execution.
- Block access to internal metadata services, local sockets and unrelated networks.
- Mount temporary, minimal filesystems; make sensitive directories unavailable.
- Apply CPU, memory, duration, process-count and outbound-data limits.
- Reset the environment between unrelated users or jobs.
- Return only structured results to the decision component, not arbitrary executable content.
Separate planning from execution. The model can suggest a sequence, while a deterministic executor checks each step and performs only approved operations. This prevents a late instruction in a webpage or tool response from silently changing both the plan and the execution privileges.
What should monitoring and incident response capture?
Log enough context to reconstruct security decisions without creating a second sensitive-data store. Useful events include the user and tenant, source provenance, retrieved-content identifiers, model-request identifier, proposed tool call, policy result, approval identifier, executor identity, destination and outcome.
Alert on repeated authorization failures, attempts to invoke disabled tools, unusual tool chains, unexpected destinations, rapid increases in records or spend, and patterns associated with data extraction. Keep a kill switch that disables a connector or pauses all consequential actions without redeploying the model.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I test an agent for direct and indirect prompt injection?
Test the complete system, not just the system prompt. Use harmless payloads and instrumented substitute tools so an attack can be observed without sending real mail, spending money or changing production records.
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Build a test matrix
- Direct attacks: a user asks the agent to ignore its contract, reveal hidden instructions or bypass approval.
- Indirect attacks: a webpage, email, attachment, calendar event, ticket or database field contains instructions aimed at the agent.
- Evasive attacks: instructions use encoding, unusual typography, multiple languages, images, delayed steps or wording that avoids screened keywords.
- Tool-output attacks: a connector returns a fabricated “next step” or an instruction to disclose credentials.
- Memory attacks: content attempts to write a persistent rule that affects a later user or tenant.
Measure the controls that matter
Record whether the agent recognized untrusted content, attempted a prohibited call, whether the tool boundary rejected it, whether approval appeared at the correct point, and whether data crossed the intended tenant or destination boundary. A successful defense is not merely a refusal in text; it is the absence of an unauthorized side effect.
Retest after every meaningful change
Repeat the suite after changing prompts, models, retrieval, memory, tools, connectors, authorization policy or provider settings. Keep regression cases for every discovered failure and include keyword-free variants so a filter cannot become the only defense.
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| Control | What it helps with | Why it cannot stand alone |
|---|---|---|
| System-prompt hardening | Clarifies priorities and reminds the model to treat retrieved text as data | The model still receives instructions and data in one context and may be persuaded |
| Delimiters and labels | Make source boundaries easier for the model to recognize | Formatting does not enforce permissions in an external system |
| Keyword or content filters | Catch obvious attack phrases and reduce nuisance content | Attacks can be encoded, multilingual, indirect or phrased without screened words |
| Model selection | Some models may resist particular attack patterns better | Robustness changes by version and does not remove tool or credential risk |
| Human review | Stops a well-presented risky action when the reviewer sees the real details | Review can be bypassed if it occurs too early, is vague or is enabled only for some tools |
| Least privilege and deterministic authorization | Limits what a manipulated agent can actually do | Still requires isolation, monitoring and recovery for residual risk |
Use these measures as layers. The stronger architecture combines untrusted-data handling, least privilege, code-enforced authorization, approval checkpoints, sandboxing, monitoring and adversarial testing.
What does a secure implementation workflow look like?
- Map the threat surface. Inventory every input source, model, memory store, connector, credential, side effect and tenant boundary.
- Write the action contract. Define purpose, permitted sources, tools, prohibited operations, data retention and approval classes.
- Start read-only. Launch with the narrowest identity and no external writes. Add one capability only when the task requires it.
- Build the policy gateway. Validate user context, tool identity, schemas, values, destination, data classification, limits and approval before execution.
- Isolate untrusted work. Sandbox browsing and code, separate memory namespaces and keep secrets out of prompts.
- Add review at the commit point. Display exact parameters and require an authorized confirmation for consequential operations.
- Instrument and test. Log security decisions, run direct and indirect injection cases, and verify that substitute tools observe no unauthorized effect.
- Operate for failure. Maintain kill switches, credential revocation, rollback and an incident process; review logs and update tests after changes.
How can I review an agent design before deployment?
- Could hostile text alter a tool name, argument, destination or approval state?
- Is every tool call authorized by deterministic code outside the model?
- Are unknown tools, malformed arguments and missing approvals rejected by default?
- Can the agent read or write data outside the user, tenant and task scope?
- Are browser, parser and code-execution environments isolated from secrets and internal networks?
- Does durable memory preserve provenance and prevent untrusted text from becoming policy?
- Are high-consequence effects paused until a person sees exact action details?
- Are retries, chain length, runtime, volume and spend bounded?
- Can operators detect anomalous destinations or extraction attempts?
- Have direct, indirect, encoded, multilingual and keyword-free cases been tested after the latest change?
If any answer is “no,” treat the agent as not ready for that capability. You can still deploy a reduced, read-only version while the missing control is added.
Frequently Asked Questions
What is the difference between direct and indirect prompt injection?
A direct injection is supplied by the person chatting with the agent. An indirect injection is carried inside content the agent retrieves or processes, so an attacker may target a webpage, document, email or record without ever speaking to the agent’s user.
Should an AI agent ever receive production API keys?
Avoid placing production secrets in the model context. Use a protected executor with short-lived, narrowly scoped credentials, and expose only validated operations rather than the secret value or a general-purpose shell.
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How often should prompt-injection tests be rerun?
Rerun them whenever the model, prompt, retrieval pipeline, memory behavior, connector, authorization policy or provider configuration changes. Keep every prior failure as a regression case and add variants that do not rely on obvious attack words.
What should I do if an agent may already have followed an injection?
Pause or disable the affected connector, revoke task credentials and invalidate pending approvals first. Then inspect tool and destination logs, rotate any exposed secrets, stop queued side effects and use rollback or restoration procedures before re-enabling the workflow.
Is a read-only agent automatically safe?
Read-only access greatly reduces impact but does not eliminate risk. The agent can still disclose information, cross tenant boundaries, poison memory or trigger downstream processes, so data isolation, output controls and monitoring remain necessary.
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