Field note
Agent Prompt Injection Is Not SQL Injection
separate instruction integrity, tool authorization, data validation and output handling instead of borrowing one control for every layer.
separate instruction integrity, tool authorization, data validation and output handling instead of borrowing one control for every layer.
Quick answer
For agent prompt injection not sql injection, run both threat tests, validate tool arguments in code, isolate secrets and require approval before external side effects.. Start with a harmless, repeatable test and record the account, plan, model or feature, device, browser or client, network, data class, exact error and time. The goal is to decide whether the issue is provider availability, identity, network path, source data, model behaviour, tool permission or workflow design. Those are different faults. A vague “AI issue” label sends the work to the wrong owner.
A useful answer has two parts. First, restore a safe small case. Second, decide whether the workflow is fit for continued use. A page that loads is not proof that a connected source is scoped correctly. A fluent answer is not proof that a source was used. A successful tool call is not proof that the resulting record is correct. Keep those claims separate.
Why this keeps happening
AI chat products look like one box, but a useful result crosses several boundaries. A request can pass through a browser or desktop client, an identity provider, an account or workspace policy, a connector, a source repository, a model, a tool call and an external record. A fault at any point can create the same visible symptom. Limits, defaults, feature flags and model versions also move quickly, so a screenshot from last month is not a lasting control.
For a small UK team, ask four plain questions before changing settings: what should the product do, what data may it see, what may it change and who owns the final decision? If the output is only a draft, label it a draft. If the system can send, publish, approve, delete or change a record, treat it as an integration with a review gate.
Fix path
run both threat tests, validate tool arguments in code, isolate secrets and require approval before external side effects.. Compare a clean conversation or session, a second approved identity and a second approved network where that comparison is safe. Keep the smallest failing case. Do not use live customer data to prove a diagnosis. If the failure follows the provider everywhere, check status and current help. If it follows one account, role, source or network, keep the model prompt out of the first fix.
For this topic, separate instruction integrity, tool authorization, data validation and output handling instead of borrowing one control for every layer. The first change should be narrow enough to undo. Record the old state before changing a permission, source, model, cache, shell setting or network rule. If a workaround needs a broad allowlist, a shared account, a copied secret or an unreviewed write, stop and escalate instead.
Check the boundary
The boundary is the part of the system that should make the decision. A prompt should not be the only access control. A browser agent should not receive a service account with more rights than the workflow needs. A connected file source should not be broader than the approved folder. A fallback model should not receive sensitive data simply because the preferred model is busy.
Use synthetic or redacted data for diagnosis. Keep passwords, tokens, private keys, customer exports and unnecessary personal information out of chats, screenshots and support tickets. If a workflow needs a secret, use the approved secret-management path. If the product cannot show where data goes, pause that sensitive use case and record the uncertainty.
Check the result
A passing test must match the actual need: a complete answer, a grounded source, a correctly scoped file, a human approval, an external record or a clean stop. Test the negative path too. Denial should leave no side effect. A timeout should not create a duplicate. A cancelled action should not send a message. A limit should trigger a documented fallback, not an improvised account-sharing workaround.
Practical SME workflow
Give the work one owner and one reviewer. The requester describes the business outcome. IT or the workspace owner confirms the product and identity path. The data owner confirms the source and retention boundary. The workflow owner defines the action, escalation and stop condition. A reviewer checks the result against the source and acceptance rule.
Keep the first pilot small: one department, one source set, one reversible outcome and one measure. Use a test identity or controlled group. Save the settings snapshot, test input and result, not a raw dump of customer data. Review the workflow after a provider change, connector addition, incident, limit pattern or new failure mode.
Checks before rollout
- run both threat tests, validate tool arguments in code, isolate secrets and require approval before external side effects..
- Keep AI agents scope narrow and documented.
- Check provider status and record the incident state.
- Test the clean path and the denied path separately.
- Log the owner, reviewer, scope and expected result.
- Remove temporary exceptions after retest.
Risk and control
The main risk is false confidence. A clean demo can hide a broad permission, stale source, missing approval or fallback path that nobody has tested. A model can be fluent while wrong, incomplete or influenced by untrusted content. Reduce impact with least privilege, deterministic business rules, source links, reviewer ownership and a real stop route.
For agent prompt injection not sql injection, the control is not “write a better prompt” by itself. run both threat tests, validate tool arguments in code, isolate secrets and require approval before external side effects.. Keep model output advisory wherever the cost of an incorrect action is material. If the provider changes a limit or feature, use the documented operating plan rather than claiming a permanent fix.
What good evidence looks like
Keep a compact evidence pack: current product or policy link, reproduction, account and scope, settings snapshot, approved data class, test input, expected result, actual result, reviewer, owner and next review date. For actions, add the external record or proof that no record was created. For content, add source links and a note about what was independently verified.
Evidence should explain the decision to someone who was not in the room. It should not contain secrets or unnecessary personal data. A dated “blocked because scope is unclear” is better than an invented pass. For an SEO page, record why the question matters, what is original, what sources support the claims and when the page should be reviewed.
Keep the change reversible
A useful fix has an owner, a review date and a way back. Keep temporary settings separate from lasting policy. If the change touches an account, file, connector, network rule, model choice, cache or content page, record the previous state before changing it. Use a synthetic case or test identity where possible. A workflow that cannot be paused cleanly is not ready for wider rollout.
The common mistake
The common mistake with agent prompt injection not sql injection is to apply a consumer workaround to a business control problem. Refreshing the page, buying a higher plan, copying a prompt, duplicating a source or adding a broad allow rule may change the symptom while hiding the root cause. Diagnose the boundary, make the smallest safe change and retest the real outcome.
Related field note
Use the right control at the right boundary. A model is not a database driver with a personality. See the AI field notes index for adjacent operating patterns. Related notes are context, not proof that every plan, tenant, region or model exposes the same setting. Recheck the live product surface before making a change, and keep the reviewer, owner and stop condition visible.