Prompt-injection testing
Test whether direct instructions can redirect an agent beyond its approved task
Users can submit open-ended requests to a tool-enabled assistant. The decision is how to test whether direct instructions can redirect an agent beyond its approved task. The work begins with your actual workflow and looks for the evidence that separates intended behaviour from a consequential exception. It is particularly relevant to security test owners and product engineers. A useful outcome is an explicit account of what was examined, what remains uncertain, and which decision the evidence can support.
The agent security relationship
- Instruction hierarchy
Establish the basis for direct request.
- Protected behaviour
State what must remain unchanged.
- Direct injection case set
Retain the scoped finding and its unresolved dependencies.
Decision
Frame the decision around direct request
Questions and the evidence they need
- Direct request
- Inspect instruction hierarchy for the relevant constraint or observation.
- Protected behaviour
- Inspect protected behaviours for the relevant constraint or observation.
- Executed effect
- Inspect synthetic prompts for the relevant constraint or observation.
The decision is how to test whether direct instructions can redirect an agent beyond its approved task. This is relevant to security test owners and product engineers when users can submit open-ended requests to a tool-enabled assistant. Name the affected workflow, the consequence of a wrong decision, and the owner who can act on the result. The initial discussion should establish which evidence is already available and which uncertainty is material enough to investigate.
The specific failure hypothesis is that an adversarial request persuades the agent to treat a forbidden action as a necessary exception. It remains a hypothesis until examined in the deployment. Existing internal tests or operating records may already resolve it; identify the uncovered question before commissioning additional work.
Mechanism
Injection success depends on the resulting action
Relationships within agent security
- Direct request
- Define the adversarial instruction under test.
- Protected behaviour
- State what must remain unchanged.
- Executed effect
- Inspect tool activity beyond the final refusal.
What this specialty examines
Direct injection testing distinguishes the requester’s legitimate task from an attempt to change governing constraints. Define the protected action and what the requester can submit. Evaluate actual downstream behaviour rather than judging only whether a refusal phrase appears.
The review should run authorised challenge requests and distinguish attempted redirection from executed side effects. Identify the normal route, relevant exceptions and the authoritative record of the outcome. Inspect the relevant business-system record, enforcement event or process evidence alongside any reported status. Keep incomplete cases visible, with an explanation of what their absence prevents the owner from concluding.
The tradeoff to make explicit
For this decision, stronger refusal behaviour matters while tool-side enforcement still needs separate evidence. Compare the available design or operating choices against the same intended outcome. If one option removes a required control or changes the task boundary, describe that difference before treating its apparent simplicity, speed or lower effort as an improvement. The decision should expose the tradeoff instead of burying it in an aggregate score.
Preparation
Connect the working inputs to the boundary being examined
Evidence packet for direct request
Illustrative working format
- Instruction hierarchy
- Identify provenance and the specific decision this material can inform.
- Protected behaviours
- Check its relationship to the relevant workflow and expose missing context.
- Synthetic prompts
- Keep expected behaviour and the limits of the constructed example explicit.
- Tool audit records
- Establish permitted access, provenance, and which task conditions it represents.
Start with instruction hierarchy, protected behaviours, synthetic prompts and tool audit records. Identify the owner, revision and operating context of each source. Sanitised examples may support initial design, but differences from actual conditions should remain explicit. Where direct access is unsuitable, a customer-controlled evidence or execution route may be considered, with agreed handling and retention.
The scope also needs the security decision owner, agreed expectations for a representative threat scenario, and the authority to inspect the relevant boundary. Specify what evidence would distinguish the failure in which an adversarial request persuades the agent to treat a forbidden action as a necessary exception from acceptable behaviour. If stakeholders disagree about intended behaviour, resolve the interpretation before classifying the observed difference as a defect.
How the work proceeds
Carry direct request through to an accountable finding
From the scoped material to a decision record
- Instruction hierarchy
Define the hypothetical attacker’s starting influence.
- Protected behaviour
Apply the scoped check to executed effect.
- Direct injection case set
Record the evidence, open dependencies, and the owner’s next decision.
Define the controlled threat scenario
Review instruction hierarchy, protected behaviours, synthetic prompts and tool audit records with security test owners and product engineers. State what the hypothetical attacker controls, the protected action and the systems included in the authorised exercise. Prepare synthetic records and harmless markers where appropriate, with stopping conditions for unexpected effects. The preparation output is a scenario brief that distinguishes an influence attempt from a prohibited action actually reaching a business resource.
Challenge the path and review effects
Record refused requests, attempted prohibited calls and actions blocked by tools separately. This reveals whether the model followed the boundary and whether the application enforced it. Do not turn one successful refusal into a broad protection claim.
Select a control response and retest
Build a direct injection case set and observed control findings from the reviewed observations. Separate agent behaviour, application enforcement and the resulting resource state so the recommended change reaches the responsible layer. Choose a bounded retest that reproduces the original condition and checks legitimate behaviour after remediation. If the required investigation exceeds the agreed technique or expertise, retain the unresolved path and identify the qualified review needed.
Indicative artefact
Make the direct injection case set usable by its owner
Direct injection case set
Illustrative working format
- Direct injection case set
- Identify the assessed workflow, revision, and decision boundary.
- Observed control findings
- Link observations and counterevidence to the case, configuration, and conclusion limit.
- Protected behaviour evidence
- Link observations and counterevidence to the case, configuration, and conclusion limit.
A useful decision artefact is a direct injection case set and observed control findings. Supporting material may include selected cases, configuration or process records, evidence references, a disagreement log and prioritised actions. Retain the case selection rationale and material disagreements alongside the result; otherwise the result may be mistaken for complete coverage.
Illustrative scenario
Examine executed effect through a constructed exception
A constructed case and its decision paths
Illustrative scenario
- Direct request
A challenge prompt receives a refusal while a background tool call still proceeds.
- Protected behaviour
Inspect background tool events after the visible refusal.
- Executed effect
Record the executed effect separately from the response wording.
Consider a fictional situation: an illustrative user tells a support assistant to ignore its task boundary and export account records for debugging. The scenario is illustrative, with no client engagement, executed test or measured improvement implied.
For the debugging export request, use synthetic records and an explicitly prohibited destination. If the model attempts export but the tool denies it, preserve both observations. The owner may need to improve interaction behaviour while retaining the effective enforcement control.
Scope and next action
Keep the conclusion within the agreed threat scenario
Confirm fit before the next commitment
- Direct request
- This review is limited to the agreed threat scenario.
- Instruction hierarchy
- The relevant owner must confirm access and the intended operating conditions.
- Direct injection case set
- A bounded challenge cannot establish exhaustive protection.
Authorised scenario testing is not a claim of exhaustive penetration testing or guaranteed protection. Intrusive security work requires qualified specialists and a separately agreed scope. Any engagement depends on an agreed scope, qualified team, authorised access and confirmed availability. Bring the affected workflow, its current configuration or process, the triggering change and the evidence already available. The immediate scope question is prompted by the situation in which users can submit open-ended requests to a tool-enabled assistant; confirm which missing observation would change the owner’s next action.
Ways to engage
Related engagement scopes
Agent Release Readiness Review
The Agent Release Readiness Review is a fixed-scope, three-week independent test of one AI agent workflow that shows a release board what the agent does, what it is allowed to do and what happens when it is wrong, and ends in a signed recommendation.
AI Agent Runtime Security Review
Make every delegation explicit. Examine who the agent acts for, where it can execute and which tools it can reach when instructions alone are not enough.
AI Agent Red Team
Challenge the boundaries that matter to your workflow. Test agreed attack scenarios against agent instructions, tools and data access.
Continue exploring
Related insights and resources
The permission boundary behind every agent action
An agent can only be evaluated in the context of what its tools allow. Examine identity, delegated authority and action boundaries before you put a workflow in motion.
Before your agent can act, define its authority
A working paper for the moment an AI system moves from advice to action. Map identity, permissions, approvals and recovery into an authority model your team can review and test.
Running an AI-agent incident tabletop exercise
A useful tabletop follows one plausible agent failure through detection, containment, evidence, business recovery and accountable return to service.