Configuration review alone misses the ways an agent can be induced to misuse legitimate access. A bank may confirm permissions on paper and still fail to see prompt injection, goal manipulation, or tool abuse in practice. That leaves the organisation exposed to unauthorized actions, fabricated transactions, and data movement that looks valid until it is too late.
Why Configuration Reviews Miss the Real Risk
Banks often treat AI agents like ordinary applications and stop at reviewing approved settings, roles, and tool permissions. That misses the core failure mode: an agent can be persuaded at runtime to use valid access in unsafe ways. A clean configuration can still produce unauthorized transfers, data leakage, or tool chaining if the agent is manipulated by prompt injection, goal hijacking, or malicious content in downstream systems.
This is why static review is not enough. Security teams need to test what the agent actually does when faced with adversarial prompts, ambiguous instructions, and hidden data dependencies. The issue is not only whether access exists, but whether the agent can be induced to exercise that access incorrectly. NHIMG’s research on OWASP NHI Top 10 highlights that agentic systems expand the attack surface beyond identity configuration into behavior under pressure.
In practice, many security teams discover the weakness only after an agent has already moved data or executed a workflow the approval record never anticipated.
How Behavioral Testing Exposes What IAM Cannot See
Behavioral testing simulates hostile or ambiguous conditions and observes whether the agent resists unsafe actions, not just whether it is allowed to take them. That means exercising prompt injection, indirect prompt injection, tool misuse, and boundary-pushing requests in a controlled environment. The most useful tests focus on task completion under stress: can the agent be redirected, does it chain tools beyond intent, and does it request or expose secrets when the conversation shifts?
Current guidance from the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 supports moving beyond paper controls to runtime evaluation. For banks, that usually means pairing configuration review with:
- Adversarial prompts that try to override policy or disguise malicious intent.
- Tool-abuse tests that force the agent to choose between legitimate and unsafe actions.
- Data-handling checks that verify the agent does not exfiltrate customer or internal records.
- Escalation scenarios that see whether the agent can be tricked into expanding its own privilege.
NHIMG’s Gemini AI Breach coverage is a useful reminder that prompt injection is not a theoretical concern when the model can ingest untrusted content and act on it. The practical control is to test the agent in the same execution paths it will use in production, with logging, approval gates, and revocation paths already in place. These controls tend to break down in highly integrated banking environments because production agents often sit inside long workflow chains where one unsafe tool call can propagate across multiple trusted systems.
What Banks Need to Change in Governance and Testing
Tighter review usually increases operational overhead, so banks need to balance faster deployment against stronger behavioral assurance. The answer is not to abandon configuration review, but to treat it as only the first layer. Best practice is evolving toward continuous adversarial testing, red-team style scenarios, and post-deployment monitoring that watches for abnormal tool calls, unusual data access, and policy bypass attempts.
There is no universal standard for this yet, but the direction is clear: banks should test for what the agent can be induced to do, not only what it is permitted to do on paper. The CSA MAESTRO agentic AI threat modeling framework and MITRE ATLAS adversarial AI threat matrix both reinforce the need to evaluate agent behavior across the full attack lifecycle, including manipulation and abuse of legitimate access.
The operational reality is that configuration-only assurance creates false confidence. If an agent can be coaxed into sending funds, disclosing customer data, or invoking downstream tooling in ways the configuration never intended, the bank has a behavior problem, not just an identity problem. NHIMG’s LLMjacking analysis shows how quickly exposed credentials can be abused once an agentic environment is compromised, and that is exactly why security teams should test for misuse before attackers do.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A3 | Addresses prompt injection and agent misuse beyond static configuration. |
| CSA MAESTRO | TM-2 | Covers agent threat modeling and runtime abuse scenarios. |
| NIST AI RMF | Focuses governance on measurable AI risk, not just documented settings. | |
| OWASP Non-Human Identity Top 10 | NHI-04 | Relevant because agent tooling depends on non-human identities and secret handling. |
| NIST CSF 2.0 | DE.CM-8 | Supports monitoring of abnormal behavior and tool misuse in production. |
Test agents with adversarial prompts and unsafe tool paths before approving production use.
