The OWASP GenAI Security Project highlights scenarios where AI support agents step beyond their intended scope when processing untrusted instructions.
Turning off a model does not automatically halt active orchestrators, queued jobs, or retained tokens, requiring comprehensive containment testing.
Why pausing the model is not enough
An AI system rarely acts alone. It typically communicates with other services, executes scheduled tasks in a queue, or has access rights to files and network connections. This means that if an unexpected problem arises, a simple stop command to the model itself is not enough to stop the workflow. A different connection or an active digital key can continue to send or receive data, while a command telling the AI to stop is not in itself a control measure.
When is attention needed?
Unusual behavior is not always due to a malicious attack, as it can be caused by a configuration error or a software problem. However, administrators should carefully examine situations where the system is using tools outside its intended purpose, performing unusually high reads or writes, or continuing to run despite being told to stop.
Steps to effective quitting
When a response is required, the process includes specific steps to prevent further problems:
- Pause the active workflow and suspend any pending requests.
- Revocation or restriction of digital keys and access rights used by the system.
- Restriction of tools and network paths to prevent contact with unknown destinations.
- Collecting available information and recording what exactly happened.
- Addressing any impacts caused.
Tools like KeeperPAM combine secret management, access controls, and logging, helping organizations oversee the digital keys that move behind AI operations.
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