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AI Risk

Robustness and Reliability

The robustness of an AI-based model refers to the stability of the model performance after abnormal changes in the input data... The cause of this change may be a malicious attacker, environmental noise, or a crash of other components of an AI-based system... This problem may be challenging in HLI-based agents because weak robustness may have appeared in unreliable machine learning models, and hence an HLI with th...

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AI Risk7. AI System Safety, Failures, & Limitations7.3 > Lack of capability or robustness2 - Post-deployment

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.

Risk profile

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The robustness of an AI-based model refers to the stability of the model performance after abnormal changes in the input data... The cause of this change may be a malicious attacker, environmental noise, or a crash of other components of an AI-based system... This problem may be challenging in HLI-based agents because weak robustness may have appeared in unreliable machine learning models, and hence an HLI with this drawback is error-prone in practice.

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.3 > Lack of capability or robustness
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryRobustness and Reliability
Subcategoryn/a

Related techniques

Attack methods connected to this risk.

No linked attack methods. No AI attack method is connected to this risk in the current data.

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

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