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Red teaming, capability evaluations, adversarial testing, and performance verification.
Also in Risk & Assurance
A model can be certified to withstand adversarial attacks given specific datapoint constraints, model constraints, and attack vectors [156, 124]. Certification means that it can be both analytically proven and shown empirically that the model will withstand such attacks up to a certain threshold. Currently, robustness certification methods are limited to certifying against attacks via manipulation of pixels on specific ℓ p norms, canonically the ℓ 2 (Euclidean) norm, up to a certain neighborhood radius.
Reasoning
Robustness certificates document formal evidence of model safety properties and deployment readiness assurance.
Fine-tuning-related
Model development
2.4 Engineering & DevelopmentModel development > Data-related
1.1 ModelModel evaluations
2.2.2 Testing & EvaluationModel evaluations > General evaluations
2.2.2 Testing & EvaluationModel evaluations > Benchmarking
3.2.1 Benchmarks & EvaluationModel evaluations > Red teaming
2.2.2 Testing & EvaluationRisk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Gipiškis, Rokas; San Joaquin, Ayrton; Chin, Ze Shen; Regenfuß, Adrian; Gil, Ariel; Holtman, Koen (2024)
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 distinct AI risk mitigations.
Verify and Validate
Testing, evaluating, auditing, and red-teaming the AI system
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
