NIST Identifies Main Types of Adversarial Machine Learning Threats
A new National Institute of Standards and Technology (NIST) publication identifies general types of cyberattacks – so-called “adversarial machine learning” threats – that can be used to attack or manipulate the behavior of AI/ML systems.
The four main types, according to the news statement, are:
- Evasion attacks, which attempt to alter an input after the AI is deployed.
- Poisoning attacks, which occur in the training phase through the introduction of corrupted data.
- Privacy attacks, which attempt to gain and misuse sensitive information about the AI or the data on which it was trained.
- Abuse attacks, which involve malicious insertion of incorrect information into a source.
The publication “is intended to help AI developers and users get a handle on the types of attacks they might expect along with approaches to mitigate them – with the understanding that there is no silver bullet.”
01/26/2024
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