NIST, the US industry standards organization, has released a document that compiles the risks of machine learning attacks and provides guidelines to mitigate them. However, they caution that there is currently no foolproof way to prevent all attacks, and developers should remain vigilant.
The NIST AI 100-2e2023 document is not an industry standard but rather a compilation of different attack scenarios and risk reduction strategies. It categorizes attack patterns into four major types:
1. Evasion attack: Attackers attempt to manipulate the behavior of machine learning systems by inputting data that alters their responses. For example, individuals may try to place fake traffic signs to deceive driverless cars.
2. Poisoning attack: Malicious actors inject harmful data into machine learning training datasets. This could involve inserting offensive language to prompt AI systems to generate inappropriate responses.
3. Privacy attack: Adversaries try to deceive AI systems into exposing personal or confidential information about themselves or the training datasets.
4. Abuse attack: False data is inserted to mislead AI systems into producing incorrect outputs. For instance, creating misleading web content to manipulate AI into providing inaccurate responses.
Although the document encompasses previous research on these subcategories of attacks and defense techniques, it still falls short of presenting a complete solution. Therefore, finding a clear resolution to address these challenges remains an ongoing issue.
TLDR: NIST has released a document outlining the risks of machine learning attacks and ways to mitigate them. However, developers should be aware that a comprehensive solution to prevent all attacks does not currently exist. The document covers evasion, poisoning, privacy, and abuse attacks and suggests defense strategies. Nonetheless, the quest for a definitive solution continues.
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