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The National Institute of Standards and Technology (NIST) has issued an urgent report to help defend against increasing threats to artificial intelligence (AI) systems.
The report, titled “Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations,” comes at a crucial time when AI systems are both highly advanced and highly vulnerable.
According to the report, adversarial machine learning (ML) is a method attackers are using to trick AI systems through small manipulations, potentially causing significant harm.
The document provides a detailed overview of these attacks, categorizing them based on the attackers’ goals, capabilities, and their knowledge of the targeted AI system.
Attackers can confuse or even “poison” AI systems to make them malfunction. These types of attacks exploit weaknesses in how AI systems are designed and deployed.
One specific attack mentioned is “data poisoning,” where adversaries tamper with the data used to train AI models. The report notes that such poisoning could be scaled to allow even those with limited resources to manipulate public datasets used for training.
Another major concern is “backdoor attacks,” where triggers are embedded in training data to cause future misclassifications. These attacks are particularly difficult to defend against.
The report also points out privacy risks, such as “membership inference attacks,” which can determine if a particular data sample was part of the training dataset. Currently, there is no foolproof way to protect AI from these kinds of manipulations.
While AI has the potential to revolutionize industries, security experts urge caution. The NIST report highlights that AI chatbots, driven by recent deep learning advancements, are powerful tools with significant business potential but should be deployed carefully.
The primary aim of the NIST report is to establish a common language and understanding regarding AI security issues. This document is expected to be an essential resource for the AI security community in addressing these emerging threats.
We seem to be in an ongoing battle against AI security threats. Experts acknowledge that more robust protections are needed for AI systems before they can be safely integrated across various industries. The risks are too significant to overlook.