The National Institute of Standards and Technology (NIST) has published a report concerning the challenges in mitigating attacks on artificial intelligence (AI) and machine learning (ML) systems. The NIST report, developed in association with the UK AI Security Institute (AISI), is intended to assist those responsible for designing, developing, deploying, evaluating, and governing AI systems in devising improved mitigations for adversarial ML (AML).
The report highlights that the data-driven nature of ML systems introduces new attack vectors that traditional software systems do not face, posing threats to security, privacy, and safety. These attacks target various stages of ML operations, including manipulating training data, providing adversarial inputs to disrupt system performance, and maliciously modifying models to extract sensitive training data.
NIST stressed that these attacks have been demonstrated in real-world scenarios, with their complexity and impact steadily rising. As AI systems become more prevalent, their security becomes increasingly critical.
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