Artificial intelligence systems are susceptible to manipulation through deliberate attacks, which can cause significant operational failures.
AI’s Critical Vulnerabilities Unveiled
The complexity of AI training datasets makes them difficult to thoroughly examine for potential threats or anomalies. Teams from the National Institute of Standards and Technology (NIST), along with partners, have pinpointed various weaknesses that can be exploited in AI systems.
Their research categorizes these threats, detailing the nature of potential attacks and the strategies to neutralize them, thus providing a guide for developers.
Dissecting the Attack Spectrum
The study identifies four main types of attacks: evasion, poisoning, privacy, and abuse, each described by the attacker’s knowledge, capabilities, and motives.
Evasion attacks involve altering inputs to deceive an AI post-deployment. Poisoning attacks manipulate the AI during its training phase by incorporating flawed data. Privacy attacks aim to extract confidential information about the AI or its training data, while abuse attacks corrupt sources the AI relies on to skew its functionality.
Strategizing Defense Mechanisms
Addressing these vulnerabilities, NIST emphasizes the importance of awareness for developers and companies who implement AI technologies. The research acknowledges that securing AI algorithms against such attacks remains an unsolved challenge, warning against claims of foolproof solutions.
