Adversarial artificial intelligence (AI) poses a significant threat to the functioning of most AI and machine learning (ML) systems in use today. These deliberate attacks manipulate or deceive AI systems, creating vulnerabilities that can be exploited by malicious actors. The article identifies three main classes of adversarial AI attacks, including 1.) attacks on machine learning algorithms, 2.) generative AI systems, and 3.) machine learning operations (ML-Ops), including software supply chain attacks. As a matter of definition, ML-Ops are a set of practices that automate machine learning (ML) workflows and deployments.
The first type of attack focuses on exploiting vulnerabilities in algorithms, with the goal of modifying AI applications, and escaping notice by AI-based detection and response systems. Adversaries also seek to steal the underlying technology for their own use, through espionage or to weaponize AI models for financial and political gain. Generative AI system attacks are the second type of attack, targeting filters and guardrails designed to protect AI models, and allowing attackers to create prohibited content such as deepfakes or misinformation. These attacks are often used as a means to influence democratic elections on a global scale, with recent reports suggesting that many nation-states are actively working to weaponize large language models for their own agendas. ML-Ops and software supply chain attacks are the third type of attack, disrupting the frameworks and networks used to build and deploy AI systems, and introducing malicious code and information through compromised components and poisoned datasets.
The article concludes by outlining four strategies that organizations should adopt to defend against adversarial AI attacks. These include incorporating 1.) “red teaming” and risk assessment into the organization’s standard practices, 2.) staying informed about defensive frameworks for AI, 3.) integrating biometric interfaces and password-less authentication techniques into identity access management systems, and 4.) regularly auditing verification systems to ensure access privileges are current. Please note that “red teaming” is the practice of an organization employing an expert group which pretends to be an adversary, attempting physical or digital intrusions against the organization at the direction of that organization, and then reports back, so the organization can improve its defenses.
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