Autonomous Security of AI Systems

Securing artificial intelligence (AI) systems presents numerous research challenges that are critical to address to ensure the safe and reliable deployment of these technologies. In many cases, academic research assumes threat models that provide attackers with more access and capabilities than what is typically available in practice. For instance, attacks on AI systems often presume complete access to training data or model parameters, which is not always reflective of real-world scenarios where such access is limited.

Moreover, AI systems are vulnerable to various types of attacks, such as data poisoning, evasion, model stealing, and membership inference. Each of these attacks exploits different aspects of AI systems, from the integrity of training data to the confidentiality of model outputs. Addressing these vulnerabilities requires a multi-faceted approach, including the development of robust defenses, improved detection mechanisms, and comprehensive threat assessments.

The importance of securing AI systems cannot be overstated, as these technologies are increasingly integrated into critical infrastructure and decision-making processes. Ensuring their security is vital for maintaining trust and preventing potentially severe consequences arising from compromised AI systems. One of major research goals of CSL is to build systems that can autonomously protect and defend AI systems.