The Dark Art of Machine Learning: Vulnerabilities, Attacks, and Defense

Machine learning has revolutionized industries, driving innovations in automation, cybersecurity, and decision-making. However, as these models grow in complexity and influence, they also become prime targets for adversaries seeking to exploit their vulnerabilities. The intersection of machine learning and cybersecurity has given rise to a new frontier of threats—ranging from data poisoning and adversarial attacks to model inversion techniques.

This whitepaper explores the dark art of machine learning vulnerabilities, shedding light on the various attack vectors, their implications, and the defense mechanisms that can safeguard these systems.