OWASP FOR ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML)

The rapid adoption of artificial intelligence (AI) and machine learning (ML) technologies across various sectors has brought both unprecedented opportunities and significant security challenges. As these systems increasingly influence critical decisions in healthcare, finance, and autonomous systems, understanding their vulnerabilities becomes essential for safeguarding data, model integrity, and user privacy. The OWASP Machine Learning Security Top 10 project aims to address this need by identifying and categorizing the top security risks unique to machine learning and large language model (LLM) applications. Through extensive collaboration with industry experts, the project delivers a meticulously vetted list of common and critical vulnerabilities in AI systems, along with actionable strategies for mitigating these risks. The vulnerabilities documented within the OWASP framework represent a diverse array of potential threats, including input manipulation, model inversion, and data poisoning. Each risk type targets different stages of the ML lifecycle from training data acquisition to model deployment making these threats particularly complex and challenging to mitigate. Moreover, with the increasing deployment of large language models, new risks specific to these architectures have emerged, requiring separate attention and security strategies.