model poisoning

Model poisoning is a critical security threat targeting artificial intelligence systems, particularly large language models (LLMs) used in business and development. This attack involves subtly manipulating training data or model parameters to corrupt an AI's behavior, often without immediate detection. The consequences can range from biased outputs to malicious actions that compromise user trust and system integrity. Discussions on WindowsForum highlight real-world incidents, such as those presented at the RSA Conference, where experts like Microsoft's 'Data Cowboy' have demonstrated how model poisoning can be executed. To mitigate these risks, organizations are advised to adopt robust security strategies, including data validation, continuous monitoring, and adherence to frameworks like the OWASP Top 10 for LLM security. Understanding model poisoning is essential for anyone deploying AI in enterprise environments.