Definition ∞ A model poisoning attack is a malicious cybersecurity tactic where an adversary contaminates the training data of a machine learning model, causing it to learn incorrect patterns or biases. This manipulation can degrade the model’s performance, introduce vulnerabilities, or force it to make erroneous predictions during deployment. The attack aims to subvert the integrity of the AI system by subtly altering its foundational knowledge. Such attacks are a significant threat to the reliability of AI applications.
Context ∞ Model poisoning attacks represent a serious and evolving threat to the trustworthiness and security of artificial intelligence systems across various sectors, including finance and autonomous vehicles. Current discussions focus on developing robust defense mechanisms, such as data sanitization techniques and anomaly detection algorithms, to identify and mitigate poisoned data. Future research is directed towards creating more resilient machine learning models that can resist such adversarial manipulations and maintain their integrity even when exposed to compromised data.
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