Injection de modèle refers to a class of attacks in which an adversary injects malicious inputs into an AI model in order to manipulate its behavior or outputs. This technique exploits vulnerabilities in the model’s architecture or données d'entraînement, often leading to unintended consequences.
Dans le contexte de apprentissage automatique and systèmes d'IA, model injection can occur during the la formation de modèles phase or at inference time. During training, an attacker may introduce poisoned data into the training dataset, which can lead to the model learning incorrect patterns. At inference, an adversary might craft inputs that are designed to elicit specific responses from the model, effectively subverting its intended function.
Un exemple courant se trouve dans traitement du langage naturel (NLP) models, where an attacker might inject prompts that cause the model to generate harmful or biased outputs. Similarly, in image recognition systems, adversarial images can be crafted that are misclassified by the model, which could have real-world implications, such as in autonomous vehicles or security systems.
Mitigation strategies against model injection include robust validation processes, continuous monitoring, input sanitization, and using entraînement antagoniste techniques, which aim to make models more resilient against such attacks. Understanding and addressing model injection is crucial for maintaining the integrity and reliability of AI systems.