Definition ∞ A machine learning attack refers to malicious actions directed against machine learning models, aiming to compromise their integrity, confidentiality, or availability. These attacks can involve extracting sensitive training data, manipulating model outputs through adversarial inputs, or corrupting training data to degrade performance. In the digital asset domain, such attacks could target models used for fraud detection, market prediction, or blockchain security analytics. Countermeasures are essential to maintain the reliability of AI-driven systems.
Context ∞ The increasing integration of machine learning into digital asset platforms, particularly for security and market analysis, elevates the concern regarding machine learning attacks. Researchers are actively developing robust defenses to protect these models from sophisticated adversarial techniques. The security of oracles and decentralized autonomous organizations that rely on ML outputs is a growing area of vulnerability. Mitigating these advanced threats is crucial for maintaining the trustworthiness of AI-powered crypto services.
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