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Data poisoning is a cybersecurity threat that targets the integrity of machine learning (ML) and artificial intelligence (AI) systems by deliberately manipulating the data used to train these models. This manipulation can lead to incorrect or biased outcomes from AI systems, making data poisoning a significant concern for the reliability and security of AI applications. The concept of data poisoning is not new, but its implications are becoming increasingly critical as AI and ML technologies become more embedded in various aspects of society, including security systems, financial services, healthcare, and autonomous vehicles. Data poisoning attacks can be categorized based on the attacker's knowledge and the tactics employed. Attacks can range from black-box attacks, where the attacker has no knowledge of the model's internals, to white-box attacks, where the attacker has full knowledge of the model and its training parameters. The tactics for data poisoning include availability attacks, targeted attacks, subpopulation attacks, and backdoor attacks, each with its own method of corrupting the AI model to achieve different malicious objectives2. The methods for carrying out data poisoning attacks can be surprisingly cheap and accessible. For instance, researchers have demonstrated that for as little as $60, a malicious actor could tamper with the datasets that generative AI tools rely on. This could involve purchasing expired domains and populating them with manipulated data, which AI models might then scrape and incorporate into their training datasets. Such attacks could control and poison at least 0.01% of a dataset, which, although it seems small, can be significant enough to cause noticeable distortions in the AI's outputs1. Preventing data poisoning attacks is crucial, especially as more organizations and government agencies rely on AI to deliver essential services. Proactive measures include being diligent about the databases used for training AI models, employing high-speed verifiers, and using statistical methods to detect anomalies in the data. Continuous monitoring of model performance is also essential to detect unexpected shifts in accuracy that could indicate a data poisoning attack2. The rise of data poisoning as a threat to AI systems underscores the need for robust security measures and ethical considerations in the development and deployment of AI technologies. As AI becomes more integrated into critical systems, the potential for harm from data poisoning attacks grows, making it imperative for researchers, developers, and policymakers to address this challenge proactively367.
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