Model Poisoning: The Hidden Risk in Supply Chain AI
Model poisoning poses a significant risk within AI supply chains, as it embeds malicious behavior directly into model weights. This type of attack can bypass traditional security measures, making it difficult to detect. With the rise of open-source models and third-party services, the potential for such vulnerabilities has increased dramatically.
- ▪Model poisoning can occur during training or fine-tuning, embedding hidden behaviors that activate under specific conditions.
- ▪Attackers can manipulate training datasets or release pre-trained models with hidden triggers that produce malicious outputs.
- ▪Traditional security controls fail against model poisoning because they operate at the wrong layer, focusing on code vulnerabilities rather than learned model weights.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3944825) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Falcons Edge Posted on May 26 • Originally published at aisecurities.uk Model Poisoning: The Hidden Risk in Supply Chain AI #security #ai #llm #supplychain Most AI security discussions focus on the perimeter — protecting API endpoints, filtering inputs, and monitoring outputs. But what if the threat isn't at the perimeter at all? What if it's already inside the model before you even deploy it? Model poisoning is the supply chain attack of the AI era.
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