AI Security
Vulnerability Scanning for AI Models: A New Frontier
AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.
Jan 12, 20266 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.
AI and ML pipelines introduce unique supply chain risks -- from poisoned training data to compromised model registries. Here is what attackers are targeting and how to defend.
As organizations adopt AI at scale, the AI/ML supply chain is becoming a new attack surface. From poisoned models to compromised training data, the threats are real and growing.
As open source AI models proliferate, their security implications extend far beyond traditional software vulnerabilities. Model poisoning, supply chain tampering, and unsafe deserialization create new attack surfaces.
As organizations download pre-trained models from Hugging Face and other model hubs, the AI supply chain introduces risks that traditional software security tools don't address.
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