machine-learning
Safeguard articles tagged "machine-learning" — guides, analysis, and best practices for software supply chain and application security.
15 articles
PyTorch Security Guide (2026)
PyTorch is the dominant deep-learning framework for research and production — and its torch.load remote-code-execution history makes loading a model checkpoint one of the most security-sensitive operations in modern ML.
TensorFlow Security Guide (2026)
TensorFlow is one of the most widely deployed machine-learning frameworks — and its history of model-deserialization RCE and crafted-tensor memory bugs makes its version and loading habits genuinely security-relevant.
Inside DeepCode AI: how Snyk Code's ML models are trained...
How Snyk's DeepCode AI turns millions of open-source commit fixes into the symbolic-AI and ML models powering Snyk Code's vulnerability detection and autofixes.
AI Security Apps: What They Do and How to Evaluate Them
An AI security app uses machine learning to detect, prioritize, or remediate security issues. Here is what the category actually delivers and how to judge one.
Artificial Intelligence Security Tools: What They Do and How to Choose
Artificial intelligence security tools now sit in two camps: tools that use AI to defend software, and tools that defend the AI itself. Knowing which one you need shapes the whole buying decision.
AI SBOMs and Model Cards: Building Transparency Into the AI Supply Chain
As AI models become critical software components, the need for AI-specific SBOMs and model cards grows urgent. How the industry is extending supply chain transparency to machine learning pipelines.
AI Endpoint Security: How Machine Learning Changes Endpoint Defense
AI endpoint security uses machine learning to detect threats by behavior rather than signatures. Here is what it actually does, where it helps, and its limits.
AI for Cyber Security: What Works and What Is Hype
AI for cyber security is real where it triages signal at machine scale, and overstated where vendors promise autonomous defense. Here is how to tell the two apart.
AI Cybersecurity Software: What It Actually Does and Where It Falls Short
AI cybersecurity software uses machine learning to spot anomalies, triage alerts, and prioritize risk faster than humans can. Here is what it genuinely helps with and where it still needs a human.
Securing ML Model Serving Infrastructure
Model serving infrastructure is a growing attack surface that most security teams overlook. From model poisoning to inference API abuse, here are the risks and how to address them.
Open Source AI Model Security: The Emerging Threat Landscape
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.
AI Model Poisoning: Detection Techniques for the Software Supply Chain
Poisoned AI models are a supply chain threat that traditional security tools can't detect. Here are the emerging techniques for identifying compromised models.
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