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.
A concrete, incident-driven walkthrough of AI supply chain security — from poisoned datasets and backdoored Hugging Face models to CI pipeline hijacks — and how to reduce the risk end to end.
Poisoned datasets and untraceable training data are already causing lawsuits and breaches. Here's why training data provenance is now a security requirement.
A practical guide to model weight integrity: baseline checksums, sign weights, verify in CI/CD, and detect tampering before it reaches production.
Fine-tuning pipeline security is now an AI supply chain priority: as few as 250 poisoned documents can backdoor a model, and LoRA adapters make it easy to hide.
A single expired domain and $60 can poison a training set. Here's how data poisoning attacks corrupt LLM behavior — and how Safeguard verifies training data before it ships.
OpenAI shipped the GPT-5.2 update to the GPT-5 system card on December 11, 2025. We dig into the preparedness scoring, the cybersecurity capability claims, and what changed for downstream defenders.
The confused deputy problem takes on new and subtle forms when AI agents invoke tools on behalf of users. A technical deep dive with concrete mitigations.
A poisoned model looks like any other checkpoint file. Here's how model poisoning attacks work, real incidents on Hugging Face, and how detection and provenance checks catch them.
An AI bill of materials (AIBOM) inventories the models, data, and dependencies behind an AI system. Here's what it is and why enterprises need one.
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