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 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.
Real incidents show malicious Hugging Face models evading scanners with pickle exploits and reverse shells. Here's what teams need to know before the next pull.
Slopsquatting attacks turn AI-hallucinated package names into real supply chain threats. Here's how it works, the numbers behind it, and how Safeguard stops it.
AI coding assistants now write huge shares of production code. Real 2025 incidents show hallucinated packages, leaked secrets, and vulnerable defaults ship with it.
How hardware-based secure enclaves keep LLM prompts and weights encrypted even during active inference, and why confidential AI inference is reshaping AI compliance in 2026.
A practical guide to AI red teaming: how to plan, run, and report a structured LLM red team exercise using a repeatable adversarial testing methodology.
A practical comparison of leading LLM red teaming tools -- PyRIT, Garak, Giskard, Promptfoo, Lakera Red, and Mindgard -- with real strengths, limits, and evaluation criteria.
A concrete look at how AI safety benchmark evaluation, LLM safety scorecards, and capability testing actually measure model risk in 2026 — and where they fall short.
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