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.
Domain adaptation has quietly become the default for LLM-assisted vulnerability detection. A look at what works in 2026, what does not, and what teams should plan for next.
GPT-4o is an excellent general-purpose model. Security workflows are a specialty, and specialty work exposes the limits of general intelligence.
A remediation PR is only useful if it does not break anything else. Griffin AI runs targeted regression before opening; Mythos-class tools usually do not.
AI code assistants are writing a growing share of production code. The security implications are significant and largely unaddressed.
Prompt injection stopped being an LLM curiosity the moment agents started committing code. It is now a software supply chain risk and should be modeled as one.
Go checksum database is one of the most underappreciated supply chain security features in any language ecosystem. Here is how it works and where it falls short.
Tag-pinning Actions feels fine until a maintainer gets compromised. Here is why SHA-pinning is the only serious option in 2026 and how to operationalize it.
NestJS's dependency injection container silently governs data isolation and supply-chain trust. Here's how scope, module, and factory misconfigurations turn into real security failures.
Eagle 3.0 is the classification model behind Safeguard's package, image, and secret detection. Here is what changed, what moved, and what it means for alerts.
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