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.
Snyk analyzed nearly 10,000 real developer environments and found 43% run 2+ AI coding tools at once — with MCP servers and skills quietly widening the attack surface.
Your AI supply chain is not just your npm dependencies anymore. It is the models you download, the weights you load, and the datasets you train on — and each is an attack surface most software security programs have never inventoried.
Safeguard's MCP server exposes 650+ tools. Here's how per-tool feature flags keep each tenant scoped to exactly what it needs — with safe defaults and a fail-safe that narrows, never widens, on error.
AI coding assistants are in nearly every IDE now. Banning them fails; trusting them blindly fails harder. The middle path is guardrails — technical controls that let assistants move fast without letting them ship the wrong thing.
You are shipping an LLM feature. Before it goes live, walk this checklist — organized around the OWASP Top 10 for LLM Applications — to catch the risks that matter most in production.
Hugging Face is the npm of machine learning, and it inherits npm's problems. Malicious weights, pickle payloads, and leaked Space secrets are all live risks — here is how to pull models safely.
AI can review pull requests and AI can write them — sometimes in the same workflow. Both roles carry security implications teams routinely underestimate. Here is how to get the benefit without the blind spots.
You do not need to corrupt most of a training set to backdoor a model — recent research suggests a small, near-constant number of poisoned documents can be enough. Defense starts with treating data as a dependency.
A jailbreak is not the same thing as a prompt injection, and conflating them leads to defenses that miss. Here is how modern jailbreaks actually work and the layered controls that hold the line.
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