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.
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.
Retrieval-augmented generation is the most common LLM deployment pattern in the enterprise and the most commonly poisoned. A senior security engineer's playbook for defences that hold up in production.
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.
Prompt injection is the top risk on the OWASP list for LLM applications for a reason: there is no single patch. Preventing it means layering controls around a model that cannot reliably tell instructions from data.
Embedding models are the silent dependency under every RAG system. We cover poisoning, deprecation, and provenance gaps that break retrieval in production.
You would never run an unknown binary from a stranger, but teams pull unknown model weights off public hubs every day. Loading them can be code execution — and that is only the most obvious link in the chain.
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