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 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.
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.
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.
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.
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.
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.
When an AI agent can read your repo, run commands, open pull requests, and call external tools on its own, the security model shifts from reviewing code to governing an actor. Here is what agentic development security means and why it is different.
Vector databases became critical infrastructure the moment RAG went mainstream, but most are deployed with the security posture of a cache. Embeddings leak, indexes get poisoned, and tenants bleed into each other.
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