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.
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.
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.
The Model Context Protocol connects AI agents to your tools and data. That power cuts both ways. Here are eight concrete practices for running MCP servers without handing attackers a remote control.
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.
The label 'AI security software' now covers two different markets — tools that secure AI systems, and security tools powered by AI. How to tell them apart, what to evaluate, and the questions that expose thin products.
Copilot Autofix pairs CodeQL with an LLM to patch code-scanning alerts up to 3x faster. Here's how it works, its limits, and where supply chain risk still slips through.
The moment you give an LLM tools, it stops being a chatbot and becomes an actor in your systems. Tool-use security is about making sure a compromised agent hits a wall instead of a credential.
Retrieval-augmented generation wired an untrusted-content pipeline straight into your model's context window. Here are the practices that keep a poisoned document or a leaked chunk from becoming an incident.
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