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.
AI coding assistants read untrusted files as instructions, not data. Here's how prompt injection sneaks malicious code into your commits — and how to catch it before it ships.
LLM coding assistants inherit insecure patterns from their training data — from SQLi-prone snippets to hallucinated packages attackers exploit. Here's how the risk propagates.
The Model Context Protocol is transforming how AI agents interact with tools, but it introduces new attack surfaces. Here is what security teams need to understand.
AI agent tools can hide invisible instructions attackers use to steal data. Here's how tool poisoning attacks work and how Safeguard stops them.
MCP lets AI models call tools automatically — and lets malicious servers hide instructions in plain sight. Here's how tool poisoning, rug pulls, and shadowing actually work.
Agent skill marketplaces are repeating npm and PyPI's supply chain mistakes—except the malicious payload is often a sentence of instructions, not code. Here's what's already been exploited.
SQL injection was solved by separating code from data. Prompt injection can't be, because in an LLM they share one channel. Here's the technical comparison, with real exploits and dates.
Coding agents run with real credentials and no pause button. Here is the threat model that treats them as autonomous infrastructure, not junior developers.
Jailbreak prompts now trade like exploits: sold as $200/month "dark" chatbots, bountied by vendors for up to $15,000. Here's how that market actually works.
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