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.
Tool-poisoning attacks against Model Context Protocol servers hide adversarial instructions inside tool descriptions and silently mutate tool definitions after install. Here is how the attack works and how to defend against it.
AI coding assistants like Copilot and Cursor can be hijacked by hidden text in files, comments, and packages. Here's how prompt injection malware works and how Safeguard detects it.
Prompt engineering is how you steer an LLM, and it is also where a lot of application security now lives. Here is how to write prompts that resist injection and leakage.
As AI models become critical software components, the need for AI-specific SBOMs and model cards grows urgent. How the industry is extending supply chain transparency to machine learning pipelines.
MCP servers give AI agents direct tool access, but most ship unvetted. Here's how security scanning catches tool poisoning and rug-pull attacks.
On May 5, 2026, NIST's CAISI signed pre-deployment evaluation agreements with Google DeepMind, Microsoft, and xAI, bringing five frontier labs into a government testing program covering cyber, bio, and chemical risk.
AI security providers fall into a few distinct categories, and picking the right one starts with knowing which risk you are actually trying to cover. This guide breaks down the landscape.
AI information security is the practice of protecting the data that flows through AI systems, training sets, prompts, outputs, and the models themselves, from disclosure, poisoning, and misuse. Here is a working model of the risks and controls.
Tool-using agents have become a viable exfiltration channel. The patterns showing up in incident reports, and the controls that contain them.
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