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Add architectural guardrails around how agents read untrusted sources and use them in the workflow. The post How Can AI Agents Read Untrusted Sources Safely? appeared first on Towards Data Science.
The Lethal Trifecta That Makes AI Agents Leak Data! The lethal trifecta is Simon Willison's name for an AI agent that has three things at once: access to private data, exposure to untrusted content, and a way to send data out.
Microsoft extends the Outlook naughty step with two more file types 29 minutes ago
Trust gaps in the new protocol spread malicious prompts from one agent to another.
A personal AI agent is useful because it can read your private data, read content written by other people, and act on your behalf. A prompt injection attack depends on those same three abilities.
Trump administration gets Big Tech to sign weak, non-binding, AI regulations 1 hour ago
Agent design patterns every data engineer must know The post How to Design Architectural Guardrails Around AI Agents appeared first on Towards Data Science.
Use this AI chatbot prompt-injection checklist to scope support sources, separate untrusted input, test refusals, and protect human handoff before launch.
Your AI agent doesn't need to be hacked to fail — sometimes a single sentence is enough. In this video, I break down the first of three guardrail layers every production AI agent needs, and show you exactly how to detect and block the most common input-side failures using Python.
Why context delivery is a trust decision: compare injected application state with MCP tools, and separate trusted instructions from untrusted data.
How we built a two-stage prompt injection classifier that achieves 0.99 F1 with sub-10ms latency.
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