🛡️ Code-executing AI agents are powerful, but that very freedom is their biggest attack surface. This is a must-read guide that systematically lays out how to pick the right sandbox. Title: How to Choose the Right Sandbox for Your Agent URL: 💡 Overview Letting agents run code unlocks huge value, but it comes with prompt injection, a threat that has no guaranteed defense today. This post frames that risk as the "lethal trifecta" and gives you a practical checklist for choosing a sandbox. ⚠️ The problem You are in danger when all three are true at once: the agent (1) can access sensitive data, (2) is exposed to untrusted content, and (3) can communicate externally. When they line up, an attacker can steal your data. Meta's "Rule of Two" says a fully autonomous agent should never satisfy all three at the same time. 🛠 Approach A sandbox does not erase the trifecta. It shrinks data access and external communication until the prompt injection risk becomes small enough to manage. The five must-have features are: ・An isolated filesystem (only the data the agent needs) ・Limited network access (block exfiltration) ・Resource limits (CPU, memory, runtime) ・Controlled reusability (so a compromise doesn't persist) ・Kernel-level isolation (so kernel bugs can't be exploited) 🎯 Use cases / implementation MicroVMs give you kernel-isolated sandboxes without paying for a full VM each time. The post highlights LangSmith Sandboxes: a dedicated microVM and isolated filesystem per sandbox, with an authorization proxy that injects credentials outside the sandbox to keep secrets isolated. It's used by companies like and drops in with just a few lines of code. #AIAgents# #Security#