How to Secure AI Agents With Container Sandboxing
During a closed beta of a data-analysis agent we were building, a tester asked it to “clean up the temp files from this session.” The agent wrote a short Python script to clean up the temp files from the session and ran it, and the script unexpectedly wandered beyond its intended scope, deleting documents that it should not have touched. Nothing irreplaceable was lost; it was a beta environment with backups, but it was a gift of a near-miss, landing three weeks before launch instead of three weeks after.
The mistake wasn't the agent's. The mistake was ours: we'd given it a working directory convention and called that a sandbox. A convention is something that well-behaved code respects. It is not a boundary, and an LLM-generated script, whether through a genuine mistake or a manipulated prompt, has no reason to respect a boundary that only exists in a comment.
Why...
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