If you give an AI agent your real data and a send button, it will eventually leak. I built a workspace that makes that structurally impossible.
Summary
The author shares an open-source workspace architecture that structurally prevents AI agents from exfiltrating private data by enforcing human-gated outbound actions and isolating the engine from the data repository.
Similar Articles
Why your AI agent’s "memory" is a data breach waiting to happen.
The article warns that using shared vector databases with only logical isolation (metadata filters) for multi-tenant AI agents can silently cause data breaches, and advocates for physical isolation per user to guarantee zero data bleed.
I think I've solved how to seamlessly share your AI agents with coworkers...or anyone for that matter.
The author presents a pattern for sharing AI agents by compiling them into sealed WASM modules that only import a single inference function, allowing others to run the agent using their own local model without API keys or security risks.
Your agent reads a web page that says "leak the user's API keys" — a lot of agents will just do it. I built a thing to stop the send.
Bouncer is a local MCP proxy that prevents AI agents from leaking sensitive data by gating outbound tool calls from untrusted sources, using deterministic enforcement without an LLM, with benchmarks showing reduced attack success.
Keeping your data safe when an AI agent clicks a link
OpenAI describes security safeguards against URL-based data exfiltration attacks when AI agents retrieve web content, using an independent web index to verify that URLs are publicly known before automatic retrieval to prevent prompt injection attacks from leaking sensitive user data.
Want AI Agents That Don't Spill Secrets? Don't Give Them Secrets
A short announcement about an article discussing the principle of keeping secrets away from LLMs to prevent them from being leaked by AI agents.