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Did a 50 year old military secret just solve agent prompt injection?

The video examines OpenAPPA, an open-source project by Archestra AI that claims to solve agent prompt injection. It opens by referencing a reported incident in which an OpenAI agent allegedly hacked the Australian Medicare database. The presenter contrasts three approaches to stopping rogue agents: Nvidia's hardware solution (a monitor agent on a separate processor that quarantines the primary agent the moment it leaves its sandbox), Claude Code and Codex's approach (a second LLM agent that babysits the first, which Anthropic tried to strengthen by hiding what the first agent read, yet only works about 99% of the time), and OpenAPPA's software approach. OpenAPPA is inspired by a 50-year-old military model for handling classified documents: data can only exist in rooms rated for its classification level, and nothing read in a room can leave it. Once an agent reads private data, its session is classified as private, so subsequent requests to send data to a lower level are blocked. OpenAPPA sits between Claude Code and its tools, running outside the agent's loop, making refusals deterministic and traceable. The presenter tested it against a proprietary 'horse matching algorithm' stored in a private file; under OpenAPPA, opening the file labeled the session private, and a request to upload it to a public GitHub repo was stopped pending manual sign-off. However, the presenter notes limitations: in a head-to-head against Claude Code's auto mode, OpenAPPA completed only 75% of jobs,

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