I asked Meta’s Muse for its filesystem and it sent me 6.8GB
summary
A security researcher discovered that Meta's Muse AI could be prompted to export its entire internal root filesystem, revealing sensitive system architecture and internal documentation. This vulnerability allows users to access internal runtime files, including SSH keys and configuration scripts, through simple conversation and connected export destinations. The leak demonstrates a significant gap in thes AI's sandboxing and data egress controls. The researcher reported the findings to Meta, though the company initially dismissed the report as not applicable.
key points
- The exported 6.8 GB archive contained detailed internal documentation on 'Hatch' (Muse's internal name), including identity files, agent logs, and guides for browser use, payments, and credentials.
- The filesystem revealed an experimental hardware integration called 'Meta Home Link' using ESP32-C5 chips, suggesting Meta is developing ways for the AI to interact with local home network devices.
- Muse manages long-term memory using plain Markdown files and a 'dreaming' process where a nightly background job reviews conversations to synthesize user preferences into standing guidance.
who should read it
Security researchers and AI developers interested in LLM sandboxing, prompt injection, and the internal architecture of production AI agents.
discussion skeptical
consensus
Commenters are deeply divided over whether prompt engineering and iterative evaluation constitute actual software engineering. While some argue that rigorous empirical testing and statistical reliability mimic engineering processes, others contend that the lack of determinism and the reliance on 'educated guesses' make it a form of alchemy or technician work rather than true engineering. There is a general skepticism toward the idea that Markdown files and stochastic word generators can replace traditional, deterministic computing principles.
top insight
The debate highlights a fundamental tension between classical engineering, which relies on reproducible, safe methods and first principles, and AI development, which operates on probabilistic systems. One commenter argues that the ability to handle probabilistic components is actually a core requirement of many engineering fields, suggesting that the shift toward non-deterministic systems is an evolution of engineering rather than a departure from it.
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