Prompt injection defenses shouldn't rely solely on a model’s own training. Meta's Muse agent assumes the model will be tricked; instead, it builds security at the OS level. 🔑 Model never handles real credentials 🛡️ Tools run in isolated Linux containers 🛑 Independent gatekeeper verifies outbound calls Read the full analysis: hubs.la/Q04xZx500 #DeepLearningAI #AIAgents #AISecurity

Sep 21, 2026 · 4:20 PM UTC

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Replying to @DeepLearningAI
model never holding real credentials is the hard part. that's agent/browser infra not the chat model
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Replying to @DeepLearningAI
セキュリティ設計でよく聴く「多層防御」って知ってますか？ 実は「ゼロトラスト」が防御の土台であり、 モデルだけでなくOSレベルで信用の境界を分けることで 認証情報をシステム全体で守る仕組みなんです。 防御はパイプライン全体の料理で味が決まる感じですね。
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Replying to @DeepLearningAI
the honest version of prompt injection defense: never let the model touch real credentials in the first place. everything else is guardrails on wishful thinking
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Replying to @DeepLearningAI
OS isolation is the only sane security pattern for tools anyway.
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Replying to @DeepLearningAI
Buen principio: no confiar en que el modelo "se porte bien", sino diseñar el control alrededor. Es control interno de toda la vida: segregación de funciones, credenciales que el operador nunca ve y un tercero que valida la salida. Si el agente toca el ERP, ese es el mínimo.
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