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cv usk
@cv_usk
AI / Software Research Notes AI Agent, LLMOps, MLOps, Software Architecture ๆŠ•็จฟใฏๅ€‹ไบบใฎๆ„่ฆ‹ใงใ™ใ€‚
๊ฐ€์ž… May 2026
286 ํŒ”๋กœ์ž‰ ์ค‘    470 ํŒฌ
TL;DR: CaMeL stops prompt injection through system design, not model smarts. It solves 77% of AgentDojo tasks with provable security. Title: Defeating Prompt Injections by Design URL: Key points ใƒป๐Ÿง  The Privileged LLM only writes the plan and never sees untrusted data ใƒป๐Ÿ”’ The Quarantined LLM only parses untrusted data into structure and has no tools ใƒป๐Ÿท๏ธ Every value carries a capability for its provenance and allowed readers ใƒป๐Ÿ“œ Python security policies check each tool call before it runs ใƒป๐Ÿ“Š Zero successful attacks on Claude 3.5 Sonnet, vs. 8 for the next-best defense ใƒปโš–๏ธ Utility goes from 84% to 77%, with about 2.8x the tokens ใƒป๐Ÿ•ณ๏ธ Side channels such as exceptions and timing remain open problems Building a secure system around an untrusted model looks key for production agents. #PromptInjection# #AIAgents#
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