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TL;DR: treat agent defense like fraud controls on a human operator—architect systems to reduce blast radius even when manipulation succeeds.
TL;DR: treat agent defense like fraud controls on a human operator—architect systems to reduce blast radius even when manipulation succeeds.
OpenAI frames prompt injection not as simple string-matching but as an evolving social-engineering-like threat that targets tool-using agents. The post defines the problem of malicious external content steering agents into risky actions. It then proposes constraining impact via architecture (source/sink thinking), defense-in-depth, and explicit control over high-risk capabilities.
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