This article outlines six patterns for defending against prompt injection attacks in large language models, emphasizing that defenses should not rely on the model's inherent intelligence. The author proposes implementing 'side filters' using regex and classifiers to screen indirect content like emails and documents before they reach the model. Additionally, a system of tool whitelisting and capability tokens is suggested, where the model's ability to call tools is controlled by a separate, secure token issuance mechanism rather than direct model instruction. AI
IMPACT Provides practical defense strategies against prompt injection, a critical security concern for LLM applications.
RANK_REASON The article details technical patterns for LLM security, akin to a research paper or technical blog post. [lever_c_demoted from research: ic=1 ai=1.0]
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