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Security-Fidelity Tradeoff in Defending LLMs Against Prompt Injection
Defenses against indirect prompt injection in large language models often suppress untrusted text, which harms tasks requiring preservation of input, like translation and editing. Attack-success metrics fail to capture this tradeoff because ignoring injections and processing them faithfully yield s...
Understanding this tradeoff is crucial for developing defenses that protect against prompt injection without degrading model performance on tasks needing accurate input retention. This insight highlights limitations in current evaluation metrics for prompt injection defenses.
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Score 69
Source Type arxiv
Reposts 0
Topic Quality 56
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