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PromptKeeper: Safeguarding System Prompts for LLMs
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PromptKeeper: Safeguarding System Prompts for LLMs
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System prompts are widely used to guide the outputs of large language models (LLMs). These prompts often contain business logic and sensitive information, making their protection essential. However, adversarial and even regular user queries can exploit LLM vulnerabilities to expose these hidden prompts. To address this issue, we propose PromptKeeper, a defense mechanism designed to safeguard system prompts by tackling two core challenges: reliably detecting leakage and mitigating side-channel vulnerabilities when leakage occurs. By framing detection as a hypothesis-testing problem, PromptKeeper effectively identifies both explicit and subtle leakage. Upon leakage detected, it regenerates responses using a dummy prompt, ensuring that outputs remain indistinguishable from typical interactions when no leakage is present. PromptKeeper ensures robust protection against prompt extraction attacks via either adversarial or regular queries, while preserving conversational capability and runtime efficiency during benign user interactions.
Forward citations
Cited by 4 Pith papers
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PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts
PragLocker protects agent prompts as IP by building non-portable obfuscated versions that function only on the intended LLM through code-symbol semantic anchoring followed by target-model feedback noise injection.
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Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives
Large-scale analysis of 1.2B URLs identifies 15.3K indirect prompt injection instances in the wild, mostly targeting AI systems with up to 8% compliance in model experiments.
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PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts
PragLocker generates function-preserving but non-portable prompts for LLM agents via code-symbol semantic anchoring followed by target-model feedback noise injection.
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Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications
Large-scale empirical study finds widespread prompt leaking in commercial LLM apps and introduces AREA defense that improves usability while resisting leaks.
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