Title: System Prompt Poisoning: Persistent Attacks on Large Language Models Beyond User Injection
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: System prompt poisoning

Abstract: Large language models (LLMs) have gained widespread adoption across diverse domains and applications. However, as LLMs become more integrated into various systems, concerns around their security are growing. Existing relevant studies mainly focus on threats arising from user prompts (e.g., prompt injection attack) and model output (e.g. model inversion attack), while the security of system prompts remains largely overlooked. This work bridges this critical gap. We introduce system prompt poisoning, a new attack vector against LLMs that, unlike traditional user prompt injection, poisons system prompts and persistently impacts all subsequent user interactions and model responses. We propose three practical attack strategies: brute-force poisoning, adaptive in-context poisoning, and adaptive chain-of-thought (CoT) poisoning, and introduce Auto-SPP, a framework that automates the poisoning of system prompts with these strategies. Our comprehensive evaluation across four reasoning and non-reasoning LLMs, four distinct attack scenarios, and two challenging domains (mathematics and coding) reveals the attack's severe impact. The findings demonstrate that system prompt poisoning is not only highly effective, drastically degrading task performance in all scenario-strategy combinations, but also persistent and robust, remaining potent even when user prompts employ prompting-augmented techniques like CoT. Critically, our results highlight the stealthiness of this attack by showing that current black-box based prompt injection defenses cannot effectively defend against it.

TL;DR: Introducing and assessing the effects of a new attack vector on large language models