"Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation"

Simple text can make AI ignore its safety rules and misbehave

This paper reveals a critical security flaw in RAG-based AI agents by demonstrating how a simple prefix "Ignore the document" can manipulate LLMs to bypass safety measures and produce harmful outputs.

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https://arxiv.org/abs/2412.04415

🎯 Original Problem:

RAG-based AI agents inherit vulnerabilities from their underlying LLMs, making them susceptible to adversarial attacks that can compromise their safety and reliability.

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🛠️ Solution in this Paper:

→ The research introduces a deceptively simple attack method using the prefix "Ignore the document" to manipulate LLM outputs.

→ Three attack strategies were tested: Baseline, Adaptive Attack Prompt, and ArtPrompt, across multiple LLM architectures.

→ The attack exploits fundamental weaknesses in LLM instruction processing, overriding contextual safeguards in RAG pipelines.

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💡 Key Insights:

→ Current agent-level defenses are inadequate against direct LLM core manipulation

→ Simple adversarial prefixes can effectively override RAG pipeline safeguards

→ Multi-agent systems with shared LLM cores are particularly vulnerable

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📊 Results:

→ Attack success rates varied significantly across models: Gemma2 (97.3%), GPT4o (22.4%), Llama3.1 (79.1%)

→ Adaptive Attack Prompt showed highest success rates across all models

→ Models with pre-trained defenses showed minimal improvement in attack resistance