Phantom: General Trigger Attacks on Retrieval Augmented Language Generation
Keywords: Large Language Models, AI Security, AI Safety, RAG, Poisoning Attacks
TL;DR: Trigger-activated, query-agnostic, indirect prompt injection through single document poisoning against RAG augmented LLMs.
Abstract: Retrieval Augmented Generation (RAG) expands the capabilities of modern large language models (LLMs), by anchoring, adapting, and personalizing their responses to the most relevant knowledge sources. It is particularly useful in chatbot applications, allowing developers to customize LLM output without expensive retraining.
Despite their significant utility in various applications, RAG systems present new security risks. In this work, we propose new attack vectors that allow an adversary to inject a single malicious document into a RAG system's knowledge base, and mount a backdoor poisoning attack.
We design Phantom, a general two-stage optimization framework against RAG systems, that crafts a malicious poisoned document leading to an integrity violation in the model's output.
First, the document is constructed to be retrieved only when a specific trigger sequence of tokens appears in the victim's queries.
Second, the document is further optimized with crafted adversarial text that induces various adversarial objectives on the LLM output, including refusal to answer, reputation damage, privacy violations, and harmful behaviors.
We demonstrate our attacks on multiple LLM architectures, including Gemma, Vicuna, and Llama, and show that they transfer to GPT-3.5 Turbo and GPT-4. Finally, we successfully conducted a Phantom attack on NVIDIA's black-box production RAG system, "Chat with RTX".
Supplementary Material: zip
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 8226
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