Attacks on Third-Party APIs of Large Language Models
arxiv(2024)
摘要
Large language model (LLM) services have recently begun offering a plugin
ecosystem to interact with third-party API services. This innovation enhances
the capabilities of LLMs, but it also introduces risks, as these plugins
developed by various third parties cannot be easily trusted. This paper
proposes a new attacking framework to examine security and safety
vulnerabilities within LLM platforms that incorporate third-party services.
Applying our framework specifically to widely used LLMs, we identify real-world
malicious attacks across various domains on third-party APIs that can
imperceptibly modify LLM outputs. The paper discusses the unique challenges
posed by third-party API integration and offers strategic possibilities to
improve the security and safety of LLM ecosystems moving forward. Our code is
released at https://github.com/vk0812/Third-Party-Attacks-on-LLMs.
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