Attacks on Third-Party APIs of Large Language Models
<|reference_start|>Attacks on Third-Party APIs of Large Language Models: 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.<|reference_end|>
@article{zhao2024attacks,
    title={Attacks on Third-Party APIs of Large Language Models},
    author={Wanru Zhao, Vidit Khazanchi, Haodi Xing, Xuanli He, Qiongkai Xu,
  Nicholas Donald Lane},
    journal={arXiv preprint arXiv:2404.16891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16891},
    primaryClass={cs.CR cs.AI cs.CL cs.CY}
}