Code generation has emerged as a pivotal capability of Large Language Models(LLMs), revolutionizing development efficiency for programmers of all skill levels. However, the complexity of data structures and algorithmic logic often results in functional deficiencies and security vulnerabilities in generated code, reducing it to a prototype requiring extensive manual debugging. While Retrieval-Augmented Generation (RAG) can enhance correctness and security by leveraging external code manuals, it simultaneously introduces new attack surfaces. In this paper, we pioneer the exploration of attack surfaces in Retrieval-Augmented Code Generation (RACG), focusing on malicious dependency hijacking. We demonstrate how poisoned documentation containing hidden malicious dependencies (e.g., matplotlib_safe) can subvert RACG, exploiting dual trust chains: LLM reliance on RAG and developers' blind trust in LLM suggestions. To construct poisoned documents, we propose ImportSnare, a novel attack framework employing two synergistic strategies: 1)Position-aware beam search optimizes hidden ranking sequences to elevate poisoned documents in retrieval results, and 2)Multilingual inductive suggestions generate jailbreaking sequences to manipulate LLMs into recommending malicious dependencies. Through extensive experiments across Python, Rust, and JavaScript, ImportSnare achieves significant attack success rates (over 50% for popular libraries such as matplotlib and seaborn) in general, and is also able to succeed even when the poisoning ratio is as low as 0.01%, targeting both custom and real-world malicious packages. Our findings reveal critical supply chain risks in LLM-powered development, highlighting inadequate security alignment for code generation tasks. To support future research, we will release the multilingual benchmark suite and datasets. The project homepage is https://importsnare.github.io.