Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment

Coding agents powered by large language models are becoming central modules of modern IDEs. They help users to perform various complex coding tasks by invoking tools. Although powerful, tool-invocation operation in coding agents opens a substantial attack surface for adversaries. Prior work has demonstrated attacks against both general-purpose LLM agents and domain-specific agents. However, to our knowledge, no previous works focus on the security risks of tool-invocation in coding agents. To fill this gap, we conduct the first systematic, in-depth red-teaming from a tool-invocation perspective in six popular real-world coding agents: Cursor, Claude Code, Copilot, Windsurf, Cline, and Trae. Our red-teaming proceeds in two phases. In Phase 1, we conduct a prompt leakage as reconnaissance to recover system prompts and related context. Specifically, we identify a mode gap between chat generation and tool-call argument generation: during schema-driven argument completion, the model behaves as if it is performing benign structured filling and may copy hidden agent context into tool-call arguments. We instantiate this gap as ToolLeak, which exfiltrates agent-internal prompts (e.g., system prompts and tool metadata) via required tool parameters. In Phase 2, we hijack the tool-invocation behavior of the coding agent with a novel two-channel prompt injection in the tool description and the tool return. Our hijacking achieves remote code execution (RCE) on major real-world coding agents. We adaptively construct the malicious payload using leaked security information in Phase 1. Our evaluation shows that ToolLeak substantially outperforms strong prompt-leak baselines in both emulated and real-world settings. In the emulated setting, ToolLeak achieves the best overall prompt-exfiltration performance across all six simulated coding agents. On real-world coding agents, ToolLeak achieves the best pseudo-recall on 18 of 25 evaluated agent-LLM pairs. Furthermore, our red-teaming successfully hijacks all six real-world coding agents for RCE and consistently yields higher attack success rates than baseline attacks. Lastly, we present two case studies on Cursor and Claude Code to demonstrate the real-world impact of our red-teaming.