Scanning the Harness: An Empirical Study of Supply-Chain Defects in AI Coding-Agent Configurations
AI coding agents such as Claude Code, Cursor, GitHub Copilot, and OpenAI Codex are configured through artifacts developers write and share: instruction files, skills, hooks, MCP server declarations, subagents. This harness is a dependency layer installed from marketplaces and public repositories, running with the developer's privileges, with no lockfile, no install-time check, and no vocabulary for what a component may do. We study it over 3,171 public GitHub repositories: 2,660 setups that assemble two or more component types and 511 published skill collections. We measure only rules decidable from bytes whose consequence is a security exposure, a configuration that cannot work, or a departure from the Agent Skills specification, and validate every finding before it counts: an independent implementation re-derives it from the repository at its pinned commit, a language-model adjudicator with a released prompt rules on every disagreement, and a second independent model session re-checks every counted pair. Three security classes survive: 9.8% of setups install an MCP server with no version pinned, 3.1% pre-approve arbitrary execution behind a scoped-looking grant such as Bash(python:*), and 3.8% carry a skill that pre-approves the shell for whoever installs it. In total 16.0% of setups carry a security defect and 16.7% a confirmed defect of any kind, against a raw scanner rate of 25.5% on the same rules; the third class ships inside 3.7% of collections, where a marketplace scan can see it. Rules that compare two files detect differences that are usually intended and are reported as observations. No credential-exfiltration path was confirmed. The instrument, corpus manifest, prompt, and every verdict are released.