NameTrap: Steering AI Coding Agents Toward Security Vulnerabilities via Variable Names
NameTrap shows that strategically chosen variable names in public repositories can steer AI coding agents into generating exploitable vulnerabilities — weaponising the web-sourced context that shapes AI-written code.
Summary written by editorial AI · Source link below
arXiv:2610.34253v1 Announce Type: new Abstract: AI coding agents routinely clone repositories, install packages, and read source code obtained from the Web, so publicly hosted code becomes part of the context that shapes the code they write. We show that this Web-borne context can be weaponized without any explicit instruction. We present NameTrap, a source-code poisoning attack that only renames identifiers in third-party Python files to steer AI coding agents toward attacker-targeted Common W
Editorial Analysis
Enterprises relying on AI coding agents face a new supply-chain threat: publicly hosted code can covertly poison AI-generated output through semantic manipulation that evades traditional code-review checks.
Add context-poisoning scenarios to your AI coding agent threat model and enforce automated vulnerability scanning on all AI-generated code before merge.
Public repositories can weaponise AI coding tools into writing vulnerable code — a supply-chain risk requiring new controls for AI-assisted development.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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