A new cybersecurity threat is putting AI-powered code editors like GitHub Copilot and Cursor AI at serious risk. Researchers have uncovered a supply chain attack technique—called the “Rules File Backdoor”—that allows hackers to silently inject malicious code into AI-generated programming projects.
According to Ziv Karliner, CTO and Co-Founder of Pillar Security, this attack leverages innocent-looking configuration files to sneak harmful instructions into the AI’s workflow. In a detailed report shared with The Hacker News, Karliner explained how attackers manipulate the AI into producing vulnerable code without raising red flags during code reviews.
The attack revolves around “rules files”—a set of configurations designed to guide AI tools in following best practices and project-specific coding standards. Hackers can embed hidden prompts within these files, subtly influencing the AI’s behavior.
The trick lies in using invisible characters like zero-width joiners and bidirectional text markers. These hidden elements carry malicious instructions that the AI model interprets while generating code. As a result, the AI unknowingly creates code with security loopholes or embedded backdoors, bypassing traditional review processes.
What makes this tactic more dangerous is its stealth. These concealed instructions exploit the AI’s understanding of natural language and code semantics, manipulating it to override built-in safety checks. Consequently, developers might remain unaware of the vulnerability embedded right within the codebase.
The Rules File Backdoor attack presents a far-reaching supply chain risk. Once a compromised rules file becomes part of a project repository, every future code generation by any team member could inherit the malicious instructions.
Karliner warns that the danger doesn’t stop there. Even if a project is forked or shared, these hidden backdoors remain intact, silently spreading vulnerabilities across downstream dependencies and end-user applications.
“This tactic turns the developer’s most trusted assistant—AI code editors—into an unintentional accomplice,” Karliner explained. “It’s a dangerous shift, weaponizing AI tools themselves as the attack vector.”
After receiving responsible disclosure reports in February and March 2024, both GitHub and Cursor responded. However, their statements made it clear that reviewing AI-generated code remains the developer’s responsibility.
By design, these tools generate suggestions based on context and embedded instructions. If poisoned rule files manipulate that context, developers might unknowingly approve code that compromises security.
Cybersecurity experts now stress the need for rigorous manual reviews of both AI-generated code and any configuration files introduced into projects. Simply trusting suggestions from AI tools is no longer safe.
To stay protected, developers should:
- Regularly audit rule and config files for hidden characters or suspicious patterns
- Use tools designed to detect zero-width or invisible characters
- Avoid blindly accepting AI-generated code, no matter how trustworthy the source
- Educate teams on the potential risks of AI-driven coding assistants
- Monitor project forks and dependencies for inherited vulnerabilities.
This discovery highlights a growing challenge in modern software development: balancing the productivity gains of AI code editors with the increased risk of subtle attacks.
AI tools like GitHub Copilot and Cursor offer speed and efficiency but can also become a blind spot in cybersecurity if not carefully monitored. As hackers find creative ways to manipulate these systems, it’s crucial to reassess how we review and trust AI-generated code.
