New AI Security Threat: How Hackers Are Exploiting GitHub Copilot & Cursor?

A new attack vector in AI-powered code assistants, such as GitHub Copilot and Cursor, has been revealed. Hackers can exploit the "Rules File Backdoor" by embedding harmful instructions into configuration files and altering AI-generated code. The result? Undetectable security vulnerabilities that could impact millions of developers and software users worldwide.

Unlike traditional code injection attacks, this method exploits the AI, turning an essential coding assistant into an unintentional security risk. By embedding hidden instructions within rule files, attackers can bypass traditional security reviews and influence AI-generated code without raising red flags.

The Growing Risk of AI Coding Assistants

Generative AI coding tools have quickly become essential in modern software development. A 2024 GitHub survey found that 97% of enterprise developers use AI-powered coding assistants. While these tools enhance productivity, they also introduce new security risks by expanding the attack surface for cyber threats.

Hackers now see AI coding assistants as prime targets, recognizing their ability to inject vulnerabilities at scale into the software supply chain. As AI integration deepens, securing these systems is no longer optional—it’s a necessity.

How Attackers Exploit AI Rule Files

A vulnerability has been identified in how AI assistants interpret and apply contextual instructions from rule files. These files, intended to enforce coding standards and best practices, are often:

• Shared across teams: Stored in centralized repositories accessible to multiple users.
• Widely adopted: Common in open-source projects and enterprise environments.
• Trusted implicitly: Assumed to be harmless configuration files.
• Rarely validated: Incorporated into projects without rigorous security checks.

These characteristics make rule files an attractive target for attackers. A well-crafted malicious rule file can develop across projects and generate security vulnerabilities to countless downstream dependencies.

The Attack Mechanism: A Step-by-Step Breakdown

By manipulating rule files, attackers can influence AI-generated code in several ways:

1. Contextual Manipulation: Injecting instructions that appear benign but easily alter code generation behaviour.
2. Unicode Encryption: Hiding malicious commands using zero-width characters, making them invisible to human reviewers.
3. Semantic Hijacking: Using precise linguistic patterns to direct AI toward insecure implementations.
4. Persistent Compromise: Once a poisoned rule file is incorporated, it continues affecting future AI-generated code without detection.

Real-World Demonstration

In a controlled test, researchers demonstrated how Cursor’s "Rules for AI" feature could be exploited:

• A malicious rule file was created with hidden Unicode characters.
• The AI assistant, when prompted to generate a simple HTML page, embedded a malicious script.
• The AI-generated code contained a backdoor, yet the assistant never mentioned the insertion of the script.

This approach ensures that security teams remain unaware of the injected vulnerability, increasing the risk of widespread exploitation.

Impact on AI-Powered Development

The "Rules File Backdoor" attack introduces multiple security concerns:

• Overriding Security Controls: AI-generated code can bypass established security measures.
• Generating Vulnerable Code: Attackers can introduce insecure cryptographic algorithms, authentication bypasses, and weak input validation.
• Data Exfiltration: Malicious rules can instruct AI to leak sensitive information such as database credentials, API keys, and user data.
• Long-Term Persistence: Poisoned rule files remain in repositories, affecting all future AI-generated code and spreading vulnerabilities across software supply chains.

Because rule files are often shared and reused, a single compromised file can have an exponential impact, affecting thousands of developers and projects.

Who is at Risk?

This attack vector primarily threatens organizations and developers who rely on shared or open-source rule files. The key propagation methods include:

• Developer Communities: Attackers sharing seemingly helpful but compromised rule files.
• Open-Source Contributions: Malicious pull requests introducing vulnerabilities into popular repositories.
• Project Templates: Prebuilt configurations that unknowingly include security flaws.

How to Protect Against AI-Based Attacks

Security teams must rethink their approach to AI-generated code and implement proactive measures to mitigate risks. Recommended actions include:

• Conducting Rule File Audits: Regularly review and validate rule files for hidden instructions or unusual formatting.
• Implementing Security Reviews: Treat AI configuration files with the same scrutiny as executable code.
• Deploying Detection Tools: Use automated tools to identify obfuscated instructions or unusual code patterns.
• Monitoring AI-Generated Code: Pay close attention to unexpected code insertions, external resource references, and unverified imports.

Industry Response and Responsible Disclosure

AI coding vendors place the responsibility on users to review and verify AI-generated outputs.

• February 26, 2025: Initial disclosure to Cursor.
• March 6, 2025: Cursor determined that this risk falls under user responsibility.
• March 12, 2025: Disclosure to GitHub, with a similar response emphasizing user diligence.

These responses highlight the urgent need for greater security awareness and safeguards in AI-driven development environments.

Securing the Future of AI-Assisted Coding

The rise of AI-powered development tools brings both opportunities and challenges. While these assistants streamline workflows and boost productivity, they also introduce new security risks that traditional review processes may not catch.

The "Rules File Backdoor" is a wake-up call for the industry. As AI continues to shape the future of software development, security strategies must evolve accordingly. Developers, security teams, and organizations must work together to implement rigorous security measures that protect AI-generated code from exploitation.