A single config file in a cloned repository could steal your AWS credentials through Amazon Q Developer
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A single config file in a cloned repository could steal your AWS credentials through Amazon Q Developer

June 26, 20265 views2 min read

A high-severity flaw in Amazon Q Developer could allow attackers to steal AWS credentials by exploiting a single configuration file in a cloned repository.

A newly discovered vulnerability in Amazon Q Developer has raised serious security concerns for developers using the AI-powered coding assistant. The flaw, tracked as CVE-2026-12957, could allow a malicious actor to silently execute commands on a developer’s machine and steal their AWS credentials simply by cloning a repository with a single, seemingly benign configuration file.

How the Vulnerability Works

Wiz Research identified the issue and reported it to Amazon on April 20. The vulnerability stems from how Amazon Q Developer handles configuration files during repository cloning, particularly those that use the Model Configuration Protocol (MCP). An attacker could embed malicious code within a configuration file, which would then be executed automatically when a developer clones the repository and opens it in Amazon Q Developer. This process happens without the developer’s knowledge or consent, making it especially dangerous.

Impact and Response

The flaw is classified as high-severity due to its potential for widespread exploitation. AWS credentials are often highly sensitive, granting access to cloud resources, data, and infrastructure that could be used for further attacks. Amazon patched the vulnerability on May 12, but the public disclosure today highlights the urgency of the issue. Developers are advised to update their Amazon Q Developer tools immediately and review any repositories they’ve cloned recently for suspicious configuration files.

This incident underscores the growing risks associated with AI-powered development tools and the need for robust security measures. As more developers rely on AI assistants for code generation and debugging, vulnerabilities like this one can have cascading effects across entire development ecosystems.

Conclusion

The discovery of CVE-2026-12957 serves as a stark reminder that even trusted AI tools can harbor hidden security risks. As the adoption of AI in development workflows continues to rise, security researchers and platform vendors must remain vigilant to protect developers and their infrastructure from increasingly sophisticated threats.

Source: TNW Neural

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