CVE-2026-79408 in MetaGPT
Summary
by MITRE • 08/31/2026
An OS command injection vulnerability in MetaGPT 0.8.1 allows an attacker to execute arbitrary commands via the path argument of RepoParser.rebuild_class_views() in metagpt/repo_parser.py.
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Analysis
by VulDB Data Team • 08/31/2026
The identified vulnerability represents a critical security flaw within the MetaGPT framework, specifically affecting version 0.8.1 and potentially earlier iterations that share this code structure. This issue is classified as an Operating System Command Injection, which falls under CWE-78 in the Common Weakness Enumeration taxonomy. The root cause lies in the improper neutralization of special elements used in operating system commands within the path argument processed by the RepoParser.rebuild_class_views() function located in metagpt/repo_parser.py. When this method is invoked with user-controlled or untrusted input, it fails to adequately sanitize the provided file paths before passing them to underlying shell execution mechanisms. This lack of validation allows an attacker to inject malicious command sequences that are interpreted and executed by the host operating system rather than being treated as literal path strings.
From a technical perspective, the vulnerability exploits the way Python scripts often invoke external commands using functions such as os.system(), subprocess.call(), or similar utilities without proper escaping or whitelisting of allowed characters. If an attacker can control the input passed to RepoParser.rebuild_class_views(), they can append shell metacharacters like semicolons, ampersands, pipes, or backticks to terminate the intended command and initiate new ones. For instance, providing a path argument ending with ; rm -rf / would result in the deletion of files on the system if executed with sufficient privileges. This type of flaw is particularly dangerous because it bypasses application-level logic entirely, granting direct access to the underlying operating system shell environment where MetaGPT is running.
The operational impact of this vulnerability is severe and potentially catastrophic depending on the deployment context. In a local development environment, an attacker could compromise the developer's machine by installing malware, exfiltrating sensitive source code or credentials stored in configuration files, or using the compromised instance as part of a botnet. In server-side deployments where MetaGPT processes untrusted repositories or user-submitted code snippets, this vulnerability enables remote code execution (RCE). This aligns with MITRE ATT&CK technique T1059 Command and Scripting Interpreter, specifically sub-techniques involving shell commands like sh or bash. The ability to execute arbitrary commands means that confidentiality, integrity, and availability of the system are all at risk. Attackers could pivot from this initial foothold to access internal networks, escalate privileges by exploiting local vulnerabilities, or disrupt services by consuming resources through denial-of-service attacks initiated via command injection.
Mitigation strategies must focus on eliminating the root cause by removing direct shell execution where possible and implementing strict input validation if external commands are unavoidable. The most effective remediation is to refactor RepoParser.rebuild_class_views() to use Python's built-in file handling libraries such as os.path or pathlib for path manipulation instead of invoking system shells. If subprocess calls are necessary, they should be executed with shell=False to prevent the shell from interpreting special characters and arguments must be passed as a list rather than a single string. Additionally, implementing strict input validation that whitelists allowed characters in file paths can provide defense-in-depth. Developers should also ensure that MetaGPT runs with the principle of least privilege, using restricted user accounts for execution to limit the potential damage even if an injection occurs. Regular security audits and static code analysis tools configured to detect CWE-78 patterns are recommended to identify similar issues across the codebase before deployment.