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CVE Vulnerability Database

CVE-2026-7157: aider-mcp-server Command Injection RCE Flaw

CVE-2026-7157 is a command injection vulnerability in disler aider-mcp-server that enables remote code execution through the relative_editable_files argument. This post covers technical details, exploit status, and mitigation.

Published:

CVE-2026-7157 Overview

A command injection vulnerability has been identified in disler aider-mcp-server, affecting the server.py file within the aider_ai_code component. The vulnerability exists in the handling of the relative_editable_files argument, allowing attackers to inject arbitrary commands that can be executed on the target system. Remote exploitation is possible via network access without authentication requirements.

Critical Impact

Attackers can remotely execute arbitrary system commands through malicious input to the relative_editable_files parameter, potentially leading to complete system compromise.

Affected Products

  • disler aider-mcp-server up to commit b2516fa466d0d851932da92ee6d0e66946db9efc
  • aider_ai_code component (src/aider_mcp_server/server.py)
  • All versions prior to the vulnerable commit (rolling release model)

Discovery Timeline

  • 2026-04-27 - CVE-2026-7157 published to NVD
  • 2026-04-29 - Last updated in NVD database

Technical Details for CVE-2026-7157

Vulnerability Analysis

This command injection vulnerability (CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component) occurs in the server.py file of the aider_ai_code component. The flaw stems from improper handling of user-supplied input through the relative_editable_files argument. When this parameter is processed, malicious payloads containing shell metacharacters or command sequences are not properly sanitized, allowing them to be interpreted and executed by the underlying operating system.

The vulnerability is exploitable remotely over the network without requiring authentication or user interaction. An exploit has been publicly disclosed, increasing the likelihood of active exploitation. The project maintainers were notified through GitHub Issue #16 but have not yet responded.

Root Cause

The root cause is insufficient input validation and sanitization of the relative_editable_files argument before it is passed to system commands or shell interpreters. The application fails to properly escape or validate special characters that have meaning in shell contexts, such as semicolons, pipes, backticks, and command substitution syntax. This allows attacker-controlled data to break out of the intended context and execute arbitrary commands.

Attack Vector

The attack can be executed remotely over the network. An attacker crafts a malicious request containing command injection payloads within the relative_editable_files parameter. When the server processes this input, the injected commands are executed with the privileges of the application process. This could enable attackers to:

  • Execute arbitrary system commands
  • Exfiltrate sensitive data from the server
  • Establish persistence mechanisms
  • Pivot to other systems on the network
  • Compromise the integrity and availability of the affected system

For technical details on the vulnerability, refer to the VulDB entry for this vulnerability and the GitHub Issue #16 discussion.

Detection Methods for CVE-2026-7157

Indicators of Compromise

  • Unusual process spawning from the aider-mcp-server Python process
  • Unexpected outbound network connections from the server hosting the application
  • Suspicious command-line arguments in process logs containing shell metacharacters
  • Modified files or new files in system directories created by the application user
  • Unexpected user accounts or SSH keys added to the system

Detection Strategies

  • Monitor application logs for requests containing shell metacharacters such as ;, |, &, `, $(), or $()
  • Implement network intrusion detection rules to identify exploitation attempts targeting the relative_editable_files parameter
  • Deploy endpoint detection to alert on anomalous child process creation from Python processes
  • Review web application firewall logs for blocked command injection patterns

Monitoring Recommendations

  • Enable verbose logging for the aider-mcp-server application to capture incoming requests
  • Configure process monitoring to track all processes spawned by the application service account
  • Implement file integrity monitoring on critical system directories
  • Set up alerts for any outbound connections from the server to uncommon destinations

How to Mitigate CVE-2026-7157

Immediate Actions Required

  • Review usage of disler aider-mcp-server in your environment and assess exposure
  • Implement network segmentation to limit access to the vulnerable service
  • Deploy web application firewall rules to filter command injection patterns
  • Monitor the aider-mcp-server GitHub repository for security updates
  • Consider disabling the service until a patch is available if risk is unacceptable

Patch Information

As of the last update, no official patch has been released by the project maintainers. The project follows a rolling release approach, meaning updates are continuously deployed without versioned releases. Users should monitor the GitHub repository and GitHub Issue #16 for updates regarding a fix. The maintainers were informed of the vulnerability but have not yet responded.

Workarounds

  • Restrict network access to the aider-mcp-server to trusted IP addresses only
  • Implement input validation at the application gateway or reverse proxy level to sanitize the relative_editable_files parameter
  • Run the application in a sandboxed or containerized environment with minimal privileges
  • Use a web application firewall configured with command injection detection rules
bash
# Example: Restrict network access using iptables
# Allow only trusted IP ranges to access the service port
iptables -A INPUT -p tcp --dport <SERVICE_PORT> -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport <SERVICE_PORT> -j DROP

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

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