Executive Summary

CVE-2026-33873 is a vulnerability in Langflow's Agentic Assistant feature prior to version 1.9.0. This feature generates and validates Python component code using large language models (LLMs). During the validation phase, the system dynamically executes the LLM-generated Python code server-side by compiling and running it, including instantiating the generated classes. Because this execution is dynamic and not static, if an attacker can access the Agentic Assistant feature and influence the model output, they can inject arbitrary Python code that will be executed on the server.

The vulnerability arises because the validation process uses Python's exec() function to run the generated code, crossing a critical trust boundary. This means malicious code embedded in the generated component can run with the privileges of the Langflow server process. The vulnerability requires authentication or access to the Agentic Assistant feature, but default settings like AUTO_LOGIN enabled can increase exposure.

Version 1.9.0 of Langflow fixes this issue by changing the validation approach to avoid dynamic execution of untrusted code.

Useful 0 Not Useful 0

Impact Analysis

If exploited, this vulnerability allows an attacker with access to the Agentic Assistant feature to execute arbitrary Python code on the Langflow server. This can lead to severe impacts including:

• Execution of arbitrary operating system commands.
• Reading from or writing to the server's file system.
• Disclosure of credentials or sensitive secrets stored on the server.
• Full compromise of the Langflow process, potentially allowing further lateral movement or persistent access.

The vulnerability requires authentication or access to the Agentic Assistant feature, but insecure default configurations such as enabling AUTO_LOGIN can allow attackers to bypass authentication and exploit the vulnerability more easily.

Useful 0 Not Useful 0

Detection Guidance

Detection of this vulnerability involves monitoring access to the Agentic Assistant feature endpoints such as `/assist` and `/assist/stream`, which accept user inputs that influence LLM-generated Python code execution. Since the vulnerability requires authentication or access to these features, reviewing authentication logs for unusual or unauthorized access attempts is important.

Additionally, detection can focus on identifying dynamic execution of Python code during validation, which occurs in the `create_class()` function that uses `exec()` to run code extracted from LLM responses. Monitoring for unexpected Python execution or suspicious process activity related to Langflow may help.

Specific commands are not provided in the resources, but general approaches include:

• Review web server or application logs for requests to `/assist` and `/assist/stream` endpoints.
• Check authentication logs for use of default or auto-login credentials, especially if `AUTO_LOGIN` is enabled.
• Use process monitoring tools (e.g., `ps`, `top`, or Windows Task Manager) to detect unusual Python processes spawned by Langflow.
• Inspect network traffic for suspicious API calls or unusual payloads targeting the Agentic Assistant feature.

Because the vulnerability involves dynamic execution of code during validation, static code analysis or runtime monitoring tools that detect execution of unexpected Python code or commands may be helpful.

Useful 0 Not Useful 0

Mitigation Strategies

The primary mitigation step is to upgrade Langflow to version 1.9.0 or later, where the vulnerability is fixed by redesigning the validation process to avoid dynamic execution of untrusted code.

If upgrading immediately is not possible, the following steps should be taken:

• Disable the `AUTO_LOGIN` feature in production environments to prevent automatic login with default superuser credentials, which can increase exposure.
• Restrict access to the Agentic Assistant feature endpoints (`/assist` and `/assist/stream`) to trusted and authenticated users only.
• Review and harden authentication settings, including API key validation and JWT configurations, to ensure strong authentication and authorization controls.
• Monitor and audit usage of the Agentic Assistant feature to detect any suspicious activity.

Longer-term mitigation involves redesigning the validation mechanism to use static analysis or sandboxed execution environments instead of dynamic execution (`exec()`) of generated code.

Useful 0 Not Useful 0

Compliance Impact

CVE-2026-33873 allows an authenticated attacker to execute arbitrary Python code on the server via the Agentic Assistant feature in Langflow. This can lead to unauthorized OS command execution, file system access including reading and writing files, and disclosure of credentials or secrets.

Such unauthorized access and potential data exposure can compromise the confidentiality and integrity of sensitive data, which are core requirements under common standards and regulations like GDPR and HIPAA.

If personal data or protected health information is stored or processed by Langflow, this vulnerability could lead to violations of data protection obligations, including failure to implement adequate security measures to protect data against unauthorized access or disclosure.

Additionally, the presence of the AUTO_LOGIN feature enabled in production environments increases the risk of unauthorized access, further impacting compliance with security best practices required by these regulations.

Mitigation steps such as upgrading to version 1.9.0 and disabling AUTO_LOGIN in production are critical to maintaining compliance and reducing risk.

Useful 0 Not Useful 0