A critical security vulnerability has been discovered in the Langflow AI Builder, a popular tool for creating agentic AI workflows.
The flaw, tracked as CVE-2025-3248, enables unauthenticated remote attackers to compromise servers running Langflow, potentially leading to full server control.
Security researchers have issued a warning about the severity of the issue, urging users to upgrade to the newly released Langflow version 1.3.0, which addresses this vulnerability.
The Langflow Platform and the Vulnerability
Langflow is an open-source Python-based application widely used for building AI agents and workflows through a visual interface.
It boasts over 50,000 stars on GitHub, with corporate backing from companies like DataStax and IBM. However, this popularity has not shielded it from security flaws.
The vulnerability lies in a poorly secured unauthenticated API endpoint (/api/v1/validate/code), which uses Python’s exec() function on user-supplied input.

This design flaw allows attackers to run arbitrary code on the server remotely—posing a significant risk when Langflow instances are exposed to the internet.
While Langflow has had minor issues in the past, including concerns about improper authentication configurations, this is the first unauthenticated code execution vulnerability confirmed to impact default installations.
By manipulating Python decorators or default function arguments, attackers can deploy malicious payloads capable of exfiltrating sensitive data, elevating user privileges, or even taking complete control of the server.

Exploitation Techniques
Security researchers have detailed two distinct methods for exploiting this vulnerability:
- Abusing Python Decorators:
Python decorators allow arbitrary expressions to execute during function definition. By inserting malicious code within a function’s decorator, attackers can compromise the Langflow server. For example:
@exec("import os; os.system('echo Vulnerable')")
def example_func():
pass
Such payloads are submitted through the validate/code API, enabling attackers to run commands such as establishing reverse shells or extracting sensitive data.
- Using Function Default Arguments:
Python’s function default arguments also permit expression execution during function definition. Attackers can use this feature to inject payloads, such as raising exceptions with sensitive outputs or executing system commands.
Interactive exploitation has also been demonstrated, allowing attackers to manipulate the server in real-time.
Public proof-of-concept (PoC) exploits have already surfaced online, increasing the urgency to fix exposed systems.

Over 500 Langflow instances are reportedly exposed on the internet, according to Censys.io scans. Without mitigation, these systems are at risk of:
- Data breaches and theft of sensitive user or project information.
- Internal network compromise if Langflow instances connect to larger infrastructure.
- Attackers gaining persistent server access, which could facilitate ransomware deployment or further lateral movement within corporate networks.
Mitigation and Best Practices
Langflow developers have released version 1.3.0, which addresses the vulnerability by requiring authentication for previously unauthenticated endpoints.
Experts recommend upgrading to this version immediately. Additionally, security practitioners advocate the following measures:
- Restrict external network access to Langflow instances through firewalls or virtual private clouds (VPCs).
- Place Langflow behind single sign-on (SSO) or other strong authentication mechanisms.
- Regularly update software and monitor for exposed endpoints using tools like Shodan or Censys.
The discovery of CVE-2025-3248 highlights the dangers of improper input handling in web applications, particularly those involving AI and automation tools.
While Langflow remains a powerful platform for building agentic AI systems, users must remain vigilant in securing deployments.
Organizations using Langflow should act swiftly to patch or secure their systems and prevent attackers from exploiting this critical vulnerability.
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