Critical Langflow Flaw Enables Malicious Code Injection – Technical Breakdown Released

A critical remote code execution (RCE) vulnerability, identified as CVE-2025-3248 with a CVSS score of 9.8, has been uncovered in Langflow, an open-source platform widely used for visually designing AI-driven agents and workflows.

This flaw, residing in the platform’s /api/v1/validate/code endpoint, poses a significant risk to organizations leveraging Langflow in their AI development ecosystems.

The vulnerability stems from the improper use of Python’s exec() function to process user-supplied code without authentication or sandboxing, enabling attackers to execute arbitrary commands on the server hosting the application.

This alarming security gap affects all Langflow versions prior to 1.3.0, leaving countless deployments exposed to potential exploitation.

Technical Details of the Exploitation Process

The root of CVE-2025-3248 lies in how Langflow handles code validation at the affected endpoint.

In vulnerable versions, the platform parses user-submitted code into an Abstract Syntax Tree (AST) using ast.parse(), imports specified modules, and executes function definitions to validate their structure via Python’s compile and exec() functions.

The critical issue emerges due to Python’s behavior during function definition parsing, where decorators and default argument values are evaluated immediately.

Attackers can craft malicious payloads within these elements such as embedding harmful logic in decorators or default arguments that get executed during AST processing.

For instance, a decorator like @exec(“import os; os.system(‘id > /tmp/pwned’)”) or a default argument with malicious intent can trigger unauthorized actions on the server.

By submitting such payloads through a simple POST request to the /api/v1/validate/code endpoint, an attacker can achieve unauthenticated RCE, potentially writing files, deploying web shells, or exfiltrating sensitive data, all while the server’s response appears innocuous.

The attack chain begins with identifying a vulnerable Langflow instance, either publicly accessible or within an internal network via compromised credentials.

Once targeted, the attacker sends crafted JSON payloads to the endpoint, embedding malicious code that executes during validation.

This unrestricted execution environment, devoid of authentication barriers in versions prior to 1.3.0, amplifies the threat, as there are no safeguards to prevent or isolate untrusted code from interacting with the server’s resources.

To mitigate this severe risk, immediate action is imperative. Users must upgrade to Langflow 1.3.0 or later, where the vulnerable endpoint now enforces authentication.

Additionally, organizations should restrict access to Langflow instances by deploying them behind Zero Trust Network Access (ZTNA) architectures, such as Zscaler Private Access (ZPA) with AppProtection.

According to the Report, Implementing robust input sandboxing to avoid direct use of exec() on untrusted code, and setting up monitoring for anomalous requests or unexpected outbound connections, are also critical steps to safeguard systems.

This vulnerability underscores the inherent dangers of processing dynamic code without stringent security controls, especially in internet-exposed applications.

CVE-2025-3248 serves as a stark reminder for developers and organizations to prioritize secure code validation practices and adopt proactive defense mechanisms to protect their AI-driven workflows from such catastrophic flaws.

Zscaler ThreatLabz urges all affected users to act swiftly on the outlined recommendations to prevent potential exploitation and secure their environments against this critical threat.

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