CVE-2026-0769: Langflow eval_custom_component_code RCE Flaw

CVE-2026-0769 Overview

CVE-2026-0769 is a critical eval injection vulnerability in Langflow's eval_custom_component_code function that enables remote code execution. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Langflow without requiring authentication.

The specific flaw exists within the implementation of the eval_custom_component_code function, where the lack of proper validation of user-supplied strings before using them to execute Python code creates a dangerous attack surface. An attacker can leverage this vulnerability to execute code in the context of the current process.

Critical Impact

Unauthenticated remote attackers can achieve arbitrary code execution on vulnerable Langflow installations, potentially leading to complete system compromise, data theft, or lateral movement within the network.

Affected Products

• Langflow (specific versions not disclosed in advisory)

Discovery Timeline

• 2026-01-23 - CVE CVE-2026-0769 published to NVD
• 2026-01-26 - Last updated in NVD database

Technical Details for CVE-2026-0769

Vulnerability Analysis

This vulnerability is classified as CWE-95 (Improper Neutralization of Directives in Dynamically Evaluated Code), commonly known as Eval Injection. The flaw resides in Langflow's custom component code evaluation functionality, which accepts user-controlled input and passes it directly to Python's eval() or similar code execution functions without adequate sanitization or validation.

Langflow, being an AI workflow orchestration platform, allows users to create custom components through code. The eval_custom_component_code function processes this user-provided code, but fails to implement proper input validation or sandboxing mechanisms. This architectural weakness allows attackers to inject malicious Python code that executes with the privileges of the Langflow process.

The vulnerability requires no authentication to exploit, meaning any network-accessible Langflow instance is at risk. Given the network-based attack vector with no user interaction required, exploitation is straightforward for attackers who can reach the vulnerable endpoint.

Root Cause

The root cause is the direct use of Python's dynamic code evaluation capabilities (eval(), exec(), or similar functions) on user-supplied input without implementing proper input validation, sanitization, or sandboxing. The eval_custom_component_code function trusts that incoming code strings are benign, creating a direct path for code injection attacks.

Attack Vector

The attack is network-based and requires no authentication or user interaction. An attacker can craft a malicious request containing Python code payloads targeting the eval_custom_component_code function endpoint. When processed, the injected code executes within the context of the Langflow process, granting the attacker the ability to:

• Execute arbitrary system commands
• Read, modify, or delete sensitive data
• Establish persistent backdoor access
• Pivot to other systems on the network
• Exfiltrate confidential information including API keys and credentials

The vulnerability was reported through the Zero Day Initiative as ZDI-CAN-26972 and assigned advisory ZDI-26-035.

Detection Methods for CVE-2026-0769

Indicators of Compromise

• Unexpected outbound network connections from Langflow processes to external IP addresses
• Unusual process spawning or child processes initiated by the Langflow application
• Suspicious Python code patterns in web server logs targeting component creation endpoints
• Evidence of unauthorized file access or modification in Langflow's working directories

Detection Strategies

• Monitor HTTP requests to Langflow endpoints for payloads containing Python code injection patterns such as __import__, os.system, subprocess, or eval calls
• Implement web application firewall (WAF) rules to detect and block common Python injection patterns
• Analyze application logs for requests to custom component endpoints with abnormal payload sizes or content
• Deploy behavioral analysis to detect unusual process execution chains originating from Langflow

Monitoring Recommendations

• Enable detailed logging for all Langflow API endpoints, particularly those handling code submission
• Implement network segmentation to limit the blast radius of potential compromises
• Monitor for DNS queries or network connections from Langflow instances to known malicious infrastructure
• Set up alerts for any process execution or file system changes initiated by Langflow outside normal operational parameters

How to Mitigate CVE-2026-0769

Immediate Actions Required

• Restrict network access to Langflow instances to trusted IP ranges only until patches are applied
• Implement authentication requirements for all Langflow endpoints if not already configured
• Deploy WAF rules to filter requests containing suspicious Python code patterns
• Consider temporarily disabling custom component functionality if operationally feasible

Patch Information

Refer to the Zero Day Initiative Advisory ZDI-26-035 for the latest patch information from the Langflow project. Organizations should monitor for vendor security updates and apply patches as soon as they become available.

Workarounds

• Place Langflow instances behind a reverse proxy with strict input filtering for code-related endpoints
• Implement network-level access controls to restrict which systems can communicate with Langflow
• Run Langflow in a containerized environment with minimal privileges and restricted system access
• Disable or remove the custom component functionality if not required for your use case