CVE-2026-93443 in Langflow OSS
Summary
by MITRE • 10/07/2026
IBM Langflow OSS 1.0.0 through 1.12.2 could allow a remote authenticated attacker to execute arbitrary code due to improper neutralization of special elements used in code.
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Analysis
by VulDB Data Team • 10/07/2026
The vulnerability identified in IBM Langflow versions 1.0.0 through 1.12.2 represents a critical security flaw rooted in the application's handling of user-supplied input within its computational pipeline. As an open-source framework designed for building and deploying large language model applications, Langflow allows users to construct complex workflows by chaining various components together. The specific weakness lies in how these workflows process data that is intended to be executed as code or script snippets. When a remote authenticated attacker provides specially crafted input containing malicious special elements, the application fails to properly neutralize or sanitize these inputs before execution. This failure results in an improper neutralization of special elements used for code, commonly referred to as Code Injection. The authentication requirement indicates that while the vulnerability is not open to unauthenticated internet-wide exploitation, it poses a severe risk within environments where legitimate users have access to create and modify workflows, potentially including internal teams or partners who interact with the platform.
From a technical perspective, this flaw allows an attacker to inject arbitrary commands into the execution context of the Langflow application. By exploiting the lack of proper input validation and sanitization mechanisms, the attacker can manipulate the flow of data such that malicious code is interpreted as legitimate instructions by the underlying runtime environment. This capability effectively bypasses intended security boundaries because the application trusts inputs from authenticated users without sufficiently verifying their safety or structure. The impact of this vulnerability extends beyond simple data leakage; it grants the attacker the ability to execute arbitrary system commands on the host machine running Langflow. Depending on the privileges under which the Langflow service operates, this could lead to complete compromise of the underlying infrastructure, including access to sensitive files, modification of application configurations, or use of the compromised server as a pivot point for further attacks against other systems within the network.
The operational impact of this vulnerability is significant due to its potential for remote code execution with authenticated access. In enterprise environments where Langflow might be deployed on-premises or in private clouds, an insider threat or a compromised user account could leverage this flaw to gain unauthorized control over critical AI infrastructure. This undermines the integrity and availability of machine learning models and data processing pipelines managed by the organization. Furthermore, if the application is configured with elevated privileges for ease of development or deployment, the consequences are exacerbated, potentially allowing attackers to install backdoors, exfiltrate proprietary model weights, or disrupt service continuity through denial-of-service attacks initiated via resource exhaustion from malicious code execution. The presence of this vulnerability highlights a gap in secure coding practices regarding dynamic code evaluation and input handling within complex application frameworks.
To mitigate the risks associated with CVE details pertaining to improper neutralization of special elements used for code, immediate remediation is required by upgrading IBM Langflow to version 1.12.3 or later, where these issues have been addressed through enhanced input validation and sanitization protocols. Organizations should also implement strict least-privilege principles when deploying the application, ensuring that the service account running Langflow has minimal permissions necessary for operation, thereby limiting the potential impact of a successful exploitation attempt. Additionally, implementing network segmentation to isolate AI development environments from critical production systems can reduce the blast radius in case of a breach. Security teams should also monitor execution logs for anomalous command patterns and consider deploying Web Application Firewalls configured to detect common code injection signatures as an additional layer of defense while patching is being applied. Regular security assessments and static application security testing focused on input handling mechanisms are recommended to prevent similar vulnerabilities from persisting in future updates or custom extensions developed by the organization.