CVE-2026-81265 in Langflow OSS
Summary
by MITRE • 09/10/2026
IBM Langflow OSS 1.0.0 through 1.11.5.
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Analysis
by VulDB Data Team • 09/10/2026
The vulnerability identified in IBM Langflow versions ranging from 1.0.0 to 1.11.5 represents a critical security flaw within the open-source large language model application development framework. Langflow is designed to facilitate the creation of complex AI workflows by allowing developers to visually connect various components, such as data sources, LLMs, and tools. The specific issue lies in how the platform handles user input during the construction and execution of these flows. Due to insufficient validation or sanitization mechanisms within certain endpoints or component handlers, an attacker can inject malicious payloads that are processed by the backend server without adequate scrutiny. This flaw effectively allows for unauthorized code execution on the host system running the Langflow instance, provided the attacker has access to a vulnerable endpoint or can trick a user into executing a crafted flow.
From a technical perspective, this vulnerability is classified under CWE-94 as Improper Control of Generation of Code (Code Injection). The root cause typically stems from the framework's dynamic nature, where components are instantiated and executed based on JSON configurations provided by users. If these configurations contain executable code snippets or if string interpolation occurs without proper escaping before being passed to underlying system commands or interpreters, an injection vector is created. In many LLM frameworks, this often manifests through prompt injection techniques that escalate into server-side command execution when the framework attempts to process external data sources or execute custom Python scripts embedded within the flow definition. The lack of strict sandboxing for user-defined components exacerbates this risk, as malicious code can bypass intended isolation boundaries and interact directly with the operating system environment.
The operational impact of exploiting this vulnerability is severe, potentially leading to full compromise of the underlying infrastructure. An attacker who successfully exploits this flaw could execute arbitrary commands on the server hosting Langflow. This capability allows for data exfiltration, where sensitive information processed by the LLM workflows, such as proprietary business logic or personal identifiable information, can be stolen. Furthermore, the compromised system can serve as a pivot point for lateral movement within an internal network, enabling attackers to target other critical assets. In cloud environments, this could lead to unauthorized access to attached storage volumes, database credentials, or metadata services, significantly expanding the blast radius of the initial breach. The availability of such exploits in public repositories increases the likelihood of automated attacks against exposed Langflow instances.
To mitigate these risks, organizations must implement strict network segmentation and ensure that Langflow instances are not directly accessible from untrusted networks without robust authentication and authorization controls. It is imperative to upgrade immediately to a patched version beyond 1.11.5 where these input validation issues have been addressed by the IBM security team. Additionally, administrators should enforce least-privilege principles for the user account running the Langflow service, ensuring it does not possess administrative privileges on the host system. Implementing Web Application Firewalls with rules specifically tuned to detect code injection patterns can provide an additional layer of defense. For environments requiring custom component execution, enabling strict sandboxing mechanisms and reviewing all third-party integrations are essential steps to reduce the attack surface associated with dynamic code generation features inherent in LLM application frameworks.