CVE-2026-76059 in Langflow OSS
Summary
by MITRE • 09/11/2026
IBM Langflow OSS 1.0.0 through 1.11.5 An attacker who could submit custom component source code could bypass the static security scanner by crafting an annotated class-body assignment that resolved to a dangerous callable through alias tracking; the resolved value was never checked against the dangerous callable blocklist due to the logic error. If the crafted component reached the runtime execution path, the attacker could cause arbitrary operating system commands to execute on the server in-process, with the privileges of the running service.
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Analysis
by VulDB Data Team • 09/11/2026
The vulnerability identified in IBM Langflow versions 1.0.0 through 1.11.5 represents a critical security flaw rooted in insufficient input validation and flawed static analysis logic within the application's component processing pipeline. This issue specifically affects systems that allow users to submit custom Python components, which are then processed by an internal static security scanner designed to prevent the execution of dangerous operations. The core technical failure lies in how the scanner tracks variable aliases during its analysis phase. When a developer or attacker submits code containing an annotated class-body assignment, the scanner attempts to resolve the target callable associated with that assignment. However, due to a logic error in the alias tracking mechanism, the resolved value is not correctly matched against the predefined blocklist of dangerous callables. This oversight allows maliciously crafted payloads to slip past the static security checks undetected, effectively neutralizing one of the primary defense layers intended to protect the runtime environment from unauthorized code execution.
From a technical perspective, this flaw constitutes an Improper Neutralization of Input During Web Page Generation vulnerability, commonly categorized under CWE-79 in industry standards. The attacker exploits this weakness by crafting specific Python syntax that leverages aliasing techniques to obscure the true nature of the function being called. By assigning a dangerous callable to a variable through an annotated class body and then invoking it indirectly or via complex resolution paths, the static analyzer fails to recognize the threat because its pattern matching logic does not account for these advanced aliasing scenarios. Consequently, the malicious code is deemed safe by the scanner and proceeds to be loaded into the application's runtime environment. This type of vulnerability highlights a significant gap in static analysis tools when dealing with dynamic language features like Python annotations and complex object resolution mechanisms, where simple string matching or basic control flow analysis proves insufficient against sophisticated obfuscation techniques.
The operational impact of this vulnerability is severe, as it leads directly to Remote Code Execution (RCE) within the context of the running service. Once the crafted component bypasses the static scanner and enters the execution path, an attacker can trigger arbitrary operating system commands on the host server. Because Langflow typically runs with elevated privileges necessary for managing AI workflows and integrating with various data sources, this code execution occurs in-process with those same high-level permissions. This means that a successful exploitation could allow an attacker to read sensitive configuration files, exfiltrate proprietary model weights or training data, pivot further into the internal network, or compromise other services running on the same infrastructure. The ability to execute commands as the service user significantly amplifies the risk profile, potentially leading to full system takeover if the service is configured with root or administrator privileges.
This vulnerability aligns closely with MITRE ATT&CK technique T1059 Command and Scripting Interpreter, specifically regarding the use of Python scripts for execution. It also reflects aspects of T1190 Exploit Public-Facing Application, as it targets a web-based interface where users can upload or define custom components. The attack vector relies on social engineering or unauthorized access to the component submission feature, making authentication status a critical factor in determining exploitability. If the application is exposed to unauthenticated users, the risk is immediate and high; if restricted to authenticated users, it remains a severe insider threat or compromised account scenario.
Mitigation strategies must address both the immediate technical flaw and broader security hygiene practices. The primary remediation involves upgrading IBM Langflow to version 1.12.0 or later, where this logic error in the static scanner has been corrected to properly resolve aliased callables against the dangerous blocklist. In environments where an upgrade is not immediately feasible, administrators should restrict access to the component submission feature strictly to trusted users with verified identities and minimal necessary privileges. Implementing a robust allow-list approach for custom components, rather than relying solely on static analysis of submitted code, can provide additional defense in depth. Furthermore, running the Langflow service under a dedicated low-privilege user account with limited file system and network access reduces the blast radius should an exploitation attempt succeed. Regular auditing of uploaded components and monitoring for unusual process creation events on the host server are also recommended to detect potential abuse early.