CVE-2026-107327 in Langflow OSS
Summary
by MITRE • 10/29/2026
IBM Langflow OSS 1.0.0 through 1.12.4 could allow a remote authenticated attacker to execute arbitrary code due to missing authorization.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 10/29/2026
The vulnerability identified in IBM Langflow versions 1.0.0 through 1.12.4 represents a critical security flaw rooted in insufficient access control mechanisms within the application's architecture. As an open-source framework designed for building and deploying large language model applications, Langflow handles sensitive data flows and potentially executes complex logic chains defined by users. The core issue lies in the failure to enforce proper authorization checks on specific endpoints or functions that allow code execution or configuration modification. This absence of robust authentication verification allows a remote attacker who has already obtained valid credentials for an account within the system to bypass intended security boundaries. Unlike unauthenticated attacks, this vulnerability requires initial access, but it significantly lowers the barrier for exploitation because many organizations may assign broad permissions to service accounts or internal users under the assumption that network perimeter defenses are sufficient protection against lateral movement and privilege escalation.
From a technical perspective, the flaw constitutes an Insecure Direct Object Reference (IDOR) or Broken Access Control scenario where the application fails to verify whether the authenticated user has the specific privileges required for the requested action. When a request is made to execute arbitrary code or modify critical system parameters, the backend processes this input without validating that the session token corresponds to a role with administrative or elevated execution rights. This oversight enables an attacker who possesses low-privilege credentials to escalate their influence within the application environment. By crafting specific HTTP requests targeting these unprotected endpoints, the attacker can inject malicious payloads into the code execution pipeline of Langflow. Since Langflow is often used in production environments for AI-driven workflows, this capability transforms a standard user account into a potent vector for system compromise, allowing the injection of scripts that interact with underlying operating systems or connected databases.
The operational impact of this vulnerability extends beyond simple data exfiltration to include complete control over the host infrastructure running Langflow. An attacker leveraging this flaw can achieve Remote Code Execution (RCE), which is among the most severe consequences in cybersecurity due to its potential for total system compromise. Once arbitrary code is executed, the attacker can install backdoors, pivot to other systems on the internal network, exfiltrate proprietary data processed by the LLM applications, or disrupt service availability through denial-of-service attacks. In enterprise settings where Langflow integrates with sensitive business logic and customer data pipelines, this breach could lead to significant regulatory violations under frameworks such as GDPR or HIPAA if protected health information or personally identifiable information is accessed. Furthermore, because AI models often process confidential corporate strategies or intellectual property, the integrity of these assets is severely compromised when an unauthorized party can manipulate the execution environment.
To mitigate this risk, organizations must immediately upgrade to a patched version of IBM Langflow that addresses the authorization logic gaps in the affected codebase. It is essential to verify that all components within the deployment pipeline are updated consistently to prevent partial exposure due to dependency mismatches. In addition to patching, implementing strict role-based access control (RBAC) policies ensures that only designated administrators can invoke functions related to code execution or system configuration. Security teams should also audit existing user accounts to ensure they adhere to the principle of least privilege, removing unnecessary elevated permissions from standard users and service accounts. Network segmentation strategies should be reviewed to isolate Langflow instances from critical internal resources, limiting the blast radius in case a breach occurs. Continuous monitoring for anomalous API calls indicative of unauthorized access attempts can provide early detection signals while these remediation steps are being implemented.
This vulnerability aligns with CWE-269 Improper Privilege Management and falls under MITRE ATT&CK technique T1078 Valid Accounts, where attackers leverage legitimate credentials to gain initial foothold or escalate privileges within a system. Understanding the context of Langflow as an AI orchestration tool highlights why such access control failures are particularly dangerous; they undermine trust in automated decision-making processes and expose downstream systems to manipulation via injected prompts or code artifacts. By treating authentication not just as a gatekeeping mechanism but as a continuous validation process for every sensitive operation, organizations can significantly reduce their attack surface against insider threats and compromised credentials that exploit these types of authorization flaws.