CVE-2026-104335 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 access control.
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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 insufficiently enforced access controls within the application's architecture. This issue specifically affects authenticated users, meaning that an attacker must first possess valid credentials to exploit this weakness. The core of the problem lies in how the system validates permissions for specific operations or resources. While basic authentication is correctly implemented to verify user identity, the subsequent authorization checks fail to properly restrict access to sensitive functions or data structures. This gap allows a malicious actor who has successfully logged into the application to perform actions that are intended only for higher-privileged roles or restricted contexts. The flaw essentially bypasses the principle of least privilege by allowing lower-level authenticated entities to interact with components they should not have permission to modify or execute, thereby escalating their effective privileges within the application environment without requiring additional authentication steps.
From a technical perspective, this vulnerability is classified under CWE-269, which denotes Improper Privilege Control. The mechanism of exploitation typically involves manipulating API requests or interface interactions that are supposed to be gated by role-based access control lists. Because Langflow is an open-source framework often used for building and deploying large language model applications, the implications of such a flaw extend beyond simple data theft. An attacker can leverage this improper access control to inject malicious payloads into the workflow definitions or execution pipelines managed by the platform. Since these workflows are executed within the context of the application server, successful exploitation leads directly to Remote Code Execution. This means that once an authenticated user triggers the vulnerable function with crafted input, arbitrary code runs on the host system where Langflow is deployed. The attacker gains a foothold in the backend infrastructure, potentially allowing them to install backdoors, exfiltrate sensitive data processed by the LLMs, or use the compromised server as a pivot point for further network attacks.
The operational impact of this vulnerability is severe due to its potential for widespread compromise within enterprise environments utilizing Langflow for AI-driven automation. If an attacker achieves remote code execution, they can disrupt business continuity by taking down critical services or altering the logic of automated workflows that drive key operations. Furthermore, because LLM applications often handle sensitive proprietary information and customer data, a successful exploit could lead to significant confidentiality breaches. The attacker might extract training datasets, prompt engineering strategies, or personally identifiable information processed through the application. In addition to direct damage, this vulnerability poses a risk to supply chain integrity if Langflow is used in CI/CD pipelines for AI model deployment, as compromised builds could propagate malicious code downstream.
To mitigate this risk, organizations running IBM Langfall versions 1.0.0 through 1.12.2 must immediately upgrade to the latest patched version where these access control checks have been hardened and validated against edge cases involving role transitions and resource ownership. In addition to patching, it is advisable to implement strict network segmentation policies that limit direct internet exposure of Langflow instances, ensuring they are only accessible through secure internal networks or via a robust identity-aware proxy with multi-factor authentication enabled. Security teams should also audit existing user roles and permissions within the application to ensure that administrative functions are restricted exclusively to trusted personnel. Monitoring logs for unusual patterns in API calls from authenticated users can help detect potential exploitation attempts before full compromise occurs, aligning detection strategies with MITRE ATT&CK techniques related to privilege escalation and unauthorized access.