CVE-2026-101331 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 obtain sensitive information due to insufficiently protected credentials.
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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 inadequate protection of user credentials within the application's architecture. As an open-source framework designed for building and deploying large language model applications, Langflow handles sensitive authentication data necessary to interact with various external services and internal components. The core issue lies in how these credentials are stored or transmitted during normal operation, allowing a remote authenticated attacker who has already gained access to the system to extract this information. This scenario highlights a failure in implementing robust security controls for secret management, which is fundamental to maintaining the integrity of any software application that processes user data and API keys.
From a technical perspective, insufficiently protected credentials typically manifest as plaintext storage in configuration files or databases, weak encryption algorithms with exposed keys, or improper handling of session tokens that can be intercepted or replayed by an attacker who has obtained valid login credentials. In the context of Langflow, this flaw enables an adversary to leverage their existing authenticated access to pivot further into the network environment. By extracting these sensitive details, such as API keys for cloud services or database connection strings, the attacker gains the ability to impersonate legitimate users or services, potentially leading to unauthorized data access, modification, or deletion of resources associated with those credentials.
The operational impact of this vulnerability is significant because it undermines the principle of least privilege and compromises the confidentiality aspect of the CIA triad. An authenticated attacker does not need complex exploitation techniques beyond basic credential harvesting; they simply require valid login privileges which are often easier to obtain through phishing, brute force attacks on weak passwords, or session hijacking. Once these credentials are obtained, the attacker can execute malicious actions that may result in substantial financial loss, reputational damage, and regulatory non-compliance for organizations relying on Langflow for their AI-driven workflows. The risk is exacerbated by the fact that many users configure multiple integrations within Langflow, meaning a single compromised credential could provide access to diverse external systems including cloud storage, email services, or proprietary data repositories.
This vulnerability aligns with CWE-798: Use of Hard-coded Credentials and CWE-256: Unprotected Storage of Credentials, indicating fundamental flaws in the application's security design regarding secret management practices. Furthermore, from a tactical standpoint related to the MITRE ATT&CK framework, this flaw facilitates techniques associated with Credential Access such as T1078: Valid Accounts or potentially T1552: Unsecured Credentials if the storage mechanism is particularly weak. The exploitation path allows for lateral movement and privilege escalation within the broader ecosystem where Langflow operates, making it a high-priority issue for remediation.
To mitigate this risk, immediate action should be taken to upgrade IBM Langflow to version 1.12.3 or later, which addresses these specific credential protection issues by implementing stronger encryption standards and secure storage mechanisms such as environment variable injection with restricted access permissions rather than hard-coded values in source code or configuration files. Organizations currently running affected versions must also rotate all credentials that were potentially exposed during the period of vulnerability existence. It is recommended to implement a secrets management solution like HashiCorp Vault or AWS Secrets Manager to handle sensitive data dynamically, ensuring that keys are never stored persistently in plaintext within the application's runtime environment. Additionally, enforcing multi-factor authentication and monitoring for anomalous access patterns can provide an additional layer of defense against attackers attempting to exploit this weakness even if they manage to obtain valid credentials through other means.