CVE-2026-19300 in Langflow OSS
Summary
by MITRE • 09/04/2026
IBM Langflow OSS 1.0.0 through 1.11.2 could allow a remote attacker to obtain sensitive information due to incomplete scrubbing of sensitive credential fields.
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Analysis
by VulDB Data Team • 09/04/2026
The vulnerability identified in IBM Langflow versions 1.0.0 through 1.11.2 represents a critical data exposure risk stemming from insufficient sanitization protocols within the application's user interface and backend processing logic. As an open-source framework designed for building large language model applications, Langflow handles numerous sensitive configuration parameters, including API keys, database credentials, and authentication tokens provided by developers during workflow construction. The core technical flaw lies in the incomplete scrubbing of these credential fields when data is serialized or displayed within the application's interface. Instead of masking or completely removing sensitive values from internal representations that might be exposed through error logs, debug modes, or API responses, the system retains plaintext or partially visible versions of these secrets under specific conditions. This failure to properly redact sensitive information allows any entity with access to the Langflow instance, whether authenticated users with lower privileges or potentially remote attackers exploiting other vectors, to extract high-value credentials that were intended to remain confidential.
From an operational perspective, this vulnerability significantly expands the attack surface for organizations deploying IBM Langflow in production environments. The exposure of API keys and authentication tokens can lead directly to unauthorized access against downstream services such as cloud providers, external APIs, or internal databases connected via LLM integrations. Attackers leveraging these stolen credentials may perform actions ranging from data exfiltration and service abuse to further lateral movement within the network infrastructure. This aligns with common exploitation patterns where initial footholds are gained through misconfigured applications leading to privilege escalation or resource compromise in dependent systems. The impact is compounded by the fact that Langflow is often used as a central orchestration layer, meaning compromised credentials could provide access to multiple critical data stores and services simultaneously, amplifying the potential damage beyond the immediate application scope.
This issue corresponds closely with CWE-200, which classifies exposure of sensitive information to an unauthorized actor due to improper handling or disclosure mechanisms. Additionally, it relates to CWE-798, involving the use of hardcoded credentials or default passwords that are not properly secured during runtime operations. In terms of offensive security frameworks, this vulnerability facilitates techniques associated with ATT&CK T1530, specifically Data from Cloud Storage Object Dumps if cloud-based secrets are exposed, and potentially T1078 for Valid Accounts if the leaked credentials allow authentication into other systems. The root cause is a design flaw in how sensitive data fields are processed during serialization or UI rendering, where validation checks fail to enforce strict masking rules before output generation.
To mitigate this vulnerability, organizations must immediately upgrade IBM Langflow to version 1.12.0 or later, which addresses the incomplete scrubbing logic by implementing robust sanitization routines for all credential-related inputs and outputs. Until an update is applied, administrators should restrict network access to the Langflow instance using firewalls or reverse proxies that limit exposure to trusted IP ranges only. It is also critical to rotate any credentials that may have been exposed through previous instances of the vulnerable software, assuming they were accessible via logs, browser history, or intercepted traffic. Implementing strict input validation and output encoding policies within custom integrations can further reduce risk by ensuring that sensitive data is never passed in plaintext formats even if underlying framework vulnerabilities persist. Regular auditing of application logs for unexpected credential patterns should also be conducted to detect potential exploitation attempts early.