CVE-2026-93674

IBM Langflow OSS 1.0.0 through 1.12.2 could allow a remote attacker to execute arbitrary code due to improper control of code generation.

IBM Langflow OSS 1.0.0 through 1.10.0 contains a path traversal vulnerability in the APIRequest component, where filenames from Content-Disposition headers are not sanitized. An attacker can write arbitrary files to locations accessible by the Langflow process.

IBM Langflow OSS versions 1.0.0 through 1.10.0 allow unauthenticated attackers to create unlimited user accounts. When NEW_USER_IS_ACTIVE=true is set, new accounts are immediately active and can access RCE endpoints.

IBM Langflow OSS versions 1.0.0 through 1.10.2 may allow a remote authenticated attacker to change the password of an account due to improper authentication.

IBM Langflow OSS versions 1.0.0 through 1.11.2 could allow a remote authenticated attacker to obtain sensitive information due to improper validation of symbolic links. An attacker could exploit this flaw to read files they should not have access to.

IBM Langflow OSS versions 1.0.0 through 1.11.2 could allow a remote authenticated attacker to obtain sensitive information due to server-side request forgery. An attacker can send requests to internal network resources, potentially leading to data disclosure.

IBM Langflow OSS versions 1.0.0 through 1.11.2 could allow a remote authenticated attacker to obtain sensitive information due to path traversal. An attacker can manipulate file paths to access files outside the allowed directory.

IBM Langflow OSS contains a weakness in the ensure_fernet_key() function related to weak cryptographic key derivation.

IBM Langflow OSS versions 1.0.0 through 1.10.3 allow an authenticated attacker to execute arbitrary code due to a cryptographic weakness in the custom component validation mechanism. Validation uses a truncated SHA-256 hash, enabling collisions and bypassing validation, leading to execution of unauthorized Python code.

IBM Langflow OSS versions 1.0.0 through 1.10.3 allow an authenticated attacker to execute unintended code during Agentic Assistant validation due to improper handling of LLM-generated components. The application executes model-generated Python code in the backend before user approval, which may lead to side effects such as network access, file system interaction, or data exfiltration.