CVE-2026-104317 in Langflow OSS
Summary
by MITRE • 10/29/2026
IBM Langflow OSS 1.0.0 through 1.12.3 could allow a remote attacker who could reach the message broker to execute arbitrary code on the system due to the deserialization of untrusted data.
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Analysis
by VulDB Data Team • 10/29/2026
The vulnerability identified in IBM Langflow versions 1.0.0 through 1.12.3 represents a critical security flaw rooted in improper handling of serialized objects within the application's message broker integration. This issue specifically manifests as an insecure deserialization vulnerability, where the software processes data from external sources without sufficient validation or sanitization prior to reconstruction into executable object structures. In modern distributed architectures, message brokers such as RabbitMQ, Kafka, or Redis serve as central hubs for inter-service communication, often transmitting complex payloads that include metadata and state information. When an application accepts these payloads and directly deserializes them using unsafe methods, it creates a direct pathway for remote code execution if the payload contains maliciously crafted objects designed to exploit specific library functions during the reconstruction process.
The technical mechanism behind this flaw relies on the inherent trust placed in serialized data formats by many programming languages, particularly Python which is commonly used with Langflow. Deserialization libraries often allow arbitrary class instantiation and method invocation as part of their normal operation to restore object state. An attacker who gains access to the message broker network can intercept or inject messages containing specially crafted payloads that trigger dangerous functions during deserialization. These payloads typically exploit built-in modules or third-party dependencies that perform system-level operations, such as executing shell commands, reading sensitive files, or establishing reverse shells. Because the vulnerability occurs at the point of data ingestion from the broker, it bypasses many traditional web application firewalls and input validation layers that might otherwise protect against direct HTTP-based attacks.
From an operational perspective, this vulnerability poses a severe risk to system integrity and confidentiality. Successful exploitation allows a remote attacker who has network access to the message broker to execute arbitrary code with the privileges of the Langflow process. This can lead to complete compromise of the underlying host operating system, lateral movement within the internal network, and exfiltration of sensitive data processed by the language model application. The impact is exacerbated in production environments where message brokers are often accessible from multiple services, increasing the attack surface significantly if proper segmentation or authentication controls are not enforced between clients and the broker infrastructure.
This vulnerability aligns with CWE-502, which describes deserialization of untrusted data as a distinct category of weakness leading to arbitrary code execution. It also maps directly to MITRE ATT&CK technique T1648, specifically the LEVERAGE CONTROLLED EXECUTION ENVIRONMENT sub-technique, where attackers exploit trusted processes or services to execute malicious commands. The exploitation path typically involves crafting serialized payloads that target known vulnerable libraries within the Python ecosystem, such as pickle, yaml with unsafe loaders, or other serialization frameworks commonly integrated into AI and data processing pipelines like Langflow.
Mitigation strategies must focus on both immediate remediation and long-term architectural hardening. The primary recommendation is to upgrade IBM Langflow to version 1.12.4 or later, where the deserialization logic has been patched to validate input types and restrict unsafe object instantiation. In environments where upgrading is not immediately feasible, administrators should implement strict network segmentation to ensure that only authorized services can communicate with the message broker. Additionally, enabling authentication on the message broker itself adds a critical layer of defense by preventing unauthorized actors from injecting malicious payloads into the communication stream. Input validation techniques such as allowing lists for permitted classes during deserialization and avoiding dynamic class loading where possible should be adopted in custom integrations to further reduce the risk surface associated with data processing components.