Preface: AI models do not use Langflow to generate or write code for you. When a large language model (like ChatGPT, Claude, Gemini, or specialized coding assistants) writes code in response to your prompts, it uses its own internal neural network, parameters, and training data.
The relationship between AI and Langflow is actually the exact opposite: human developers use Langflow to build, connect, and manage AI models.
Background: Langflow is an open-source, visual low-code framework specifically built to design, prototype, and deploy Artificial Intelligence (AI) workflows, multi-agent systems, and Retrieval-Augmented Generation (RAG) applications. It functions as a visual orchestration layer that abstracts complex Python AI code into drag-and-drop components.
Langflow features an embedded AI sidekick called the Langflow Assistant. The coolest part about this feature is its “inception-style” architecture: the Langflow Assistant is actually powered by a hidden Langflow graph running behind the scenes on your local server. When you ask it a question or give it a command, it runs an internal AI flow to alter or build the external AI flow you are working on.
When developers use Langflow, the strongest and most effective type of coding is Python-based integration, data orchestration, and AI pipeline customization.
Security Focus: Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to 1.9.2, by controlling a files that are digested into the RAG, an attacker can direct the node to read any file on the file-system by absolute path. All components based on BaseFileComponent are vulnerable to the vulnerability.
Ref: Controlling the files ingested into a Retrieval-Augmented Generation (RAG) pipeline means curating, filtering, and optimizing your source data before it is processed by the search and language models.
This process directly dictates the quality of your AI’s responses and prevents the system from “hallucinating” or wasting resources on irrelevant noise.
Affected Nodes on Your Canvas
Any flow using the following visual components prior to version 1.9.2 is vulnerable:
- Read File (FileComponent)
- Docling nodes (DoclingInlineComponent, DoclingRemoteComponent)
- NVIDIA Retriever Extraction (NvidiaIngestComponent)
- Video File (VideoFileComponent) Unstructured API (UnstructuredComponent)
Official announcement: Please refer to the link for details – https://www.tenable.com/cve/CVE-2026-55447