EXECUTIVE SUMMARY:
CVE-2026-7524 with a CVSS score of 9.8 is a critical arbitrary file read vulnerability in the open‑source Langflow artificial‑intelligence framework, affecting versions 1.0.0 through 1.9.1. The flaw resides in the core library’s _unpack_bundle extraction routine used by modules such as Docling, Docling Serve, and the Unstructured API; when a tar archive containing crafted symbolic links is uploaded, the extractor follows the symlinks and writes the target files into the application’s internal vector database without validation. An attacker with only the ability to upload a malicious archive—typically a low‑privilege user of a RAG chatbot interface—can trigger the extraction, causing the system to read any file the process can access, including private JWT secret keys or configuration files. By obtaining these secrets, the adversary can forge authentication tokens, bypass security controls, and ultimately execute arbitrary Python code via the interpreter node, achieving full system compromise. The business impact includes exposure of confidential credentials, unauthorized data access, and potential ransomware or data‑exfiltration events, especially in environments that rely on Langflow for confidential AI workflows. Exploitation requires that the vulnerable Langflow instance processes user‑supplied archives without sanitizing symlinks and that the attacker can supply such an archive.[/subscribe_to_unlock_form]
EXECUTIVE SUMMARY:
CVE-2026-7524 with a CVSS score of 9.8 is a critical arbitrary file read vulnerability in the open‑source Langflow artificial‑intelligence framework, affecting versions 1.0.0 through 1.9.1. The flaw resides in the core library’s _unpack_bundle extraction routine used by modules such as Docling, Docling Serve, and the Unstructured API; when a tar archive containing crafted symbolic links is uploaded, the extractor follows the symlinks and writes the target files into the application’s internal vector database without validation. An attacker with only the ability to upload a malicious archive—typically a low‑privilege user of a RAG chatbot interface—can trigger the extraction, causing the system to read any file the process can access, including private JWT secret keys or configuration files. By obtaining these secrets, the adversary can forge authentication tokens, bypass security controls, and ultimately execute arbitrary Python code via the interpreter node, achieving full system compromise. The business impact includes exposure of confidential credentials, unauthorized data access, and potential ransomware or data‑exfiltration events, especially in environments that rely on Langflow for confidential AI workflows. Exploitation requires that the vulnerable Langflow instance processes user‑supplied archives without sanitizing symlinks and that the attacker can supply such an archive.[emaillocker id="1283"]
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REFERENCES:
The following reports contain further technical details:
https://securityonline.info/langflow-oss-vulnerability-cve-2026-7524/