CVE-2026-8476 in Langflow
Summary
by MITRE • 07/17/2026
IBM Langflow OSS 1.0.0 through 1.10.0 contain a critical remote code execution vulnerability in the disk-based caching mechanism. The AsyncDiskCache class uses Python's unsafe pickle.loads() function to deserialize cached objects from disk without validation, integrity verification, or authentication, enabling arbitrary code execution when malicious pickle payloads are processed. Attackers who can influence cached data through file system access, malicious workflow inputs, custom components, or API manipulation can achieve complete system compromise with the privileges of the Langflow server process.
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Analysis
by VulDB Data Team • 07/17/2026
This vulnerability exists within IBM Langflow Open Source Software versions 1.0.0 through 1.10.0 where the AsyncDiskCache class implements a dangerous deserialization pattern that directly invokes Python's pickle.loads() function without proper validation mechanisms. The flaw represents a critical security weakness that enables remote code execution when maliciously crafted pickle payloads are processed by the application. This vulnerability is categorized under CWE-502 as "Deserialization of Untrusted Data" and aligns with ATT&CK technique T1203 "Exploitation for Client Execution" within the context of remote code execution attacks.
The technical implementation of this vulnerability stems from the unsafe handling of serialized data within the disk-based caching system. When cached objects are retrieved from disk storage, the AsyncDiskCache class blindly deserializes these objects using pickle.loads() which executes arbitrary Python bytecode during the deserialization process. This pattern lacks any form of input validation, integrity checks, or authentication mechanisms that would normally protect against malicious payload injection. The vulnerability is exacerbated by the fact that the cache mechanism can be influenced through multiple attack vectors including file system access, manipulation of workflow inputs, development of custom components, and API endpoint exploitation.
The operational impact of this vulnerability is severe as it allows attackers with minimal privileges to achieve complete system compromise when they can influence cached data. The privilege level of the compromised system corresponds directly to the privileges of the Langflow server process which typically runs with elevated permissions to perform its core functions. This means that successful exploitation could result in full administrative control over the affected system, enabling attackers to install persistent backdoors, exfiltrate sensitive data, or establish further footholds within the network infrastructure. The remote code execution capability eliminates the need for additional attack vectors and provides direct access to execute arbitrary commands on the target system.
Mitigation strategies should focus on eliminating the unsafe pickle deserialization patterns entirely by replacing the AsyncDiskCache implementation with secure alternatives that do not rely on Python's pickle module for data persistence. Organizations should implement proper input validation and sanitization mechanisms for all cached data, utilize secure serialization formats such as JSON or XML instead of binary pickle data, and ensure that any caching mechanism includes integrity verification and authentication checks. Additionally, the system should be configured with least privilege principles where the Langflow server process operates with minimal required permissions to reduce potential impact from successful exploitation. Network segmentation and monitoring should also be implemented to detect anomalous behavior patterns associated with cache manipulation activities.