CVE-2025-49131 is a medium-severity vulnerability affecting labring's FastGPT platform, specifically versions prior to 4.9.11. FastGPT is an open-source framework designed for building, deploying, and operating AI-driven workflows and conversational agents. A critical component of FastGPT is the fastgpt-sandbox, a containerized isolated environment intended to safely execute user-submitted or dynamically generated code. The vulnerability arises from insufficient sandbox isolation and overly permissive system call (syscall) allowances. Before version 4.9.11, the sandbox permitted a broad set of syscalls, which attackers could exploit to escape the sandbox boundaries. This escape enables unauthorized reading and overwriting of arbitrary files on the host system and bypassing Python module import restrictions, potentially leading to unauthorized code execution or data manipulation outside the sandbox. The root cause is an incorrect permission assignment for critical resources (CWE-732), where the sandbox fails to enforce strict syscall filtering and isolation policies. The issue was addressed in FastGPT version 4.9.11 by restricting allowed syscalls to a safer subset and improving error messaging to clarify restrictions. The CVSS v3.1 score is 6.3 (medium), reflecting network attack vector, low attack complexity, requiring low privileges but no user interaction, and impacting confidentiality, integrity, and availability to a limited extent. No known exploits are reported in the wild yet, but the vulnerability presents a significant risk given the sandbox's role in isolating potentially untrusted code execution.

For European organizations deploying FastGPT, especially those using versions prior to 4.9.11, this vulnerability poses a tangible risk of sandbox escape leading to unauthorized access and modification of sensitive files and data. Organizations leveraging FastGPT for AI workflows in sectors such as finance, healthcare, or critical infrastructure could face data breaches, intellectual property theft, or disruption of AI services. The ability to bypass Python module import restrictions may allow attackers to execute arbitrary or malicious code, potentially compromising the host system or lateral movement within the network. Given the increasing adoption of AI-driven automation and conversational agents in Europe, exploitation could undermine trust in AI platforms and cause regulatory compliance issues under GDPR if personal data is exposed. Although no active exploits are known, the ease of exploitation (low complexity, network accessible) and the critical role of sandboxing in securing AI workloads elevate the threat level. The impact extends beyond confidentiality to integrity and availability, as attackers could modify or delete files or disrupt AI service operations.

European organizations should immediately upgrade FastGPT to version 4.9.11 or later to benefit from the patched syscall restrictions and enhanced sandbox isolation. Beyond upgrading, organizations should implement strict network segmentation and access controls around FastGPT deployment environments to limit exposure. Employ runtime monitoring and anomaly detection focused on syscall patterns and container escape attempts to detect exploitation attempts early. Restrict privileges of FastGPT processes and sandbox containers using Linux security modules (e.g., SELinux, AppArmor) and container security best practices to minimize the blast radius if exploitation occurs. Conduct regular code audits and penetration testing on AI workflow environments to identify potential sandbox bypasses or misconfigurations. Additionally, organizations should maintain up-to-date incident response plans tailored to AI platform compromises and ensure logging of sandbox activity for forensic analysis. Finally, educating developers and operators on secure sandbox usage and the risks of permissive syscall policies will help prevent similar vulnerabilities.