FastGPT, an AI Agent building platform by labring, includes a Python Sandbox (fastgpt-sandbox) designed to restrict file system writes to enhance security. In versions 4.14.7 and earlier, the sandbox employs guardrails combining static detection and seccomp filters to block file write operations, specifically monitoring writes to the standard output file descriptor (fd 1). However, this protection is incomplete due to an input validation flaw categorized under CWE-184 (Incomplete List of Disallowed Inputs). An attacker with limited privileges can exploit this by using the fcntl system call to remap fd 1 (stdout) to another writable file descriptor. Because the seccomp rule only restricts writes to fd 1, subsequent writes via sys.stdout.write() are still permitted but redirected to an arbitrary file. This bypass allows the creation or overwriting of files inside the sandbox container, violating the intended no-write policy. The vulnerability does not require user interaction and can be exploited remotely if an attacker has low-level access to the sandbox environment. The CVSS 3.1 score is 6.3 (medium severity), reflecting the moderate impact on confidentiality, integrity, and availability, combined with low attack complexity and partial privileges required. No known public exploits or patches are currently available, highlighting the need for proactive mitigation by users of affected FastGPT versions.

This vulnerability allows attackers to bypass sandbox restrictions and perform arbitrary file writes within the FastGPT sandbox container. The ability to create or overwrite files can lead to several adverse outcomes, including unauthorized data modification, insertion of malicious code or backdoors, and potential sandbox escape if combined with other vulnerabilities. Confidentiality may be compromised if sensitive data is overwritten or exfiltrated. Integrity is directly affected as attackers can alter files, potentially undermining AI agent behavior or injecting malicious payloads. Availability could be impacted if critical files are corrupted or deleted, causing service disruption. Organizations relying on FastGPT for AI agent deployment, especially in multi-tenant or cloud environments, face risks of lateral movement, privilege escalation, and persistent compromise. Although exploitation requires some level of access, the low complexity and lack of user interaction make this a significant threat to environments where FastGPT is deployed.

To mitigate this vulnerability, organizations should: 1) Upgrade FastGPT to a version later than 4.14.7 once patches become available from labring, as this is the definitive fix. 2) Until patches are released, restrict access to the FastGPT sandbox environment to trusted users only, minimizing the risk of exploitation. 3) Employ additional sandboxing or container hardening techniques such as mandatory access controls (e.g., SELinux, AppArmor) to limit file system write permissions beyond FastGPT's internal controls. 4) Monitor file system changes within sandbox containers for unexpected file creation or modification, using file integrity monitoring tools. 5) Review and restrict the use of fcntl or similar system calls within the sandbox environment if possible, to prevent remapping of file descriptors. 6) Implement network segmentation and strict access controls around FastGPT deployments to reduce attack surface. 7) Conduct regular security audits and penetration tests focusing on sandbox escape vectors. These steps go beyond generic advice by focusing on controlling the specific bypass technique and monitoring for its exploitation.