CVE-2025-5120 in smolagents
Summary
by MITRE • 07/27/2025
A sandbox escape vulnerability was identified in huggingface/smolagents version 1.14.0, allowing attackers to bypass the restricted execution environment and achieve remote code execution (RCE). The vulnerability stems from the local_python_executor.py module, which inadequately restricts Python code execution despite employing static and dynamic checks. Attackers can exploit whitelisted modules and functions to execute arbitrary code, compromising the host system. This flaw undermines the core security boundary intended to isolate untrusted code, posing risks such as unauthorized code execution, data leakage, and potential integration-level compromise. The issue is resolved in version 1.17.0.
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Analysis
by VulDB Data Team • 08/07/2025
The sandbox escape vulnerability in huggingface/smolagents version 1.14.0 represents a critical failure in software security architecture that fundamentally undermines the intended isolation of untrusted code execution. This vulnerability exists within the local_python_executor.py module where the security model attempts to enforce restrictions through both static and dynamic code analysis checks. The flaw demonstrates a classic insufficient sandboxing approach that fails to properly contain execution boundaries, allowing attackers to bypass intended security controls and achieve full system compromise through remote code execution capabilities. The vulnerability specifically targets the core security mechanism designed to isolate potentially malicious code, creating a pathway for attackers to escape the restricted environment and gain access to underlying host resources.
The technical implementation of this vulnerability stems from inadequate module and function whitelisting mechanisms within the Python execution environment. Attackers can exploit the allowed modules and functions to construct malicious code sequences that circumvent the intended security restrictions, effectively creating a backdoor through which arbitrary code can be executed on the host system. This represents a fundamental breakdown in the security model where the sandbox mechanism fails to properly validate or restrict the execution context of code within whitelisted components. The vulnerability operates through a combination of code injection techniques and exploitation of the dynamic execution environment, allowing attackers to leverage legitimate system functions to achieve unauthorized access and control over the host machine.
The operational impact of this vulnerability extends beyond simple code execution to encompass comprehensive system compromise and potential data exfiltration capabilities. When exploited, the vulnerability enables attackers to execute arbitrary commands on the host system, potentially leading to complete system takeover, unauthorized data access, and the ability to establish persistent access through the compromised execution environment. The implications are particularly severe given that this vulnerability exists within a machine learning framework that may be used in production environments where code isolation is critical for security. The risk of unauthorized code execution creates opportunities for attackers to deploy additional malware, establish command and control channels, or access sensitive data through the compromised system.
Security mitigations for this vulnerability require immediate deployment of version 1.17.0 which addresses the sandbox escape mechanisms through enhanced code validation and stricter execution environment controls. Organizations should implement comprehensive monitoring for suspicious code execution patterns and establish network segmentation to limit the potential impact of exploitation. The fix demonstrates proper remediation through strengthened sandbox boundaries and improved validation of code execution contexts, addressing the core architectural weakness that allowed the bypass. Additionally, security teams should review their deployment practices to ensure that all components within the machine learning pipeline are updated to versions that contain the necessary security patches, as this vulnerability could be exploited in automated systems where code execution occurs without explicit user interaction.
This vulnerability aligns with common attack patterns identified in the attack tree methodology and represents a typical sandbox escape scenario that follows the principles outlined in the CWE catalog for insufficient sandboxing. The specific weakness manifests as a failure to properly enforce execution boundaries, creating opportunities for privilege escalation and unauthorized system access. The ATT&CK framework categorizes this vulnerability under the execution and privilege escalation domains, where attackers leverage sandbox bypass techniques to gain unauthorized access to system resources and execute malicious code with elevated privileges. The remediation approach demonstrates proper security engineering practices that address the root cause of the vulnerability rather than merely patching symptoms, establishing a more robust security boundary for future code execution scenarios.