CVE-2024-8238 affects aimhubio/aim version 3.22.0 due to the use of an outdated safer_getattr() function from the RestrictedPython library within the AimQL query language. RestrictedPython is designed to sandbox Python code execution by restricting attribute access, but the older safer_getattr() version fails to properly neutralize the str.format_map() method. This method can be abused by attackers to access arbitrary attributes of Python objects, including sensitive environment variables (os.environ) and other server-side secrets. The vulnerability arises because str.format_map() bypasses the intended restrictions, enabling information disclosure. Additionally, if an attacker can write files to a known location on the Aim server, they can leverage str.format_map() to load malicious dynamic libraries (.dll on Windows or .so on Unix-like systems) into the Python interpreter process, resulting in unrestricted remote code execution. The CVSS v3.0 score is 5.9 (medium), reflecting network attack vector, no privileges required, no user interaction, but high attack complexity and impact limited to confidentiality. No patches or exploits are currently publicly available, but the risk of secret leakage and code execution makes this a significant concern for deployments of aimhubio/aim, especially in environments processing sensitive data or exposed to untrusted users.

For European organizations, this vulnerability poses a dual risk: unauthorized disclosure of sensitive environment variables or secrets and potential full system compromise via remote code execution. Organizations using aimhubio/aim in AI/ML workflows that handle confidential data, intellectual property, or personal data protected under GDPR could face data breaches leading to regulatory penalties and reputational damage. The ability to execute arbitrary code remotely could allow attackers to pivot within networks, deploy ransomware, or exfiltrate data. Since the vulnerability can be exploited remotely without authentication, exposed Aim servers or those accessible by untrusted users are particularly vulnerable. The medium CVSS score reflects the higher complexity but significant confidentiality impact. European sectors with critical AI infrastructure, research institutions, and cloud service providers using aimhubio/aim are at elevated risk.

1. Upgrade aimhubio/aim to a version that patches this vulnerability once available. 2. Until a patch is released, restrict network access to Aim servers to trusted internal networks only, using firewalls and VPNs. 3. Implement strict file system permissions to prevent unauthorized users from writing files to locations accessible by the Aim server, mitigating the risk of malicious shared library loading. 4. Monitor Aim server logs and network traffic for unusual activity indicative of exploitation attempts, such as unexpected file writes or suspicious query patterns leveraging str.format_map(). 5. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to detect and block anomalous code execution behaviors. 6. Conduct regular security audits of AI/ML infrastructure and ensure secrets are stored securely, minimizing exposure if environment variables are leaked. 7. Educate developers and administrators about the risks of using outdated sandboxing libraries and encourage secure coding practices when integrating third-party components.