CVE-2009-0367 in Wesnoth
Summary
by MITRE
The Python AI module in Wesnoth 1.4.x and 1.5 before 1.5.11 allows remote attackers to escape the sandbox and execute arbitrary code by using a whitelisted module that imports an unsafe module, then using a hierarchical module name to access the unsafe module through the whitelisted module.
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Analysis
by VulDB Data Team • 12/25/2024
The vulnerability described in CVE-2009-0367 represents a critical sandbox escape flaw within the Python AI module of the popular strategy game Wesnoth. This issue affects versions 1.4.x through 1.5 before 1.5.11, where the game's artificial intelligence system operates within a restricted environment designed to prevent unauthorized code execution. The fundamental problem lies in the module loading mechanism that employs a whitelist approach to control which Python modules can be imported and executed within the AI sandbox. Attackers can exploit this weakness by leveraging a whitelisted module that internally imports an unsafe module, then utilizing hierarchical module naming conventions to bypass the sandbox restrictions and gain access to restricted functionality.
The technical implementation of this vulnerability exploits the Python module resolution process and the way the Wesnoth AI system handles module imports. When a whitelisted module is loaded, it can import other modules that are not explicitly whitelisted but are accessible through the Python path. By carefully constructing module names using hierarchical paths, attackers can traverse the module namespace to access the unsafe modules that should have been restricted. This approach bypasses traditional sandboxing mechanisms that rely solely on explicit whitelisting, instead exploiting the implicit trust placed in the module loading system. The vulnerability is particularly dangerous because it operates at the Python interpreter level, allowing attackers to execute arbitrary code with the privileges of the game process, potentially leading to complete system compromise.
The operational impact of this vulnerability extends beyond simple code execution, as it fundamentally undermines the security model of the Wesnoth AI system. An attacker who can successfully exploit this vulnerability gains the ability to execute arbitrary code on the target system, potentially leading to data theft, system compromise, or further attacks. The sandbox escape capability means that even if the game's AI system is designed to be isolated from the broader system, this flaw allows attackers to break out of that isolation. This vulnerability affects not only the immediate game environment but also poses risks to any system where Wesnoth is installed, particularly in environments where the game might be used by untrusted users or where it could be exploited as part of a larger attack chain. The vulnerability is classified under CWE-254 as a "Weakness in a Security Model" and aligns with ATT&CK techniques related to privilege escalation and sandbox evasion.
Mitigation strategies for this vulnerability require both immediate patching and architectural improvements to the sandboxing approach. The primary solution involves updating to Wesnoth version 1.5.11 or later, where the developers have addressed the module loading mechanism to prevent hierarchical path traversal attacks. Organizations should implement comprehensive patch management processes to ensure all affected systems are updated promptly. Additionally, system administrators should consider implementing additional security controls such as network segmentation, process isolation, and monitoring for unusual module loading patterns. The vulnerability highlights the importance of proper module access controls and the need for robust sandboxing implementations that cannot be bypassed through path manipulation or implicit trust assumptions. Security teams should also consider implementing runtime application self-protection measures and regular security assessments of interpreted language environments to identify similar weaknesses in other applications.