CVE-2024-5826 in vanna
Summary
by MITRE • 06/27/2024
In the latest version of vanna-ai/vanna, the `vanna.ask` function is vulnerable to remote code execution due to prompt injection. The root cause is the lack of a sandbox when executing LLM-generated code, allowing an attacker to manipulate the code executed by the `exec` function in `src/vanna/base/base.py`. This vulnerability can be exploited by an attacker to achieve remote code execution on the app backend server, potentially gaining full control of the server.
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Analysis
by VulDB Data Team • 06/27/2024
The vulnerability identified as CVE-2024-5826 affects the vanna-ai/vanna library where the vanna.ask function exhibits a critical remote code execution flaw through prompt injection mechanisms. This security weakness stems from insufficient input validation and sanitization within the natural language processing pipeline that generates code from user prompts. The vulnerability specifically manifests in the absence of proper code sandboxing when executing LLM-generated code, creating an environment where malicious input can directly influence the execution context of the system. The root cause lies in the direct invocation of the exec function in src/vanna/base/base.py without adequate security boundaries or isolation mechanisms to prevent unintended code execution. This flaw represents a classic example of insecure deserialization and code execution vulnerabilities that can be exploited through crafted input sequences.
The technical exploitation of this vulnerability occurs when an attacker crafts a malicious prompt that, when processed by the LLM, generates code that includes harmful instructions. The vulnerability is particularly dangerous because it bypasses traditional security controls by leveraging the legitimate code generation capabilities of the AI system. When the exec function processes the generated code, it executes the malicious payload directly within the application's runtime environment without proper sandboxing or privilege restrictions. This allows attackers to execute arbitrary commands on the backend server, potentially gaining complete control over the system. The vulnerability is classified under CWE-94 as "Improper Control of Generation of Code" and aligns with ATT&CK technique T1059.001 for command and scripting interpreter, specifically targeting the execution of code within the application context.
The operational impact of CVE-2024-5826 extends far beyond simple code execution, as it provides attackers with full system compromise capabilities that can lead to data breaches, service disruption, and lateral movement within network environments. Attackers can leverage this vulnerability to establish persistent backdoors, exfiltrate sensitive data, or use the compromised server as a launch point for further attacks. The vulnerability affects any system running the vulnerable version of vanna-ai/vanna where the ask function is exposed to untrusted input, making it particularly dangerous in production environments where user input is processed through AI-generated code execution. Organizations using this library in web applications, API services, or automated systems face significant risk as the vulnerability can be exploited through various attack vectors including web interfaces, API endpoints, or direct input manipulation.
Mitigation strategies for CVE-2024-5826 should focus on implementing comprehensive code sandboxing mechanisms and input validation controls to prevent malicious code execution. Organizations should immediately update to patched versions of the vanna-ai/vanna library where appropriate sandboxing has been implemented. Alternative approaches include implementing strict code execution boundaries using containerization technologies, employing restricted execution environments with limited system privileges, and implementing comprehensive input sanitization and validation before any code generation or execution occurs. Additional defensive measures should include network segmentation, monitoring for suspicious execution patterns, and implementing application firewalls to detect and block malicious payloads. Security teams should also consider implementing principle of least privilege access controls and regular security assessments to identify similar vulnerabilities in other AI-powered systems and code generation pipelines within their infrastructure. The vulnerability underscores the critical importance of secure code execution practices in AI systems and highlights the need for robust security controls in machine learning applications that generate executable code from natural language input.