CVE-2024-5826: Vanna-AI Vanna RCE Vulnerability

CVE-2024-5826 Overview

A critical remote code execution vulnerability exists in the vanna-ai/vanna library, specifically within the vanna.ask function. The vulnerability stems from prompt injection that allows attackers to manipulate LLM-generated code, which is then executed without proper sandboxing via the exec function in src/vanna/base/base.py. This flaw enables attackers to achieve remote code execution on the application backend server, potentially gaining full control of the affected system.

Critical Impact

Successful exploitation allows attackers to execute arbitrary code on the backend server, leading to complete system compromise including data theft, lateral movement, and persistent access.

Affected Products

• vanna-ai/vanna (latest version at time of disclosure)

Discovery Timeline

• 2024-06-27 - CVE-2024-5826 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2024-5826

Vulnerability Analysis

This vulnerability is classified as Code Injection (CWE-94), representing a severe security flaw in AI-powered applications that execute dynamically generated code. The vanna-ai/vanna library is designed to convert natural language queries into SQL and Python code using large language models (LLMs). However, the architecture fails to implement proper security boundaries between user input, LLM output, and code execution.

The core issue lies in the trust model: the application treats LLM-generated code as safe without validation or sandboxing. Since LLMs can be manipulated through carefully crafted prompts, an attacker can inject malicious instructions that cause the model to generate harmful code. This code is then executed with the same privileges as the application server, creating a direct path from user input to arbitrary code execution.

Root Cause

The root cause is the absence of a secure sandbox environment when executing LLM-generated code. The vanna.ask function passes code generated by the language model directly to Python's exec function in src/vanna/base/base.py. Without input validation, output sanitization, or execution containment, any code produced by the LLM—including malicious payloads injected through prompt manipulation—runs with full server privileges.

Attack Vector

The attack vector is network-based and requires no authentication or user interaction. An attacker can exploit this vulnerability by sending specially crafted prompts to the vanna.ask function that manipulate the LLM into generating malicious Python code. When the application processes this prompt, the LLM produces attacker-controlled code that bypasses any intended functionality and executes arbitrary commands.

For example, an attacker could craft prompts that instruct the LLM to include system commands within the generated code, such as reverse shells, file system access, or data exfiltration routines. Because the exec function runs this code without restrictions, the attacker gains the same access level as the application process—typically full control over the backend server. For detailed technical information, refer to the Huntr Bounty Details.

Detection Methods for CVE-2024-5826

Indicators of Compromise

• Unusual outbound network connections from the application server to unknown external hosts
• Unexpected system processes spawned by the Python application process
• Suspicious file creation or modification in the application directory or system paths
• Anomalous database queries or data access patterns following natural language query submissions

Detection Strategies

• Monitor exec function calls within the vanna library for code patterns that include system commands, network operations, or file system manipulation
• Implement application-level logging to capture all prompts submitted to the vanna.ask function and the corresponding generated code
• Deploy behavioral analysis to detect deviations from normal application execution patterns
• Use network intrusion detection to identify command-and-control traffic originating from the application server

Monitoring Recommendations

• Enable verbose logging for all LLM interactions and code execution events in the vanna library
• Configure alerting for any use of dangerous Python functions such as os.system, subprocess, socket, or eval in generated code
• Implement runtime application self-protection (RASP) to monitor and block malicious code execution attempts

How to Mitigate CVE-2024-5826

Immediate Actions Required

• Assess your environment for deployments of vanna-ai/vanna and determine if the vanna.ask function is exposed to untrusted input
• Implement network segmentation to limit the blast radius of potential code execution on application servers
• Consider temporarily disabling the vulnerable functionality until a patch or workaround is implemented
• Review application logs for signs of exploitation or suspicious prompt patterns

Patch Information

At the time of publication, users should monitor the Huntr Bounty Details and the official vanna-ai/vanna repository for security updates and patches addressing this vulnerability.

Workarounds

• Implement a code review layer that validates LLM-generated code before execution, rejecting any code containing dangerous functions or patterns
• Execute LLM-generated code within a restricted sandbox environment with limited system access and network isolation
• Add input sanitization to filter prompt injection attempts before they reach the LLM
• Restrict the application's system privileges using containerization or process isolation to minimize impact if exploitation occurs