Data Breach News

Vanna AI Vulnerability: Remote Code Execution (CVE-2024-5565)

The Vanna AI vulnerability arises primarily from how Vanna.AI handles user prompts within its ask function.

by Ashish Khaitan June 28, 2024

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A critical security flaw has been uncovered in the Vanna.AI library, exposing SQL databases to potential remote code execution (RCE) attacks through prompt injection techniques. Tracked as CVE-2024-5565 with a CVSS score of 8.1, this Vanna AI vulnerability allows malicious actors to manipulate prompts in Vanna.AI’s “ask” function of Vanna.AI, leveraging large language models (LLMs) to execute arbitrary commands.

Vanna.AI is a Python-based machine learning library designed to simplify interaction with SQL databases by converting natural language prompts into SQL queries. This functionality, facilitated by LLMs, enables users to query databases simply by asking questions.

Vanna AI Vulnerability Leads to Remote Code Execution (RCE)

The Vanna AI vulnerability was first identified by cybersecurity researchers at JFrog. They found that by injecting malicious prompts into the “ask” function, attackers could bypass security controls and force the library to execute unintended SQL commands. This technique, known as prompt injection, exploits the inherent flexibility of LLMs in interpreting user inputs.

According to JFrog, “Prompt injection vulnerabilities like CVE-2024-5565 highlight the risks associated with integrating LLMs into user-facing applications, particularly those involving sensitive data or backend systems. In this case, the flaw in Vanna.AI allows attackers to subvert intended query behavior and potentially gain unauthorized access to databases.”

The issue was also independently discovered and reported by Tong Liu through the Huntr bug bounty platform, highlighting its significance and widespread impact potential.

Understanding Prompt Injection and Its Implications

Prompt injection exploits the design of LLMs, which are trained on diverse datasets and thus susceptible to misinterpreting prompts that deviate from expected norms. While developers often implement pre-prompting safeguards to guide LLM responses, these measures can be circumvented by carefully crafted malicious inputs.

“In the context of Vanna.AI,” explains JFrog, “prompt injection occurs when a user-supplied prompt manipulates the SQL query generation process, leading to unintended and potentially malicious database operations. This represents a critical security concern, particularly in applications where SQL queries directly influence backend operations.”

Technical Details and Exploitation

The Vanna AI vulnerability arises primarily from how Vanna.AI handles user prompts within its ask function. By injecting specially crafted prompts containing executable code, attackers can influence the generation of SQL queries. This manipulation can extend to executing arbitrary Python code, as demonstrated in scenarios where the library dynamically generates Plotly visualizations based on user queries.

“In our analysis,” notes JFrog, “we observed that prompt injection in Vanna.AI allows for direct code execution within the context of generated SQL queries. This includes scenarios where the generated code inadvertently includes malicious commands, posing a significant risk to database security.”

Upon discovery, Vanna.AI developers were promptly notified and have since released mitigation measures to address the CVE-2024-5565 vulnerability. These include updated guidelines on prompt handling and additional security best practices to safeguard against future prompt injection attacks.

“In response to CVE-2024-5565,” assures JFrog, “Vanna.AI has reinforced its prompt validation mechanisms and introduced stricter input sanitization procedures. These measures are crucial in preventing similar vulnerabilities and ensuring the continued security of applications leveraging LLM technologies.”

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