CVE-2026-32949 in SQLBot
Summary
by MITRE • 03/20/2026
SQLBot is an intelligent data query system based on a large language model and RAG. Versions prior to 1.7.0 contain a Server-Side Request Forgery (SSRF) vulnerability that allows an attacker to retrieve arbitrary system and application files from the server. An attacker can exploit the /api/v1/datasource/check endpoint by configuring a forged MySQL data source with a malicious parameter extraJdbc="local_infile=1". When the SQLBot backend attempts to verify the connectivity of this data source, an attacker-controlled Rogue MySQL server issues a malicious LOAD DATA LOCAL INFILE command during the MySQL handshake. This forces the target server to read arbitrary files from its local filesystem (such as /etc/passwd or configuration files) and transmit the contents back to the attacker. This issue was fixed in version 1.7.0.
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Analysis
by VulDB Data Team • 03/26/2026
The vulnerability described in CVE-2026-32949 represents a critical Server-Side Request Forgery (SSRF) flaw within SQLBot, an intelligent data query system leveraging large language models and Retrieval Augmented Generation (RAG) technologies. This vulnerability affects versions prior to 1.7.0 and demonstrates how modern AI-driven database interaction systems can introduce unexpected security risks through seemingly benign configuration parameters. The flaw exists in the system's validation mechanisms for MySQL data source configurations, where the application fails to properly sanitize or restrict external resource access during connectivity verification processes. The vulnerability is particularly concerning because it operates at the server-side level, allowing attackers to bypass traditional network segmentation and access sensitive local system files without requiring authentication or direct network access to the target server. This type of vulnerability falls under CWE-918, which specifically addresses Server-Side Request Forgery, and aligns with ATT&CK technique T1071.004 for Application Layer Protocol: DNS, as it exploits legitimate database protocols to achieve unauthorized file access.
The technical exploitation mechanism involves a sophisticated multi-stage attack that begins with crafting a malicious data source configuration through the /api/v1/datasource/check endpoint. Attackers configure a forged MySQL data source with the specific parameter extraJdbc="local_infile=1" which enables the MySQL client to accept local file loading commands from the server. When the SQLBot backend attempts to verify connectivity to this maliciously configured data source, it establishes a connection to what appears to be a legitimate MySQL server. However, the attacker controls a rogue MySQL server that during the initial handshake process issues a malicious LOAD DATA LOCAL INFILE command. This command forces the target SQLBot server to execute a file read operation on its local filesystem, effectively allowing the attacker to retrieve arbitrary files such as system configuration files, credential stores, or application-specific data. The vulnerability exploits the MySQL client's trust relationship with the server and the implicit assumption that database connectivity verification should not trigger local file system operations. This technique demonstrates the dangerous intersection of database protocol features and application security misconfigurations, where legitimate database functionalities become attack vectors when improperly controlled.
The operational impact of this vulnerability extends beyond simple information disclosure, as it provides attackers with unprecedented access to sensitive server resources that could include database credentials, application configuration files, system user information, and potentially even application source code. The ability to read system files like /etc/passwd indicates that attackers can gain insights into the system's user structure and potentially identify additional attack vectors or targets within the network. This vulnerability particularly affects organizations that rely on SQLBot for database query automation and AI-driven data analysis, as it could compromise not only the integrity of database queries but also the confidentiality of the entire underlying system infrastructure. The vulnerability's exploitation requires minimal privileges and can be executed through the standard API interface, making it particularly dangerous as it can be leveraged by attackers with limited access to the system. Organizations may face compliance violations, data breaches, and potential lateral movement within their network infrastructure if this vulnerability is exploited successfully. The vulnerability's impact is amplified by the fact that it operates silently during normal database connectivity verification processes, making detection difficult and potentially allowing prolonged unauthorized access.
Mitigation strategies for this vulnerability should focus on both immediate patching and architectural improvements to prevent similar issues in the future. The most direct solution is upgrading to SQLBot version 1.7.0 or later, where the vulnerability has been addressed through proper parameter validation and enhanced security controls for database connectivity verification. Organizations should implement strict input validation for all database configuration parameters, particularly those that enable potentially dangerous MySQL client features like local file loading. Network segmentation and firewall rules should be implemented to restrict communication between the SQLBot application and external MySQL servers, preventing unauthorized rogue servers from being reached during connectivity checks. Additional security measures include implementing database query auditing, monitoring for unusual file access patterns, and restricting the execution privileges of the SQLBot application to minimize potential damage from successful exploitation attempts. Organizations should also consider implementing a principle of least privilege for database connections, ensuring that applications only have access to the minimum required database resources and that local file operations are strictly controlled and monitored. The vulnerability highlights the importance of security testing for AI-driven applications and the need for comprehensive security controls that address both traditional application vulnerabilities and emerging risks associated with machine learning and natural language processing systems.