SQLBot is an intelligent Text-to-SQL system based on large language models and RAG. Prior to 1.8.0, SQLBot contains a Cross-Workspace IDOR (Insecure Direct Object Reference) and Authorization Bypass vulnerability in the /api/v1/datasource/exportDsSchema and /api/v1/datasource/uploadDsSchema endpoints. An attacker can access and modify database schemas and data sources belonging to other tenants/workspaces. This vulnerability is fixed in 1.8.0.
SQLBot is an intelligent Text-to-SQL system based on large language models and RAG. In versions 1.7.0 and earlier, the Text2SQL chat interface is vulnerable to prompt injection. The user-provided question parameter is directly concatenated into the LLM prompt without filtering or escaping, and the SQL extracted from the LLM response is executed against the database without validation or sanitization. An authenticated attacker can craft a malicious question to manipulate the LLM into generating and executing arbitrary SQL statements. When connected to a PostgreSQL data source, this can lead to remote code execution via COPY FROM PROGRAM. This issue has been fixed in version 1.7.1.
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.
SQLBot is an intelligent data query system based on a large language model and RAG. Versions prior to 1.7.0 contain a critical SQL Injection vulnerability in the /api/v1/datasource/uploadExcel endpoint that enables Remote Code Execution (RCE), allowing any authenticated user (even the lowest-privileged) to fully compromise the backend server. The root cause is twofold: Excel Sheet names are concatenated directly into PostgreSQL table names without sanitization (datasource.py#L351), and those table names are embedded into COPY SQL statements via f-strings instead of parameterized queries (datasource.py#L385-L388). An attacker can bypass the 31-character Sheet name limit using a two-stage technique—first uploading a normal file whose data rows contain shell commands, then uploading an XML-tampered file whose Sheet name injects a TO PROGRAM 'sh' clause into the SQL. Confirmed impacts include arbitrary command execution as the postgres user (uid=999), sensitive file exfiltration (e.g., /etc/passwd, /etc/shadow), and complete PostgreSQL database takeover. This issue has been fixed in version 1.7.0.