Critical SQL Injection in LlamaIndex (CVE-2025-1793): Exposing LLM‑Driven Backdoor Risks

LlamaIndex, a popular framework for connecting large language models to vector stores, was found to contain a critical SQL injection vulnerability, CVE-2025-1793. This flaw stemmed from unsanitized inputs flowing from LLM-generated prompts into database queries via methods like vector_store.delete(). In a typical scenario, a user’s natural language request could be transformed by the LLM into a malicious SQL command—such as "project:X' OR 1=1 --"—leading to unauthorized data deletion, exposure, or manipulation. The vulnerability affects multiple vector store integrations (ClickHouse, Couchbase, DeepLake, Jaguar, Lantern, Nile, OracleDB, SingleStoreDB) and has been addressed in LlamaIndex version 0.12.28. Patches include input sanitization, though rigor varies across database types. The advisory highlights a broader risk: when LLMs encode backend operations without proper sanitization, they can create hidden attack vectors. Developers are urged to apply the patch and implement comprehensive sanitization and secure design practices in LLM‑integrated systems. 

https://www.endorlabs.com/learn/critical-sql-injection-vulnerability-in-llamaindex-cve-2025-1793---advisory-and-analysis

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