Framework Proposed to Mitigate SQL Injection in LLM-Driven Applications
Researchers have introduced a new multi-layered security framework designed to address the growing risk of SQL injection attacks in applications powered by Large Language Models (LLMs). As natural language interfaces replace traditional SQL queries, they create vulnerabilities where malicious users can craft adversarial prompts to manipulate model behavior and generate unsafe database queries. The proposed solution integrates three key components: a front-end security shield for prompt sanitization, an advanced threat detection model for identifying behavioral and semantic anomalies, and a signature-based control layer for recognizing known attack patterns. The team evaluated this framework against diverse realistic scenarios, including prompt injection, obfuscated SQL payloads, and context-manipulation attacks. By utilizing a comprehensive benchmark dataset of adversarial prompts and testing on fine-tuned LLM configurations, the study demonstrates that the approach achieves high detection accuracy with low false-positive rates. This work significantly enhances the secure deployment of LLM-powered database systems, offering a robust defense mechanism against emerging AI-mediated security threats.
Wire timeline
Framework Proposed to Mitigate SQL Injection in LLM-Driven Applications
Researchers have introduced a new multi-layered security framework designed to address the growing risk of SQL injection attacks in applications powered by Large Language Models (LLMs). As natural language interfaces replace traditional SQL queries, they create vulnerabilities where malicious users can craft adversarial prompts to manipulate model behavior and generate unsafe database queries. The proposed solution integrates three key components: a front-end security shield for prompt sanitization, an advanced threat detection model for identifying behavioral and semantic anomalies, and a signature-based control layer for recognizing known attack patterns. The team evaluated this framework against diverse realistic scenarios, including prompt injection, obfuscated SQL payloads, and context-manipulation attacks. By utilizing a comprehensive benchmark dataset of adversarial prompts and testing on fine-tuned LLM configurations, the study demonstrates that the approach achieves high detection accuracy with low false-positive rates. This work significantly enhances the secure deployment of LLM-powered database systems, offering a robust defense mechanism against emerging AI-mediated security threats.
cs.AI updates on arXiv.org