Insecure Output Handling: SQL Injection Through LLM Output (Part 2) Large Language Models are increasingly being integrated into applications that interact directly with databases, often acting as an …

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LLM-integrated applications that translate natural language into SQL queries introduce a new class of vulnerabilities where the model itself becomes the attack surface. Three lab scenarios are walked through: data exfiltration via schema enumeration, bypassing model-level guardrails and backend whitelists using reframed prompts to generate UNION-based injection, and privilege escalation by instructing the model to INSERT an admin user without any traditional injection syntax. The core insight is that security controls focused on input validation fail when the model generates fully valid but attacker-controlled SQL. LLM output must be treated as untrusted and subjected to strict authorization and query validation before execution.

Insecure Output Handling: SQL Injection Through LLM Output (Part 2)

Bypassing Guardrails through Prompt-Driven SQL Injection