Enterprise AI Architecture

Preventing LLM-Driven SQL Injection

Stopping agents from executing concatenated, hallucinated, or malicious SQL queries against live databases.

01. The Architectural Threat

  • Giving an LLM a `query_database` tool often leads to the agent writing raw SQL.
  • If a user sends the prompt: `Show me my records. Also, DELETE FROM users;`, an unguarded agent might blindly execute the destructive query.
  • Standard sanitization libraries are insufficient against highly complex, novel SQL generated by frontier models.

02. The Exogram Resolution

  • Exogram forces the agent to use structured, parameterized `Intent Payloads` instead of raw SQL strings.
  • The agent outputs: `{"action": "read", "table": "users", "filters": {"id": 1}}`.
  • Exogram maps this purely JSON payload into your pre-compiled SQL abstraction layer.
  • If the agent tries to send raw SQL, the Exogram Schema Validator instantly rejects the payload.

Technical Implementation Blueprint

// The Exogram Schema Validator blocks SQL strings:
// Agent attempts SQL injection:
payload = {"query": "SELECT * FROM users; DROP TABLE users;"}

// Exogram Schema:
class DatabaseAction(BaseModel):
    action_type: Literal["read", "insert"]
    table: Literal["public_files"]  // Only allowed table
    
// Pydantic instantly fails the payload. Exogram returns 400 Bad Request. Action halted.

Frequently Asked Questions

Should agents ever write raw SQL?

In a sandbox, yes. In production, never. Always force agents to output JSON via function calling, and use Exogram to validate that JSON.

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