Prompt Injection in Adonisjs with Cockroachdb

Prompt injection in an Adonisjs application using Cockroachdb typically occurs when user-controlled input is incorporated into prompts that are sent to an LLM endpoint, and those prompts are subsequently executed against a database. In this stack, an attacker may try to manipulate the prompt via query parameters, request bodies, or headers to cause the LLM to leak system instructions or to misuse database permissions. Because Adonisjs often serves as the backend API layer, it may construct dynamic prompts and then forward them to an LLM service; if those prompts include raw user data or concatenated SQL-like fragments intended for Cockroachdb, the LLM may treat them as authoritative instructions.

Consider an endpoint that builds a system prompt by embedding a user-supplied query context and then asks the LLM to generate SQL for Cockroachdb. If the system prompt includes unescaped user input, an LLM security probe can attempt to override the original instructions. For example, an attacker could supply a payload designed to inject a new instruction set, causing the model to ignore prior constraints and attempt unauthorized actions such as reading from other tables or extracting schema details. Even without direct database access from the LLM, the injection can lead to information disclosure or logic abuse, especially when the generated SQL is later executed by the application with elevated privileges.

Another angle specific to Adonisjs with Cockroachdb is the use of LLM-generated queries that reference database objects. If the application uses an unauthenticated endpoint to submit user data to an LLM and then directly executes the returned statements against Cockroachdb, the LLM might produce output containing API keys, PII, or executable code hidden within seemingly valid SQL. Because Cockroachdb supports advanced SQL features, the generated statements might inadvertently expose sensitive data or bypass intended row-level restrictions if authorization checks are implemented at the application layer rather than enforced by the database. This highlights the importance of validating and sandboxing LLM outputs before they reach the database, particularly when the model is asked to produce or modify data in a multi-tenant environment.

To reduce prompt injection risk when using Adonisjs with Cockroachdb, treat LLM inputs and outputs as untrusted and enforce strict separation between prompt construction and database execution. Do not directly embed user input into system or user prompt strings. Instead, use parameterized prompts and validate all inputs against an allowlist where possible. For database interactions, use Adonisjs ORM or query builder with parameterized statements rather than string concatenation, and avoid passing generated SQL directly to Cockroachdb without review.

Below are concrete code examples demonstrating safer patterns.

// Safe prompt construction with no user injection into system prompt
const { prompt } = require('@ai-sdk/prompts');
const { drizzle } = require('drizzle-orm/cockroachdb');
const { sql } = require('@vercel/postgres'); // compatible with Cockroachdb

// Do NOT do this:
// const systemPrompt = `You are a helper. User says: ${userInput}`;

// Do this instead:
const systemPrompt = 'You are a helper. Convert the user intent into safe SQL for Cockroachdb.';
const userMessage = { role: 'user', content: userInput };

const result = await prompt({
  model: 'your-llm-model',
  messages: [systemPrompt, userMessage],
  // restrict model capabilities to minimize agency
});

// Validate and parameterize before sending to Cockroachdb
const db = drizzle(sql);
const safeQuery = db.select().from(yourTable).where(eq(yourTable.id, userId));
const rows = await safeQuery.execute();

Additionally, implement explicit output validation for any SQL or data returned by the LLM before it reaches Cockroachdb. Use allowlists for table and column names, and avoid dynamic execution of DDL or DML statements produced by the model. For continuous monitoring, the middleBrick CLI can scan your Adonisjs endpoints to detect whether unauthenticated LLM endpoints are exposed and whether prompt injection tests reveal system prompt leakage.

Finally, leverage infrastructure boundaries: keep LLM calls and database calls in separate trust zones, and ensure the Adonisjs app does not automatically execute LLM-generated statements without human review or strict schema checks. The Pro plan’s continuous monitoring can help track changes in your endpoints that might reintroduce prompt injection risks over time.