Hallucination Attacks in Flask with Cockroachdb

A Hallucination Attack in a Flask application using CockroachDB typically occurs when an attacker manipulates inputs or prompts to an integrated LLM so that the model produces false, misleading, or fabricated information—often presented as authoritative. In this stack, the vulnerability is not primarily in CockroachDB itself, which is a strongly consistent, distributed SQL database, but in how application code constructs queries and responses when an LLM is involved.

Consider a Flask endpoint that accepts a natural-language question, passes it to an LLM to generate a SQL query, executes that query against CockroachDB, and returns results. If the LLM output is not strictly validated and parameterized, an attacker can supply crafted input that causes the LLM to hallucinate a query that bypasses intended filters or exposes sensitive rows. For example, a prompt injection may cause the LLM to generate a query with tautology conditions like 1=1 or to omit WHERE clauses entirely. Because CockroachDB returns results faithfully, the database will execute the malformed query and expose data the application should have restricted.

The risk is compounded when the LLM has direct access to the database connection string or when connection pooling is misconfigured, allowing the hallucinated query to run with higher privileges than intended. Additionally, if the Flask app logs raw LLM outputs or errors verbosely, it may leak schema details that aid further exploitation. OWASP API Top 10 categorizes this as an input validation and LLM/AI Security issue; unchecked LLM-generated queries can lead to unauthorized data access, a form of IDOR or BOLA when combined with weak object-level authorization.

Real-world attack patterns include crafting a request like {"question": "Show me salaries where 1=1--"} that tricks the LLM into producing SELECT id, salary FROM employees WHERE 1=1--. CockroachDB executes this and returns all rows. Because the database enforces serializable isolation and distributed consistency, there is no error—just unintended data exposure. Without proper input validation and strict schema-bound query templates, the Flask layer becomes the weak point, and the database simply reflects the hallucinated intent.

Remediation centers on strict input validation, parameterized queries, and avoiding dynamic SQL assembly with LLM output. Never directly concatenate LLM-generated text into database calls. Instead, design your Flask routes to use prepared statements and enforce schema constraints explicitly.

Secure Query Execution Pattern

Use SQLAlchemy or psycopg2 with parameterized statements. If you must allow LLMs to influence query structure, restrict it to predefined, validated templates only.

import psycopg2
from flask import Flask, request, jsonify
import re

app = Flask(__name__)

# CockroachDB connection parameters
DB_CONFIG = {
    'dbname': 'app_db',
    'user': 'app_user',
    'password': 'strong_password',
    'host': 'localhost',
    'port': 26257,
    'sslmode': 'require',
}

def get_db_connection():
    return psycopg2.connect(**DB_CONFIG)

@app.route('/api/employees', methods=['GET'])
def list_employees():
    department = request.args.get('department')
    # Strict allowlist validation
    if department and not re.match(r'^[A-Za-z0-9_\-\s]{1,64}$', department):
        return jsonify({'error': 'Invalid department'}), 400

    conn = None
    try:
        conn = get_db_connection()
        cur = conn.cursor()
        if department:
            cur.execute('SELECT id, name, department FROM employees WHERE department = %s', (department,))
        else:
            cur.execute('SELECT id, name, department FROM employees')
        rows = cur.fetchall()
        return jsonify([{'id': r[0], 'name': r[1], 'department': r[2]} for r in rows])
    except Exception as e:
        return jsonify({'error': 'Internal server error'}), 500
    finally:
        if conn:
            conn.close()

LLM Integration Guardrails

If integrating an LLM, treat its output as untrusted input. Parse and validate before use. For example, if an LLM suggests a column name or table, verify it against an allowlist derived from your schema.

ALLOWED_COLUMNS = {'id', 'name', 'department', 'salary', 'start_date'}
ALLOWED_TABLES = {'employees', 'departments'}

def sanitize_llm_output(prompt_response: str):
    # Basic lexical validation to prevent hallucinated identifiers
    tokens = re.findall(r'\b\w+\b', prompt_response)
    safe_tokens = [t for t in tokens if t in ALLOWED_COLUMNS or t in ALLOWED_TABLES]
    return ' '.join(safe_tokens) if safe_tokens else ''

Additionally, enforce role-based access control at the database level in CockroachDB, using least-privilege users for the Flask app. Avoid granting DELETE or DDL permissions to the application user. Use prepared statements exclusively and never build queries by string interpolation, even when logs or debugging suggest convenience.

Finally, monitor and log query patterns without exposing raw LLM outputs. This helps detect anomalies that may indicate hallucination attempts while preserving data confidentiality and integrity.