Hallucination Attacks in Flask with Dynamodb

A Hallucination Attack in a Flask application that uses Amazon DynamoDB occurs when an attacker manipulates inputs or API behavior to trick downstream systems—such as language models or caching layers—into generating, amplifying, or exposing false information that appears authoritative. In this combination, Flask routes often construct DynamoDB queries dynamically, and if those queries or their results are passed to an LLM or logged for observability, hallucination risks emerge.

DynamoDB itself does not hallucinate, but its data model and query patterns can enable scenarios where incomplete or misinterpreted responses lead to inconsistent states. For example, a Flask endpoint that performs a GetItem or Query on DynamoDB and then feeds partial results into an LLM for natural-language summarization may produce plausible but incorrect statements if the item attributes are missing, stale, or overridden by conditional updates. Attackers can probe for these conditions by sending crafted parameters that cause sparse attributes or alternate partition key values, resulting in inconsistent responses that an LLM treats as valid context.

Another vector involves unauthenticated LLM endpoints reachable from Flask when developers expose model inference routes without proper checks. If a Flask route invokes an LLM using data derived from DynamoDB—such as user-supplied IDs used to fetch records—the model might incorporate injected noise or fabricated attributes present in the item (e.g., a description field containing user-generated content). This can lead to prompt injection or data exfiltration when the model’s output is trusted downstream. Additionally, DynamoDB Streams combined with Lambda can create asynchronous paths where hallucinated or malformed data propagates into event-driven workflows before human review.

Because middleBrick scans unauthenticated attack surfaces and includes LLM/AI Security checks—such as system prompt leakage detection, active prompt injection testing, and output scanning for PII or executable code—it can surface these risks in the Security risk score. The scan’s inventory management and unsafe consumption checks help identify places where DynamoDB responses are consumed by LLMs or other automated systems without validation. Findings map to OWASP API Top 10 categories like Improper Asset Management and Excessive Data Exposure, emphasizing the need to treat LLM outputs as untrusted regardless of the backend datastore.

Remediation focuses on strict input validation, defensive querying, and safe consumption patterns when integrating Flask with DynamoDB. Avoid dynamic construction of keys or condition expressions based on raw user input, and ensure every DynamoDB operation uses explicit parameter structures. Return only necessary attributes, and never forward raw item contents directly to LLMs without normalization and filtering.

Use the AWS SDK for Python (boto3) with strongly typed inputs and enable client-side validation. For example, when retrieving an item by a user-supplied ID, validate the ID format before using it in GetItem:

import re
import boto3
from flask import Flask, jsonify, request

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('Products')

PRODUCT_ID_REGEX = re.compile(r'^[A-Z]{2}-\d{6}$')  # e.g., AB-123456

@app.route('/product')
def get_product():
    pid = request.args.get('id', '')
    if not PRODUCT_ID_REGEX.match(pid):
        return jsonify({'error': 'invalid product id'}), 400
    response = table.get_item(Key={'product_id': pid})
    item = response.get('Item')
    if item is None:
        return jsonify({'error': 'not found'}), 404
    # Return a controlled subset of fields
    return jsonify({
        'product_id': item['product_id'],
        'name': item['name'],
        'price': item['price']
    })

When querying with conditions, prefer parameterized expressions over string concatenation to prevent injection and ensure predictable results:

import boto3
from flask import Flask, jsonify, request

app = Flask(__name__)
dynamodb = boto3.client('dynamodb')

@app.route('/orders')
def list_orders():
    user_id = request.args.get('user_id', '')
    # Validate user_id format before use
    if not user_id.isalnum() or len(user_id) > 32:
        return jsonify({'error': 'invalid user_id'}), 400
    response = dynamodb.query(
        TableName='Orders',
        KeyConditionExpression='user_id = :uid',
        ExpressionAttributeValues={':uid': {'S': user_id}},
        Limit=50
    )
    items = response.get('Items', [])
    # Normalize and filter fields before any further processing
    safe_items = [{'order_id': i['order_id']['S'], 'total': i['total']['N']} for i in items]
    return jsonify(safe_items)

To reduce hallucination surface when consuming data in downstream AI workflows, implement normalization and schema checks. For example, before passing an item to an LLM endpoint, strip unexpected keys and ensure numeric fields are correctly typed:

import boto3
from flask import Flask, jsonify

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('Inventory')

ALLOWED_FIELDS = {'sku', 'name', 'quantity', 'price', 'updated_at'}

def sanitize_item(item):
    return {k: v for k, v in item.items() if k in ALLOWED_FIELDS}

@app.route('/inventory')
def get_inventory():
    sku = request.args.get('sku', '')
    response = table.get_item(Key={'sku': sku})
    item = response.get('Item')
    if not item:
        return jsonify({'error': 'not found'}), 404
    clean = sanitize_item(item)
    # Only pass sanitized data to LLM
    # llm_response = call_llm(f"Summarize: {clean}")
    return jsonify(clean)

Additionally, enforce least privilege IAM roles for the Flask application and enable DynamoDB encryption at rest. Monitor CloudTrail and DynamoDB Streams for anomalous access patterns that could precede data manipulation attacks. These measures align with the findings and remediation guidance provided by middleBrick’s Pro plan continuous monitoring and CI/CD integration, which can automatically flag insecure configurations in your pipeline.