Poodle Attack in Flask with Dynamodb

The Poodle attack (Padding Oracle On Downgraded Legacy Encryption) targets systems that negotiate SSL 3.0 and use block ciphers in CBC mode without proper integrity protection. In a Flask application that stores or transmits sensitive data using DynamoDB as a backend, the vulnerability arises when TLS 1.0 or SSL 3.0 is accidentally enabled and CBC ciphers are negotiated. An attacker can intercept HTTPS traffic between the Flask app and clients (or between the app and DynamoDB if custom encryption is applied at the application layer), then use a padding oracle to decrypt ciphertext by observing server behavior to chosen ciphertext queries.

Flask itself does not enable SSL; the risk typically comes from the deployment environment (e.g., an outdated WSGI server or load balancer) or from custom code that performs encryption before persisting to DynamoDB. If the Flask app encrypts data with a block cipher in CBC mode and exposes an error distinction between padding errors and other failures, an attacker can iteratively decrypt captured DynamoDB-stored ciphertext. This is especially relevant when sensitive fields (such as session tokens or personal data) are stored in DynamoDB and the Flask app does not enforce strong transport security and authenticated encryption.

DynamoDB does not use TLS 1.0 or SSL 3.0 for its HTTPS endpoint, so the Poodle attack does not apply to standard AWS SDK calls. However, a Flask app that retrieves ciphertext previously encrypted with a weak scheme and stored in DynamoDB remains at risk if the app’s decryption logic uses CBC without integrity checks. For example, if the Flask app stores encrypted blobs in DynamoDB and later decrypts them using a custom CBC implementation with a padding oracle, an attacker controlling the ciphertext can exploit this to recover plaintext.

To detect this specific combination, middleBrick runs the LLM/AI Security checks alongside standard API tests and maps findings to frameworks such as OWASP API Top 10 and PCI-DSS. The scan will flag use of weak ciphers, missing integrity protections, and unauthenticated endpoints, providing prioritized remediation guidance rather than attempting to fix the issue automatically.

Remediation focuses on removing CBC-mode cryptography where padding oracles are possible, enforcing strong transport security, and using authenticated encryption. Avoid implementing custom block cipher modes; prefer higher-level libraries and AWS services that handle encryption correctly.

• Enforce TLS 1.2+ for all endpoints and service communication. Configure your WSGI server or load balancer to disable SSL 3.0 and TLS 1.0/1.1. For DynamoDB, the AWS SDK uses HTTPS with modern TLS by default—do not add custom encryption layers that reintroduce CBC.
• If you must store sensitive data in DynamoDB, use envelope encryption with AWS KMS and ensure data integrity. Do not roll your own CBC padding scheme. Below is a secure example using the AWS SDK for Python (Boto3) and Fernet symmetric encryption for fields, which avoids CBC entirely.

from flask import Flask, request, jsonify
import boto3
from cryptography.fernet import Fernet
import os

app = Flask(__name__)
# Load or generate a Fernet key; store it securely (e.g., AWS Secrets Manager)
FERNET_KEY = os.environ.get("FERNET_KEY")
if not FERNET_KEY:
    raise RuntimeError("FERNET_KEY environment variable is required")
cipher = Fernet(FERNET_KEY)

dynamodb = boto3.resource("dynamodb", region_name="us-east-1")
table = dynamodb.Table("SecureData")

@app.route("/store", methods=["POST"])
def store_data():
    data = request.get_json()
    user_id = data.get("user_id")
    ssn = data.get("ssn")
    if not user_id or not ssn:
        return jsonify({"error": "user_id and ssn are required"}), 400
    encrypted_ssn = cipher.encrypt(ssn.encode())
    table.put_item(Item={"user_id": user_id, "ssn_encrypted": encrypted_ssn.decode()})
    return jsonify({"status": "stored"}), 201

@app.route("/retrieve")
def retrieve_data():
    user_id = request.args.get("user_id")
    if not user_id:
        return jsonify({"error": "user_id is required"}), 400
    resp = table.get_item(Key={"user_id": user_id})
    item = resp.get("Item")
    if not item:
        return jsonify({"error": "not found"}), 404
    try:
        ssn = cipher.decrypt(item["ssn_encrypted"].encode()).decode()
    except Exception:
        return jsonify({"error": "decryption failed"}), 400
    return jsonify({"user_id": user_id, "ssn": ssn}), 200

• Use authenticated encryption with associated data (AEAD) such as AES-GCM or Fernet instead of raw CBC. This prevents padding oracle attacks by providing integrity alongside confidentiality.
• Apply the principle of least privilege to the IAM role used by your Flask app when accessing DynamoDB, and enable AWS CloudTrail logging to monitor access to encrypted attributes.
• Validate and sanitize all inputs to prevent injection and ensure that error messages do not distinguish between padding failures and other errors, removing any potential oracle behavior in your application layer.

middleBrick’s scans include checks for unsafe consumption patterns and LLM/AI Security relevant to data handling logic. By following the above practices and using the provided DynamoDB integration patterns, you reduce the risk introduced by legacy encryption and ensure robust handling of sensitive data stored in DynamoDB.