Phishing Api Keys in Flask with Dynamodb

A Flask application that stores or references API keys in DynamoDB can unintentionally expose those keys through insecure endpoints or misconfigured access patterns. When an API key is treated as a routine DynamoDB attribute, developers may overlook how retrieval routes, error handling, or logging behavior can leak the credential.

Consider a Flask route that fetches a key by an identifier without enforcing strict authorization:

import boto3
from flask import Flask, request, jsonify

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

If this route is exposed without authentication and reflects the stored key in responses, an attacker can use social engineering or compromised subdomains to trick users into visiting a URL that calls this endpoint and exfiltrate the key. This aligns with common phishing techniques where the attacker crafts a convincing frontend that invokes the backend API to harvest credentials.

DynamoDB configurations also contribute to risk. A table with a broad IAM policy or open to the internet (e.g., via Resource Policies or a misconfigured Lambda trigger) increases the attack surface. If a Flask app queries DynamoDB using user-supplied parameters without strict validation, injection or excessive data exposure can occur, making the API key accessible through unintended queries.

Logging and error messages are additional weak points. Flask apps that print raw exceptions including DynamoDB responses may inadvertently include API keys in logs or browser output:

@app.route('/get-key')
def get_key():
    key_id = request.args.get('id')
    try:
        item = table.get_item(Key={'id': key_id})
        return jsonify(item.get('Item', {}))
    except Exception as e:
        return jsonify({'error': str(e)}), 500

In this pattern, a verbose error could reveal internal details that aid a phishing campaign. The combination of Flask’s flexible routing, DynamoDB’s attribute-based storage, and insufficient access controls creates conditions where API keys become phishable assets.

Moreover, if the Flask app serves a web interface that includes the key in JavaScript or HTML, client-side exposure occurs. Attackers can use cross-site scripting (XSS) or social engineering to capture the key directly from the browser, especially when the app does not implement Content Security Policy (CSP) or strict input encoding.

Finally, scanning tools like middleBrick highlight these risks by correlating endpoint behavior with insecure data handling patterns. Its checks for Data Exposure, Input Validation, and Authentication help detect routes that return sensitive attributes like API keys, while LLM/AI Security probes identify potential client-side leakage paths that facilitate phishing.

Secure handling of API keys in Flask with DynamoDB requires strict access boundaries, least-privilege IAM, and careful data exposure management. Below are concrete, working examples that reduce the risk of key leakage.

1. Use IAM-bound access and scoped queries to limit what the Flask app can read:

import boto3
from flask import Flask, request, jsonify

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

Ensure the IAM role attached to the Flask environment (or the credentials used by boto3) has a policy that only allows dynamodb:GetItem on the specific table with a key condition on a partition attribute like owner:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "dynamodb:GetItem",
            "Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/ApiKeys",
            "Condition": {
                "ForAllValues:StringEquals": {
                    "dynamodb:LeadingKeys": ["${aws:username}"]
                }
            }
        }
    ]
}

2. Avoid returning the key directly; instead, use short-lived tokens or proxy access:

@app.route('/use-key', methods=['POST'])
def use_key():
    key_id = request.json.get('id')
    # Validate and enforce ownership checks here
    item = table.get_item(Key={'id': key_id})
    stored = item.get('Item')
    if not stored:
        return jsonify({'error': 'not found'}), 404
    # Use the key internally without echoing it back
    # e.g., call another service on behalf of the client
    return jsonify({'status': 'used'}), 200

3. Sanitize inputs to prevent injection and enforce schema validation:

from pydantic import BaseModel, ValidationError

class KeyRequest(BaseModel):
    id: str

@app.route('/validate-key', methods=['POST'])
def validate_key():
    try:
        body = KeyRequest(**request.json)
    except ValidationError:
        return jsonify({'error': 'invalid input'}), 400
    # Proceed with safe query using body.id
    return jsonify({'status': 'ok'}), 200

4. Disable verbose errors in production to prevent information leakage:

import logging
from logging import Formatter

app.config['PROPAGATE_EXCEPTIONS'] = False
handler = logging.StreamHandler()
handler.setFormatter(Formatter('%(asctime)s %(levelname)s %(message)s'))
app.logger.addHandler(handler)

5. Encrypt keys at rest and in transit. Use AWS KMS and enforce HTTPS in Flask:

from flask_talisman import Talisman

Talisman(app, content_security_policy=None)  # Enforces HTTPS

These steps align with the checks middleBrick performs, such as Data Exposure and Input Validation, by ensuring that API keys are not unnecessarily reflected and that endpoints enforce strict access controls.