Ssrf Server Side in Flask with Dynamodb

Server-side request forgery (SSRF) in a Flask application that interacts with Amazon DynamoDB can occur when user-supplied input is used to construct HTTP requests or to build DynamoDB resource identifiers without validation. In this combination, an attacker may trick the server into making unintended requests to internal services or metadata endpoints, potentially reaching the DynamoDB service itself or other back-end systems. For example, if a Flask endpoint accepts a table name or key condition and passes it to an HTTP client to validate existence or to fetch metadata, an attacker can supply a malicious URL that causes the server to probe internal AWS metadata service (169.254.169.254) or other internal endpoints.

Flask routes that accept parameters and forward them to an HTTP library (e.g., requests) are common SSRF vectors. When those requests influence or bypass access controls to DynamoDB operations, the risk expands: an attacker might cause the server to perform unintended scans or retrieve items by leveraging the server’s IAM permissions. Because DynamoDB endpoints are reachable from the same network context, SSRF can lead to unauthorized data access or reconnaissance within a VPC. The server-side nature means the attacker does not need direct network access to DynamoDB; they abuse the trusted server to perform operations on their behalf.

An illustrative scenario: a Flask route accepts a URL parameter supposed to be a public resource, uses it to fetch data, and then conditionally writes to DynamoDB. If input validation is weak, an attacker supplies an internal AWS service URL (e.g., http://169.254.169.254/latest/meta-data/iam/security-credentials/), causing the server to leak credentials. Those credentials can then be used to interact with DynamoDB, bypassing any intended restrictions. Therefore, the SSRF vector here is not just about outbound HTTP calls; it is about how those calls intersect with DynamoDB operations and the broader AWS environment.

Remediation centers on strict input validation, avoiding the use of user input in HTTP requests, and applying least privilege to the IAM role associated with the Flask application. Do not forward user-controlled values to external URLs, and never allow user input to directly dictate DynamoDB table names or key expressions without an allowlist. Below are concrete code examples for a Flask application using the AWS SDK for Python (Boto3) with DynamoDB.

Example 1: Safe DynamoDB GetItem with validated input

import boto3
from flask import Flask, request, jsonify

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

# Allowlist of permitted tables
ALLOWED_TABLES = {'users', 'products'}

@app.route('/item')
def get_item():
    table_name = request.args.get('table')
    key = request.args.get('key')

    if table_name not in ALLOWED_TABLES:
        return jsonify({'error': 'table not allowed'}), 400
    if not key or not isinstance(key, str):
        return jsonify({'error': 'invalid key'}), 400

    table = dynamodb.Table(table_name)
    response = table.get_item(Key={'id': key})
    item = response.get('Item')
    if item is None:
        return jsonify({'error': 'not found'}), 404
    return jsonify(item)

Example 2: Safe DynamoDB Query with parameterized expression

@app.route('/search')
def search_items():
    table_name = request.args.get('table')
    filter_value = request.args.get('value')

    if table_name not in ALLOWED_TABLES:
        return jsonify({'error': 'table not allowed'}), 400

    table = dynamodb.Table(table_name)
    try:
        response = table.query(
            KeyConditionExpression='category = :val',
            ExpressionAttributeValues={':val': filter_value}
        )
    except Exception as e:
        return jsonify({'error': str(e)}), 400

    return jsonify(response.get('Items', []))

Example 3: Avoiding SSRF in external fetches

If you must fetch from an external source, validate and sanitize the target URL rigorously. Do not allow open redirects or internal IPs.

import requests
from urllib.parse import urlparse

INTERNAL_IPS = {'127.0.0.1', '169.254.169.254'}

def is_safe_url(url: str) -> bool:
    parsed = urlparse(url)
    if parsed.scheme not in {'http', 'https'}:
        return False
    if parsed.hostname in INTERNAL_IPS:
        return False
    # optionally allowlist specific domains
    return True

@app.route('/fetch')
def fetch_data():
    target = request.args.get('url')
    if not is_safe_url(target):
        return jsonify({'error': 'invalid url'}), 400
    try:
        resp = requests.get(target, timeout=5)
        resp.raise_for_status()
        return jsonify({'data': resp.text[:200]})
    except requests.RequestException:
        return jsonify({'error': 'fetch failed'}), 400

IAM and network hardening

• Assign the Flask instance an IAM role with minimal permissions: only the required DynamoDB actions on specific resources.
• Use VPC endpoints for DynamoDB to restrict traffic to AWS-controlled networks and prevent exposure to the public internet.
• Enable AWS CloudTrail and monitor for unusual API activity from the Flask application’s IAM role.