Path Traversal in Flask with Dynamodb

Path Traversal in a Flask application that uses Dynamodb typically arises when user-controlled path components (e.g., a file key or object identifier) are used to construct DynamoDB key expressions or are reflected in API responses without validation. In a black-box scan, middleBrick tests inputs that flow into query parameters, headers, and body fields, then checks whether crafted payloads traverse directory boundaries or access unintended resources. Even though DynamoDB is a managed NoSQL service and does not have a filesystem, path-like inputs can still lead to authorization issues such as BOLA/IDOR or unsafe data exposure when logical references are not properly scoped.

Consider a Flask route that retrieves an object from DynamoDB using a key built from a user-supplied identifier:

@app.route('/objects/<path:obj_id>')
def get_object(obj_id):
    key = {'object_id': obj_id}
    response = table.get_item(Key=key)
    return jsonify(response.get('Item', {}))

If obj_id is not validated, an attacker can supply sequences like ../../../secret. While DynamoDB will treat the literal string as a key, a missing ownership check or misconfigured partition-key design may allow the request to resolve to another user’s item (BOLA). middleBrick’s checks include Authentication, BOLA/IDOR, and Property Authorization, and it tests whether manipulated path segments can bypass intended access boundaries. The scan also examines whether responses inadvertently disclose sensitive fields or API keys, which aligns with Data Exposure and Unsafe Consumption checks. In environments where the same DynamoDB table serves multiple tenants, improper key construction can unintentionally expose items across tenants, a pattern often flagged under BOLA/IDOR with severity high.

Moreover, if the Flask app uses the path component to build low-level SDK operations such as get_item or query, unsanitized input can affect conditional expressions or key schema usage. middleBrick’s Input Validation and Property Authorization checks look for missing type and format checks, and whether the application relies solely on path traversal prevention without server-side authorization. Because middleBrick performs unauthenticated, black-box testing, it can identify endpoints where path-like inputs reach DynamoDB key construction without proper validation or tenant isolation.

To reduce path traversal and authorization risks when using Flask with DynamoDB, enforce strict input validation, canonicalize logical references, and apply tenant or ownership checks on every request. The following patterns demonstrate secure handling of user-supplied identifiers in DynamoDB operations within Flask.

• Validate and sanitize path-like inputs: reject or encode sequences containing .. or path separators when they are not semantically meaningful, and use allowlists for expected identifier formats.
• Scope queries with the authenticated subject: include the user or tenant identifier as part of the DynamoDB key condition, ensuring that one user cannot read another’s items even if the raw key appears valid.
• Use the DynamoDB Expression Attribute Values to avoid injection-like key construction issues and always prefer strongly-typed attribute access.

Example secure Flask route with DynamoDB:

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

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

def is_safe_id(value: str) -> bool:
    # Allow only alphanumeric, underscore, and hyphen; reject path traversal patterns
    return bool(re.fullmatch(r'[A-Za-z0-9_-]+', value))

@app.route('/objects/<obj_id>')
def get_object(obj_id):
    if not is_safe_id(obj_id):
        return jsonify({'error': 'invalid object identifier'}), 400
    # Include tenant context to enforce tenant isolation
    tenant_id = get_current_tenant_id()  # implemented per your auth model
    key = {
        'partition_key': f'tenant#{tenant_id}',
        'sort_key': f'object#{obj_id}'
    }
    response = table.get_item(Key=key)
    item = response.get('Item')
    if not item:
        return jsonify({'error': 'not found'}), 404
    # Explicitly filter out sensitive fields if needed
    safe_item = {k: item[k] for k in ('sort_key', 'data') if k in item}
    return jsonify(safe_item)

When using query or scan patterns, embed tenant identifiers in the key condition expression:

def get_user_files(user_sub: str, file_name: str):
    if not is_safe_id(file_name):
        return {'error': 'invalid file name'}
    tenant_id = get_current_tenant_id()
    response = table.query(
        KeyConditionExpression='partition_key = :pk AND begins_with(sort_key, :sk)',
        ExpressionAttributeValues={
            ':pk': f'tenant#{tenant_id}',
            ':sk': f'file#{file_name}'
        }
    )
    return response.get('Items', [])

These patterns align with middleBrick’s checks for Input Validation, Property Authorization, and BOLA/IDOR by ensuring that every DynamoDB request is constrained by tenant context and strict format rules. The scanner will flag remaining issues such as missing ownership checks or unsafe consumption patterns, providing prioritized findings with severity and remediation guidance.