Sandbox Escape in Flask with Dynamodb

A sandbox escape in the context of a Flask application using Amazon DynamoDB occurs when attacker-influenced input traverses the application logic and reaches the DynamoDB layer in a way that violates the intended isolation boundaries. Unlike injection into a SQL database, DynamoDB is a NoSQL service, and misuse often maps to DynamoDB-specific constructs such as key conditions, expression attribute names, and filter expressions. When Flask routes build DynamoDB requests by concatenating or loosely validating user input, an attacker may manipulate parameters to change the target table, index, or key schema, or to affect conditional checks that govern access control.

In a Flask app, common routes that interact with DynamoDB include dynamic resource identifiers (e.g., /items/) or query parameters that specify a table name or partition key. If these values are passed directly into boto3 calls without strict allowlisting or canonicalization, an attacker can attempt to pivot across logical boundaries. For example, an item_id value crafted to include a newline or a reserved path segment could be interpreted by the application as a different key, enabling access to another item or table. Similarly, expression attribute names that reflect user input without strict validation can allow an attacker to reference sensitive attributes that were not intended to be included in the query.

Because DynamoDB supports condition expressions for writes and filter expressions for queries, misuse can lead to bypassing optimistic locking or authorization checks implemented via condition expressions. An attacker who can control the condition value might force a write to succeed even when it should not, or cause a query to return results that should be hidden. The Flask application may interpret a successful write or returned item as normal behavior, while the underlying request effectively escaped the logical sandbox the developer intended. This is especially risky when the same DynamoDB table stores data for multiple tenants or contexts and access control is enforced solely at the application layer rather than being embedded in the request’s key structure.

Another vector involves the use of reserved keywords in attribute names. If Flask routes construct expression attribute names dynamically from user input (for example, mapping a query parameter to an attribute name without normalization), an attacker can supply values that map to DynamoDB reserved words, causing unexpected expression evaluation or injection-like behavior. Because DynamoDB’s API is JSON-based, malformed or ambiguous input can change the semantics of the request in ways that bypass intended filters. The scanner’s checks for unsafe consumption and input validation highlight these risks by flagging missing allowlists and weak parameter sanitization in endpoints that build DynamoDB requests.

In practice, the combination of Flask’s flexible routing, boto3’s expressive but complex API, and DynamoDB’s schema-less attributes creates ample opportunity for boundary violations. The scanner’s parallel checks—particularly input validation, property authorization, and unsafe consumption—help surface these patterns by comparing the declared OpenAPI contract with runtime behavior. By correlating findings across the 12 checks, the tool can indicate when DynamoDB-specific parameters such as table names, key schemas, or expression attribute names are improperly influenced by unvalidated input, signaling a potential sandbox escape in the API surface.

Remediation focuses on strict input validation, canonicalization, and avoiding dynamic construction of key expressions. Below are concrete, secure patterns for a Flask route that retrieves an item by user-supplied key, using boto3 with DynamoDB.

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

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

# Allowlist for table names: only lowercase alphanumeric and underscore
TABLE_NAME_PATTERN = re.compile(r'^[a-z][a-z0-9_]{0,62}$')

def get_valid_table_name(table_name):
    if not TABLE_NAME_PATTERN.match(table_name):
        raise ValueError('Invalid table name')
    table = dynamodb.Table(table_name)
    return table

@app.route('/items//', methods=['GET'])
def get_item(table_name, item_id):
    # Validate table name against allowlist
    table = get_valid_table_name(table_name)

    # Validate item_id: assume simple string primary key; reject unexpected characters
    if not re.match(r'^[A-Za-z0-9\-._~]+$', item_id):
        return jsonify({'error': 'Invalid item_id'}), 400

    try:
        response = table.get_item(
            Key={
                'pk': item_id,
                'sk': 'METADATA'
            },
            # Use explicit attribute names; avoid dynamic expression attribute names
            ProjectionExpression='#dt, val',
            ExpressionAttributeNames={
                '#dt': 'data_type'
            }
        )
    except Exception as e:
        return jsonify({'error': str(e)}), 500

    item = response.get('Item')
    if not item:
        return jsonify({'error': 'Not found'}), 404
    return jsonify(item)

@app.route('/query/', methods=['POST'])
def query_items(table_name):
    table = get_valid_table_name(table_name)
    body = request.get_json(force=True)

    # Validate partition key value; do not trust client-supplied key schema
    pk = body.get('pk')
    if not pk or not re.match(r'^[A-Za-z0-9\-._~]+$', pk):
        return jsonify({'error': 'Invalid partition key'}), 400

    # Build a safe expression attribute names map from a fixed set
    safe_attr_names = {
        '#dt': 'data_type',
        '#st': 'sort_attr'
    }
    # Do not echo user input into expression attribute names
    filter_expr = body.get('filter', '#dt = :v')
    # Validate that filter references only known safe placeholders
    if not all(tok in safe_attr_names.values() for tok in filter_expr.replace(':', ' ').split()):
        return jsonify({'error': 'Invalid filter'}), 400

    try:
        response = table.query(
            KeyConditionExpression='pk = :pkval',
            FilterExpression=filter_expr,
            ExpressionAttributeNames=safe_attr_names,
            ExpressionAttributeValues={
                ':pkval': pk,
                ':v': body.get('value', 'default')
            }
        )
    except Exception as e:
        return jsonify({'error': str(e)}), 500

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

Key remediation points:

• Use an allowlist for table names and strictly validate item IDs to prevent path traversal or table switching.
• Avoid constructing ExpressionAttributeNames from user input; use a fixed mapping for known safe attribute names.
• Do not directly interpolate user input into ConditionExpressions or FilterExpressions; prefer parameterized values and validate token usage.
• Enforce schema checks on key attributes before issuing GetItem or Query requests to ensure the request targets the expected logical sandbox.

These practices reduce the risk that user-influenced data can alter the intended DynamoDB request structure, helping to contain operations within the designed authorization boundary.