Command Injection in Flask with Dynamodb

Command Injection occurs when untrusted input is concatenated into system or shell commands. In a Flask application that interacts with DynamoDB, the risk typically arises not from DynamoDB itself, but from how the application builds commands for auxiliary services or infrastructure tooling. For example, an endpoint that accepts a user-supplied table_name or key_condition_expression and passes it to a shell-based utility (such as AWS CLI via subprocess) can become vulnerable.

Consider a Flask route that uses subprocess.run to call the AWS CLI to describe a DynamoDB table based on user input:

import subprocess
from flask import request, jsonify

@app.route('/describe-table', methods=['GET'])
def describe_table():
    table_name = request.args.get('table', 'default_table')
    # Vulnerable: unsanitized input used in shell command
    result = subprocess.run(['aws', 'dynamodb', 'describe-table', '--table-name', table_name], capture_output=True, text=True)
    return jsonify({'output': result.stdout})

If an attacker provides table_name as valid_table; cat /etc/passwd, the shell command becomes two commands, leading to unintended data exposure. Even if the DynamoDB API call is properly parameterized, using shell injection-prone helpers (like os.system or unsanitized subprocess calls) introduces a path for command injection. DynamoDB data is not directly at risk from injection into the NoSQL layer, but the surrounding infrastructure and tooling can be compromised.

Another scenario involves generating AWS resource identifiers for logging or tagging. If a Flask endpoint constructs a DynamoDB ARN using unvalidated input and then passes it to a script or external process, injection can occur at that boundary. For instance:

import subprocess
from flask import request

table = request.args.get('table', 'users')
arn = f'arn:aws:dynamodb:us-east-1:123456789012:table/{table}'
subprocess.run(['custom-tool', '--resource', arn])

Here, if table includes shell metacharacters, the injected payload may execute on the host. DynamoDB itself does not interpret shell commands; the vulnerability is in the command construction layer, but DynamoDB identifiers become the vector because they flow through the same pipeline.

To mitigate, avoid shell invocation entirely. Use the AWS SDK directly, which handles request serialization and signing safely. If shell usage is unavoidable, rigorously validate and sanitize inputs, and prefer passing arguments as a list to subprocess.run rather than a shell string. This ensures each argument is treated as a separate token, preventing the shell from interpreting metacharacters.

Remediation centers on removing shell interpretation and using the DynamoDB API safely. The AWS SDK for Python (Boto3) does not allow command injection because it sends structured HTTP requests. Replace subprocess calls with Boto3 resource access.

Secure Flask example using Boto3:

import boto3
from flask import abort, jsonify, request

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

@app.route('/describe-table', methods=['GET'])
def describe_table():
    table_name = request.args.get('table')
    if not table_name or not table_name.isalnum():
        abort(400, 'Invalid table name')
    try:
        table = dynamodb.Table(table_name)
        desc = table.describe()
        return jsonify(desc)
    except Exception as e:
        return jsonify({'error': str(e)}), 500

This approach validates the table name to contain only alphanumeric characters, avoiding path traversal or shell metacharacters. It also uses the SDK directly, which eliminates shell injection risk. For more complex queries, construct expression attribute values and names programmatically:

@app.route('/query', methods=['POST'])
def query_items():
    data = request.get_json()
    table_name = data.get('table')
    partition_key = data.get('pk')
    if not table_name or not table_name.isalnum():
        abort(400, 'Invalid table name')
    if not partition_key:
        abort(400, 'Missing partition key')
    try:
        table = dynamodb.Table(table_name)
        response = table.query(
            KeyConditionExpression=boto3.dynamodb.conditions.Key('pk').eq(partition_key)
        )
        return jsonify(response['Items'])
    except Exception as e:
        return jsonify({'error': str(e)}), 500

If you must interface with external tools, avoid building command strings. Instead, write parameters to a temporary file and reference the file path, or use structured APIs. For continuous monitoring, integrate the CLI tool via the middlebrick CLI to scan endpoints for such risky patterns:

middlebrick scan https://api.example.com/openapi.json

Use the GitHub Action to enforce security gates in CI/CD, and the MCP Server to scan APIs directly from your AI coding assistant while developing. These integrations help detect insecure command construction patterns before deployment.