Formula Injection in Flask with Dynamodb

Formula Injection is a class of injection that occurs when untrusted data is interpreted as a formula, expression, or function call by downstream systems such as spreadsheets, BI tools, or parsers. While the injection target is commonly a spreadsheet endpoint, the root cause often originates in an API that exposes user-controlled data without validation. In a Flask application using Amazon DynamoDB as the backend, risk arises when string attributes retrieved from DynamoDB are later embedded into computed fields, exported to spreadsheets, or processed by libraries that evaluate expressions.

Consider a Flask route that exports user-supplied data to a CSV intended for spreadsheet import. If a DynamoDB item contains a field such as formula or discount provided by an attacker, and the Flask app writes these values directly into rows without sanitization, spreadsheet applications may interpret strings like =cmd|' /C calc'!A0 as executable formulas. The vulnerability is not in DynamoDB itself, but in how the Flask layer packages data for downstream consumption. A typical pattern:

import boto3
from flask import Flask, jsonify

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

@app.route('/item/')
def get_item(item_id):
    resp = table.get_item(Key={'id': item_id})
    item = resp.get('Item', {})
    # item may contain user-controlled fields like 'label' or 'formula_tag'
    return jsonify(item)

If item is later exported to Excel and a cell contains =HYPERLINK("http://evil"), the spreadsheet becomes a delivery mechanism for phishing. Additionally, if the Flask API includes computed fields assembled from DynamoDB attributes, an attacker can supply values such as "price": "1000000*999999" that cause arithmetic or precision abuse when processed by reporting tools.

Another scenario involves BI or analytics integrations. A Flask service might stream DynamoDB records into a visualization tool that supports embedded expressions. If user data populates column headers or metric expressions without normalization, malicious payloads can alter query behavior or execute unwanted operations in the analytics layer. The DynamoDB scan or query itself is not vulnerable; the exposure occurs when Flask exposes concatenated or semi-trusted data downstream.

Because middleBrick tests unauthenticated endpoints and inspects data exposure, it can surface findings where exported or reflected data from DynamoDB lacks encoding or validation. Findings typically highlight missing output encoding, missing integrity checks, and lack of input validation on fields that may be used in formulas. MiddleBrick also flags risky integrations such as LLM endpoints that may receive raw DynamoDB output, where prompt injection probes can attempt to coax model behavior using formula-like payloads.

Remediation focuses on strict input validation, output encoding for the target context, and defensive handling of DynamoDB attributes. Never trust values stored in DynamoDB; treat them as untrusted input even if they originate from internal services.

Input validation and sanitization

Validate and sanitize all data coming from DynamoDB as well as incoming request parameters. For string fields that must be safe for spreadsheet export, reject or encode characters that can change the interpretation of a formula.

import re
from flask import escape

SAFE_REF_PATTERN = re.compile(r'^[A-Za-z0-9 _\-.:,]+$')

def is_safe_ref(value):
    return isinstance(value, str) and bool(SAFE_REF_PATTERN.fullmatch(value))

Use this validator on fields that may be used as references, labels, or formulas. For numeric fields, enforce type and range checks before using them in computed expressions.

Output encoding for spreadsheets and BI

When generating CSV, TSV, or Excel-compatible output, ensure that values that could be interpreted as formulas are prefixed with a safe escape character (e.g., a single quote ' in CSV for Excel) or encoded appropriately.

import csv
import io
from flask import Response

def make_safe_csv(items):
    output = io.StringIO()
    writer = csv.writer(output, quoting=csv.QUOTE_MINIMAL)
    writer.writerow(['id', 'label', 'value'])
    for item in items:
        label = item.get('label', '')
        # Force Excel to treat the cell as text if it looks like a formula
        safe_label = label if not label.startswith('=') else "'" + label
        writer.writerow([item['id'], safe_label, item.get('value', '')])
    return Response(output.getvalue(), mimetype='text/csv')

This ensures that a value like =HYPERLINK("http://evil") is written as '=HYPERLINK("http://evil") and Excel does not execute it.

Defensive DynamoDB integration in Flask

Use strongly typed access patterns and avoid concatenating raw attribute values into expressions. Below is a complete example that retrieves an item, validates critical fields, and returns safe JSON.

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

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

NUMERIC_PATTERN = re.compile(r'^-?\d+(\.\d+)?$')

def validate_item(item):
    if 'price' in item:
        p = str(item['price'])
        if not NUMERIC_PATTERN.fullmatch(p):
            return False
    if 'label' in item:
        if not isinstance(item['label'], str) or not item['label'].strip():
            return False
    return True

@app.route('/item/')
def get_item(item_id):
    try:
        resp = table.get_item(Key={'id': item_id})
    except Exception:
        abort(500, 'Database error')
    item = resp.get('Item')
    if not item:
        abort(404, 'Not found')
    if not validate_item(item):
        abort(400, 'Invalid item data')
    # Safe to use; encoding handled at export time if needed
    return jsonify(item)

For the Pro plan, continuous monitoring can be added to detect when new attributes appear in DynamoDB items that may introduce unexpected formula-like content. In CI/CD, the GitHub Action can fail builds if exported data patterns resemble formula syntax without encoding.

Finally, apply principle of least privilege to the IAM role used by Flask so it can only access the minimal DynamoDB table and attributes required, reducing the impact of any downstream exposure.