Formula Injection in Flask with Cockroachdb

Formula Injection (a subset of Injection and Business Logic Abuse) occurs when an attacker can control data that is interpreted as code or formula by the application or downstream systems. In a Flask application backed by CockroachDB, the risk is elevated when user input is concatenated into dynamic SQL or interpreted by spreadsheet-like operations executed on database side-effects or exported data. CockroachDB supports PostgreSQL wire protocol and standard SQL syntax; while it does not natively evaluate formulas, a Flask layer can construct SQL or computed expressions using string interpolation, and CockroachDB will execute them as written. If Flask builds queries by embedding user-controlled values directly into SQL strings, an attacker can inject crafted payloads that alter query logic, bypass authentication, or cause unexpected behavior when the data is later exported or processed by reporting tools that treat values as formulas.

Consider a Flask route that builds a dynamic SQL string to filter products by a price multiplier supplied by the user. If the input is used verbatim in SQL, an attacker can terminate the intended expression and append a new statement or condition. For example:

query = "SELECT id, name, price * " + user_multiplier + " AS adjusted_price FROM products WHERE category = 'parts'"

If user_multiplier is 1; DROP TABLE products; --, the resulting SQL becomes two statements, and CockroachDB will attempt to execute both if the driver allows multiple statements. Even if multiple statements are disabled, an attacker can still change the effective logic, for example by injecting 1 OR 1=1 to adjust price calculations for all rows or cause arithmetic errors that lead to information leakage through error messages. CockroachDB’s strict SQL compliance means that injected expressions are evaluated exactly as provided, and there is no built-in formula parsing layer that would neutralize malicious content. The database returns results that Flask may render directly, enabling stored or reflected XSS when values are displayed, or data integrity issues when injected formulas corrupt computed fields. Moreover, if Flask exports query results to CSV or spreadsheet formats for downstream analysis, values that start with = or + can be interpreted as formulas by the receiving application, creating a second-stage injection path where CockroachDB data triggers client-side formula execution.

The OWASP API Top 10 category that aligns with this pattern is Injection (API1:2023), and real-world analogs such as CVE patterns involving SQL injection via crafted input that manipulates query logic are relevant. Because middleBrick scans test input validation and property authorization across 12 checks in parallel, it can surface these classes of risk by correlating runtime behavior with OpenAPI/Swagger definitions that describe expected parameter formats. The scanner cross-references spec definitions with observed responses, highlighting cases where user-supplied data reaches SQL construction without sufficient sanitization or parameterization.

Additionally, if Flask applications expose endpoints that return computed values without integrity checks, an attacker can supply values designed to overflow numeric types or exploit floating-point rounding when CockroachDB performs arithmetic. Such issues are exacerbated when the API is unauthenticated or weakly authenticated, as the attack surface expands. middleBrick’s Authentication and BOLA/IDOR checks help identify endpoints that accept manipulated identifiers or lack proper ownership verification, further exposing formula injection risks in multi-tenant scenarios.

Remediation focuses on strict separation of code and data using parameterized queries and avoiding string-based expression building. With CockroachDB and PostgreSQL-compatible drivers, always use placeholders and pass user input as parameters. Below are concrete, working Flask examples that demonstrate safe patterns.

Safe parameterized query with psycopg2 (or compatible driver):

from flask import Flask, request
import psycopg2

app = Flask(__name__)

@app.route('/products')
def get_products():
    user_multiplier = request.args.get('multiplier', type=float, default=1.0)
    category = request.args.get('category', 'parts')
    conn = psycopg2.connect("dbname=mydb host=localhost user=myuser sslmode=require")
    cur = conn.cursor()
    # Use %s placeholders; driver handles quoting and type safety
    cur.execute("SELECT id, name, price * %s AS adjusted_price FROM products WHERE category = %s", (user_multiplier, category))
    results = cur.fetchall()
    cur.close()
    conn.close()
    return {'products': results}

Using SQLAlchemy with CockroachDB:

from flask import Flask, request
from sqlalchemy import create_engine, text
import pandas as pd

app = Flask(__name__)
engine = create_engine('cockroachdb://myuser:mypass@localhost:26257/mydb?sslmode=require')

@app.route('/products')
def get_products_sqlalchemy():
    user_multiplier = request.args.get('multiplier', type=float, default=1.0)
    category = request.args.get('category', 'parts')
    # text() with bound parameters ensures safe composition
    sql = text("SELECT id, name, price * :multiplier AS adjusted_price FROM products WHERE category = :category")
    with engine.connect() as conn:
        df = pd.read_sql(sql, conn, params={'multiplier': user_multiplier, 'category': category})
    return {'products': df.to_dict(orient='records')}

Input validation and type enforcement: Even with parameterization, constrain values to expected ranges and types. For numeric multipliers, enforce boundaries to prevent abuse that could cause excessive computation or exposure of sensitive data:

def get_positive_float(value, default=1.0):
    try:
        v = float(value)
        return v if v > 0 else default
    except (TypeError, ValueError):
        return default

Avoid dynamic SQL building entirely for filters: Do not concatenate column names or table names from user input. If dynamic sorting or column selection is required, use an allowlist:

ALLOWED_COLUMNS = {'price', 'name', 'id'}

@app.route('/products')
def safe_sort():
    sort_by = request.args.get('sort_by', 'id')
    if sort_by not in ALLOWED_COLUMNS:
        sort_by = 'id'
    # Still use parameterized queries for values, but column names must be vetted
    query = f"SELECT id, name, price FROM products WHERE category = 'parts' ORDER BY {sort_by}"
    # Execute with a trusted connection; note column names are not parameterized in SQL
    # Ensure query is built from allowlist only
    ...

These practices reduce the risk of Formula Injection by ensuring that CockroachDB receives only intended, validated expressions. middleBrick’s CLI can be used to verify that endpoints adhere to these patterns by scanning unauthenticated attack surfaces and mapping findings to the OWASP API Top 10, providing prioritized remediation guidance.