Formula Injection in Cassandra

Formula Injection in Cassandra contexts occurs when an application stores unsanitized, formula-based payloads in Cassandra tables, which are later exported to spreadsheet applications (CSV, Excel) where they execute as commands. Unlike traditional SQL injection targeting CQL syntax, this attack exploits the downstream consumption of data. Cassandra's role is as a storage vector; the vulnerability originates in the application layer that writes user input to Cassandra without validation and later exports it.

Attack Pattern Example: An attacker submits a payload like =cmd|'/c calc'!A1 through an API endpoint. If the application stores this string in a Cassandra column (e.g., user_notes) via an unparameterized CQL query, and an admin later exports the table to CSV, opening the file in Excel will trigger command execution. The payload leverages spreadsheet software's formula interpretation, not Cassandra itself.

Cassandra-Specific Code Vulnerabilities:

• String Concatenation in CQL: Using driver execute() with concatenated strings instead of prepared statements. Example in Java with DataStax driver:

// VULNERABLE: Direct string concatenation
String userInput = request.getParameter("note");
String cql = "INSERT INTO notes (id, content) VALUES (1, '" + userInput + "')";
session.execute(cql);

• Lack of Input Sanitization: Applications often treat Cassandra as a blind data store, assuming stored data is harmless. This ignores that exported data may be parsed by formula-aware applications.

Data Flow: User Input → Application (no validation) → Cassandra (stores raw string) → Export to CSV/Excel → Formula execution. The risk is exacerbated when Cassandra holds data from multiple sources (APIs, user forms) that feed reporting pipelines.

Detecting Formula Injection risks in a Cassandra-backed system requires analyzing both the data storage patterns and the API endpoints that write to Cassandra. middleBrick's Input Validation check identifies unparameterized CQL queries that could store malicious payloads. It also scans API responses for suspicious patterns that might indicate stored formulas (e.g., strings starting with =, +, -, @).

Manual Detection:

• Review application code for CQL queries built via string concatenation or formatting. Look for session.execute(String) with dynamic content.
• Inspect Cassandra tables for columns storing free-form text (e.g., comments, description) that accept API input. Query sample data: SELECT * FROM table WHERE column LIKE '=cmd%'.
• Check export workflows (reports, admin panels) that generate CSV/Excel files from Cassandra data without sanitization.

Scanning with middleBrick: Submit the API endpoint that writes to Cassandra. The scanner's black-box approach tests input vectors by sending payloads like =1+1 or =cmd|' /c calc'!A1 and analyzing how the API stores and later serves the data. A finding under Input Validation with a description like "Potential formula injection payload stored" indicates that the API accepts and persists dangerous characters without sanitization, which could be exploited via export.

Example Scan Command (CLI):

middlebrick scan https://api.example.com/notes
# Output includes: [Input Validation] Stored payloads with leading '=' detected in response

The scanner correlates runtime findings with the OpenAPI spec to identify which parameters map to Cassandra-bound columns, providing prioritized remediation guidance.

Remediation focuses on two layers: securing the write path to Cassandra and sanitizing data during export. The primary fix is to use prepared statements with bound parameters in the application code, ensuring user input is treated as data, not executable content.

Code Fixes Using Cassandra Drivers:

• Java (DataStax Driver):

// SAFE: Using prepared statements
PreparedStatement ps = session.prepare(
    "INSERT INTO notes (id, content) VALUES (?, ?)"
);
BoundStatement bs = ps.bind(1, userInput);
session.execute(bs);

• Python (cassandra-driver):

# SAFE: Parameterized query
from cassandra.query import SimpleStatement
query = "INSERT INTO notes (id, content) VALUES (%s, %s)"
session.execute(query, (1, user_input))

Additional Sanitization for Export: If the application generates CSV/Excel files from Cassandra data, escape or prefix dangerous characters in the output layer. For CSV, prefix =, +, -, @ with a single quote (') or space. Example in a reporting service:

def sanitize_for_csv(value):
    if value and value[0] in ('=', '+', '-', '@'):
        return "'" + value
    return value

# Apply when writing rows from Cassandra result sets
for row in result:
    csv_row = [sanitize_for_csv(row.content) for ...]

Cassandra Configuration Note: While Cassandra itself has no built-in formula sanitization, using the ALLOW FILTERING caution is unrelated. Focus on application-level controls. middleBrick's remediation guidance maps these fixes to OWASP API Top 10:2023 A03:2021 – Injection and A05:2021 – Security Misconfiguration.

Testing Remediation: After implementing prepared statements, rescan the API with middleBrick. The Input Validation finding should disappear. Also, test export workflows manually with payloads like =cmd|'/c calc'!A1 to confirm sanitization.