Mass Assignment in Cassandra

Mass assignment vulnerabilities in Cassandra applications occur when user input is automatically bound to object properties without proper filtering. This is particularly dangerous in Cassandra environments because the database's flexible schema and dynamic query patterns can mask these vulnerabilities.

In Cassandra-based applications, mass assignment typically manifests through data transfer objects (DTOs) that map directly to database entities. When a client sends a JSON payload to create or update a record, frameworks often automatically bind all properties to the corresponding object fields. This becomes problematic when certain fields should never be user-modifiable, such as administrative flags, timestamps, or internal identifiers.

Consider a Cassandra user management system where clients can update profiles. A typical vulnerable pattern looks like this:

class UserDTO {
    public String username;
    public String email;
    public String password;
    public boolean isAdmin; // Should never be user-modifiable
    public long createdAt;   // Should never be user-modifiable
}

When this DTO is used directly with Cassandra queries, an attacker can modify properties they shouldn't have access to. The vulnerability is amplified in Cassandra because of its eventual consistency model and distributed nature—an attacker might exploit this across multiple nodes before consistency is restored.

Cassandra-specific attack patterns include:

• Modifying isAdmin flags to elevate privileges
• Changing createdAt timestamps to manipulate audit trails
• Altering userId or ownerId to access other users' data
• Modifying balance or credits fields in financial applications
• Changing role or permissions arrays to bypass authorization

The problem is exacerbated in Cassandra's data modeling patterns. Many applications use wide-row designs where a single partition contains numerous columns. Without proper filtering, mass assignment can modify dozens or hundreds of columns unintentionally.

Here's a vulnerable Cassandra query pattern:

public void updateUser(String userId, UserDTO userDto) {
    String cql = "UPDATE users SET " +
                "username = ?, email = ?, password = ?, " +
                "isAdmin = ?, createdAt = ?, lastLogin = ? " +
                "WHERE user_id = ?";
    
    session.execute(cql, userDto.username, userDto.email, 
                    userDto.password, userDto.isAdmin, 
                    userDto.createdAt, System.currentTimeMillis(),
                    userId);
}

An attacker can exploit this by sending a payload that includes isAdmin=true or manipulates timestamps, gaining unauthorized access or covering their tracks.

Detecting mass assignment vulnerabilities in Cassandra applications requires examining both the data access layer and the API endpoints that interact with Cassandra. The distributed nature of Cassandra means vulnerabilities can exist across multiple nodes and data centers.

Static code analysis for Cassandra applications should focus on:

• DTO classes that map directly to Cassandra entities
• Repository or DAO methods that accept DTOs as parameters
• Query builders that automatically include all DTO properties
• JSON deserialization that populates entity objects directly

Dynamic scanning with middleBrick specifically targets Cassandra vulnerabilities by examining:

• API endpoints that accept JSON payloads for Cassandra operations
• Response structures that reveal internal Cassandra column names
• Authentication bypass attempts on Cassandra management endpoints
• Property authorization violations in multi-tenant Cassandra schemas

middleBrick's Cassandra-specific detection includes scanning for:

Manual detection techniques for Cassandra applications:

# Check for exposed Cassandra management ports
nmap -p 9042,9160,7199 target_host

# Examine API endpoints for mass assignment patterns
# Look for endpoints that accept full entity updates
curl -X PUT http://api.example.com/users/123 \
  -H "Content-Type: application/json" \
  -d '{"username":"test","isAdmin":true,"createdAt":0}'

Security-focused code review should examine:

// Vulnerable pattern - accepts all DTO properties
public void createOrder(OrderDTO order) {
    // No filtering of properties
    session.execute(buildInsertQuery(order));
}

// Secure pattern - explicitly specifies allowed properties
public void createOrder(OrderDTO order) {
    String cql = "INSERT INTO orders (order_id, user_id, amount, status) " +
                "VALUES (?, ?, ?, ?)";
    session.execute(cql, order.getOrderId(), order.getUserId(), 
                    order.getAmount(), order.getStatus());
}

middleBrick's scanning process for Cassandra APIs includes testing for BOLA (Broken Object Level Authorization) by attempting to access or modify resources across user boundaries, which is particularly relevant in Cassandra's multi-tenant architectures.

Remediating mass assignment vulnerabilities in Cassandra applications requires a defense-in-depth approach that combines proper data modeling, explicit property mapping, and input validation.

The most effective remediation is explicit property whitelisting. Instead of accepting entire DTOs, applications should explicitly specify which properties are allowed for each operation:

public class SecureUserRepository {
    private static final Set ALLOWED_UPDATE_FIELDS = 
        Set.of("username", "email", "password");
    
    public void updateUser(String userId, Map updates) {
        // Filter to only allowed properties
        Map filteredUpdates = updates.entrySet().stream()
            .filter(e -> ALLOWED_UPDATE_FIELDS.contains(e.getKey()))
            .collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
            
        if (filteredUpdates.isEmpty()) {
            throw new IllegalArgumentException("No valid properties to update");
        }
        
        StringBuilder cql = new StringBuilder("UPDATE users SET ");
        List