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Here is the combined list of vulnerabilities, formatted as requested:

Combined Vulnerability List

This document outlines the security vulnerabilities identified across the provided lists. Each vulnerability is detailed with its description, potential impact, severity ranking, current mitigations, missing mitigations, preconditions for exploitation, source code analysis, and a security test case to verify its existence.

Insecure PostgreSQL Authentication Configuration in Docker Compose

  • Vulnerability Name: Insecure PostgreSQL Authentication Configuration in Docker Compose

  • Description: The project's Docker Compose configuration (/code/docker-compose.yml) sets up a PostgreSQL database with the POSTGRES_HOST_AUTH_METHOD environment variable set to "trust". This configuration bypasses password authentication, allowing any connection to the database without credentials. An attacker who can reach the exposed PostgreSQL port (5432, as mapped in the Docker Compose file) can connect and execute arbitrary SQL commands. This vulnerability arises from the direct exposure of an unsecured PostgreSQL instance to potential network access.

    Step-by-step exploitation process:

    1. An attacker scans publicly accessible IPs and identifies port 5432 as open, indicating a PostgreSQL service.
    2. The attacker uses a PostgreSQL client (like psql) to connect to the target IP and port using the default PostgreSQL user (postgres) and database name (modelutils as per the Docker Compose file), without providing a password: psql -h <target_ip> -U postgres modelutils.
    3. The connection succeeds due to the "trust" authentication method.
    4. Once connected, the attacker can perform any database operations, including reading, modifying, or deleting data.
    5. In a severe scenario, the attacker could exfiltrate sensitive data, drop database tables, or otherwise disrupt the application's data integrity and availability.

  • Impact: Unauthenticated access to the PostgreSQL database grants an attacker complete control over the data. This can lead to severe consequences, including:

    • Data Breach: Confidential information stored in the database can be accessed and stolen.
    • Data Manipulation: Critical data can be altered or deleted, compromising data integrity and application functionality.
    • Service Disruption: Database operations can be disrupted, leading to application downtime or malfunction.
    • Reputational Damage: A data breach or service disruption can severely damage the reputation of the application and its developers.

  • Vulnerability Rank: Critical

  • Currently Implemented Mitigations: No explicit mitigations are present in the provided project files. The Docker Compose configuration directly sets POSTGRES_HOST_AUTH_METHOD to "trust" and exposes port 5432 without any additional security measures.

  • Missing Mitigations: Several crucial mitigations are absent and should be implemented:

    • Secure Authentication Method: Replace "trust" with a secure authentication method. Use POSTGRES_PASSWORD to set a strong, randomly generated password for the PostgreSQL user. Consider using "md5" or "scram-sha-256" for password hashing.
    • Restrict Network Access: Limit network access to the PostgreSQL port. In production environments, the port should not be publicly exposed. Consider removing the port mapping (ports: - 5432:5432) or mapping it to localhost only, ensuring only services within the Docker network can access it. Use firewall rules to further restrict access if necessary.
    • Environment-Specific Configurations: Employ different Docker Compose configurations for development, testing, and production environments. Development/testing configurations using "trust" should never be used in production. Use environment variables or separate configuration files to manage these differences.

  • Preconditions:

    • The Docker Compose configuration with POSTGRES_HOST_AUTH_METHOD: trust is deployed.
    • Port 5432 of the container is reachable from outside the host's secure network perimeter, either due to direct exposure or inadequate network segmentation.

  • Source Code Analysis: The vulnerability is directly configured in the /code/docker-compose.yml file within the postgres service definition:

    services:
  postgres:
    image: postgres:13-alpine
    environment:
      POSTGRES_HOST_AUTH_METHOD: trust
      POSTGRES_DB: modelutils
      POSTGRES_USER: postgres
    ports:
    - 5432:5432

    The line POSTGRES_HOST_AUTH_METHOD: trust explicitly disables password authentication for PostgreSQL. Combined with the port mapping 5432:5432, which exposes the database port to the host and potentially the public network, this configuration directly enables unauthenticated access.

  • Security Test Case:

    1. Preparation: Deploy the application using the provided Docker Compose file, ensuring the PostgreSQL container is running and port 5432 is accessible from an external machine (or simulate external access within a test network).
    2. Execution: From a machine outside the Docker host's security perimeter, use a PostgreSQL client (e.g., psql) to attempt a connection to the exposed port without providing a password: psql -h <target_ip> -U postgres modelutils. Replace <target_ip> with the public IP or resolvable hostname where the Docker application is deployed.
    3. Expected Result: The psql command should connect successfully without prompting for a password. You should see the PostgreSQL prompt.
    4. Verification: Execute SQL commands such as \dt to list tables or SELECT version(); to confirm database access. Attempt to read data from existing tables or create new tables to further demonstrate the extent of unauthenticated access. Document the successful unauthenticated connection and execution of SQL commands as evidence of the vulnerability.

SQL Injection in JoinQueryset.join method

  • Vulnerability Name: SQL Injection in JoinQueryset.join

  • Description: The JoinQueryset.join method in managers.py constructs a raw SQL query to create a temporary table. It uses string formatting to embed the SQL query string obtained from an input queryset into a larger SQL statement. If this input queryset is crafted using user-controlled data that is not properly sanitized within this specific context, it becomes susceptible to SQL injection. An attacker can manipulate the input queryset to inject malicious SQL code. This injected code is then executed by the database when the join method is invoked, leading to a SQL injection vulnerability. The vulnerability stems from directly embedding a potentially user-influenced SQL query string into another SQL query via string formatting without proper parameterization.

    Step-by-step trigger:

    1. An attacker identifies an application endpoint that utilizes a Django model managed by JoinManager or JoinQueryset.as_manager().
    2. The application uses the join method on a queryset that is influenced by user-provided input. This influence could be through URL parameters, form data, or other mechanisms that allow users to affect the queryset's filtering or ordering.
    3. The attacker crafts a malicious input designed to inject SQL code. When this input is processed by the application and incorporated into the queryset used by join, it becomes part of the final SQL query string constructed within the join method.
    4. When join executes the constructed SQL query, the injected SQL code is executed by the database, resulting in SQL injection.

  • Impact: Successful exploitation of this SQL injection vulnerability can have severe consequences, including:

    • Critical Data Breach: Attackers can gain unauthorized access to sensitive data stored within the database, enabling them to read, extract, and exfiltrate confidential information.
    • Unauthorised Data Manipulation: Attackers can modify or delete data, leading to data integrity breaches, corruption of records, and potential disruption of application functionalities.
    • Account Takeover Potential: In certain scenarios, successful SQL injection might allow attackers to escalate their privileges or gain access to administrative accounts, granting them further control over the application and its data.
    • Denial of Service (Indirect): While explicitly excluded from the initial vulnerability scope, SQL injection can be exploited to cause database overload or performance degradation, indirectly leading to denial of service conditions.
    • Remote Code Execution (Severe Cases): In the most critical scenarios, depending on the underlying database system and its configuration, it may be possible to achieve remote code execution on the database server, allowing for complete system compromise.

  • Vulnerability Rank: Critical

  • Currently Implemented Mitigations: There are no mitigations implemented within the JoinQueryset.join method to prevent SQL injection. The method directly uses string formatting to construct the SQL query, embedding the potentially vulnerable SQL query string obtained from the input queryset without any sanitization or parameterization in the final SQL construction.

  • Missing Mitigations: The primary missing mitigation is the implementation of parameterized queries for constructing the SQL statement within the JoinQueryset.join method. Instead of relying on .format() to embed the raw SQL string, the code should utilize the database cursor's execute method with the SQL query and parameters obtained from qs.query.sql_with_params(). This approach ensures that user-provided data is treated as data and properly escaped, preventing it from being interpreted as executable SQL code.

  • Preconditions:

    1. The Django project uses models configured with JoinManager or JoinQueryset.as_manager().
    2. An application feature exists where the join method is called on a queryset that is derived from or influenced by user-controlled input. This could be through URL parameters, form submissions, or any other mechanism where user data can affect the initial queryset subsequently used in the join method.
    3. The application is deployed in an environment accessible to external attackers who can send requests to trigger the vulnerable code path.

  • Source Code Analysis:

    def join(self, qs: QuerySet[Any] | None = None) -> QuerySet[Any]:
    '''
    ...
    '''
    # ... [Code to prepare qs and fk_column] ...

    TABLE_NAME = 'temp_stuff'
    query, params = qs.query.sql_with_params() # <-- Get raw SQL and params
    sql = '''
        DROP TABLE IF EXISTS {table_name};
        DROP INDEX IF EXISTS {table_name}_id;
        CREATE TEMPORARY TABLE {table_name} AS {query}; # <-- Vulnerable string formatting
        CREATE INDEX {table_name}_{fk_column} ON {table_name} ({fk_column});
    '''.format(table_name=TABLE_NAME, fk_column=fk_column, query=str(query)) # <-- Vulnerability is here

    with connection.cursor() as cursor:
        cursor.execute(sql, params) # <-- Executes the constructed SQL

    # ... [Rest of the method] ...
    return new_qs

    Visualization:

    [User Input] --> [Application Logic] --> [QuerySet Construction (qs)] --> JoinQueryset.join(qs)
                                                                            |
                                                                            v
                                qs.query.sql_with_params() --> (SQL Query String, Parameters)
                                                                            |
                                                                            v
                String Formatting with SQL Query String --> [Injected SQL if qs is malicious] --> SQL Query
                                                                            |
                                                                            v
                                        cursor.execute(SQL Query, params) --> [Database Execution] --> SQL Injection

    Explanation:

    1. The join method retrieves the raw SQL query string and associated parameters from the input queryset qs using qs.query.sql_with_params().
    2. It proceeds to construct a new SQL query string designed to create a temporary table. Critically, it uses .format() to directly embed the SQL query string obtained from qs into the CREATE TEMPORARY TABLE AS clause.
    3. If the queryset qs is influenced by user input and this input is not adequately sanitized by Django's query building process, malicious SQL code can be injected into the query string.
    4. Subsequently, when cursor.execute(sql, params) is invoked, the entire constructed SQL string, including any potentially injected malicious code, is executed against the database.
    5. Although sql_with_params returns parameters, these parameters are not utilized in the string formatting process of the final SQL query. This makes the string formatting operation inherently vulnerable. The parameters returned are intended for use with the outer query, if any, but not for the inner query that is subject to string formatting.

  • Security Test Case: Objective: Demonstrate SQL injection vulnerability in JoinQueryset.join when the input queryset is manipulated with malicious SQL.

    Pre-requisites:

    1. Set up a Django project that includes the model_utils library and defines a model that uses JoinManager or JoinQueryset.as_manager(). Assume a model named MyModel.
    2. Create a view that uses JoinQueryset.join and allows user input to influence the queryset used in join. For simplicity, assume user input can control a filter on MyModel through a GET parameter.

    Steps:

    1. Craft Malicious Input: Prepare a malicious SQL injection payload. For testing, use '; SELECT pg_sleep(10); -- (for PostgreSQL, adjust for other DBs), which will cause a 10-second delay if successful.
    2. Construct Malicious URL: Create a URL with the malicious payload in a parameter that influences the queryset in JoinQueryset.join. If the view filters MyModel based on a parameter named filter_param, the URL might be: http://example.com/vulnerable_endpoint/?filter_param=malicious_payload. Assume the application filters a field 'name' in MyModel using this parameter.
    3. Send Malicious Request: Send the crafted HTTP request (using curl, wget, or a browser) to the vulnerable endpoint.
    4. Observe Response Time: Monitor the server response time. Successful SQL injection with pg_sleep(10) will cause a ~10-second delay. Normal requests should respond much faster.
    5. Verify Injection (Optional, Recommended): For stronger verification, use a payload to extract data (e.g., database version or username) and check the response for injected data. Time-based injection tests with pg_sleep are often sufficient for confirmation.

    Expected Result: Requests with the malicious payload will take significantly longer (e.g., ~10 seconds longer with pg_sleep(10)) than normal requests, indicating successful SQL injection.