Skip to content

Latest commit

 

History

History
138 lines (130 loc) · 13.1 KB

vulnerabilities-workflow-1.md

File metadata and controls

138 lines (130 loc) · 13.1 KB

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

Combined Vulnerability List

This document outlines the identified vulnerabilities within the project. Each vulnerability is detailed with its description, potential impact, rank, existing and missing mitigations, preconditions for exploitation, source code analysis, and a security test case to validate its presence.

  • Vulnerability: Insecure Remote Script Execution in Docker Build Process

    • Description: The Dockerfiles for both Django and Caddy (for example, in /docker/django/Dockerfile) use commands that pipe remote content directly into an interpreter. In particular, commands such as 
      curl -sSL 'https://install.python-poetry.org' | python -
      and 
      curl -o /usr/local/bin/tini -sSLO "https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${dpkgArch}"
      download and immediately execute remote scripts and binaries. An attacker with man‐in‐the‐middle capabilities (or one who compromises the remote host) could intercept these HTTPS requests (especially if strict certificate pinning or integrity verification is not applied) and substitute a malicious payload. When the build process runs these commands, the attacker’s code would be executed inside the container, thereby compromising the build and later the production runtime. To trigger this vulnerability, an attacker would need to intercept the HTTPS connection during the container build (for example, by controlling a proxy between the CI/CD system and the internet) and serve a malicious version of the installer or binary.
    • Impact: If exploited, the attacker could run arbitrary code in the build environment. This in turn can lead to compromised container images (or even a full container takeover), which may then be deployed in production—enabling data exfiltration, persistent backdoors, or further lateral movement in the network.
    • Vulnerability Rank: High
    • Currently Implemented Mitigations:
      • The commands use HTTPS (via the -sSL flags), which provides basic transport encryption.
      • Builds run in isolated containerized environments.
    • Missing Mitigations:
      • No certificate pinning or cryptographic integrity checking is performed on the downloaded scripts/binaries.
      • There is no fallback or verification step (such as comparing an expected checksum or signing) to ensure that the downloaded content is authentic.
    • Preconditions:
      • The attacker must be positioned to perform a man‐in‐the-middle attack (for example, by controlling network traffic between the build server and the remote host or by compromising the remote host itself).
      • The build process must run in an environment where interception is possible (CI/CD systems without strict TLS validation on outbound requests).
    • Source Code Analysis:
      • In /docker/django/Dockerfile:
        • The command 
          curl -sSL 'https://install.python-poetry.org' | python -
          downloads and immediately executes the installation script for Poetry without verifying its integrity.
        • Similarly, Tini is downloaded using 
          curl -o /usr/local/bin/tini -sSLO "https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${dpkgArch}"
          without any hash or digital signature check.
      • No additional steps are taken (e.g., comparing file hashes or verifying signatures) that would protect against the content being tampered with in transit.
    • Security Test Case:
      1. Set up an intercepting proxy (using a tool such as mitmproxy) between the build environment and the internet.
      2. Configure the build environment (or CI/CD system) to route its HTTPS traffic through the proxy.
      3. Using the proxy, intercept requests made by the Docker build during the execution of the curl commands, and provide a modified (but benign and detectable) payload in place of the genuine installer or binary.
      4. Execute a Docker image build using the project’s Dockerfile.
      5. Verify that the altered payload is executed (e.g., by logging a custom message or altering a file in the container) thus proving that manipulation is possible during the build stage.

  • Vulnerability: Incomplete Secret Key Replacement in Environment Configuration

    • Description: The project’s post‐generation hook in hooks/post_gen_project.py is responsible for “securing” the generated configuration by replacing a placeholder string (__CHANGEME__) with a cryptographically secure random secret key. This is performed by the function _create_secret_key, which reads the configuration file and executes: 
      file_contents = config_file.read_text().replace(CHANGEME, secret_key, 1)
      However, by using a replacement count of “1”, only the first occurrence of the insecure placeholder is replaced. If the source template (typically config/.env.template) contains multiple occurrences of __CHANGEME__, subsequent occurrences will remain unchanged (and insecure), causing the application to use the default value for critical security functions such as session signing. To trigger this vulnerability, an attacker (or an inattentive developer) could supply — intentionally or by oversight — a .env.template file that contains more than one instance of __CHANGEME__. After project generation, some parts of the configuration would still have the predictable default value.
    • Impact: If parts of the configuration continue to use an insecure secret key, attackers might be able to forge session cookies or otherwise compromise the integrity of cryptographic signing, leading to session hijacking, data tampering, or broader application compromise.
    • Vulnerability Rank: High
    • Currently Implemented Mitigations:
      • The post‐generation hook does generate a secure random string and replaces one occurrence of the placeholder automatically during project creation.
    • Missing Mitigations:
      • The implementation does not check whether the placeholder exists only once. There is no validation to ensure that all occurrences of __CHANGEME__ have been replaced.
      • No post-processing verification is performed on the generated configuration file to detect if any insecure default values remain.
    • Preconditions:
      • The .env.template file (or other configuration files) contains more than one instance of __CHANGEME__.
      • The post-generation hook is executed without additional verification that all sensitive placeholders are replaced.
    • Source Code Analysis:
      • In hooks/post_gen_project.py, observe the snippet: 
        def _create_secret_key(config_path: Path) -> None:
    # Generate a SECRET_KEY that matches the Django standard
    secret_key = _get_random_string()

    with config_path.open(mode='r+', encoding='utf8') as config_file:
        # Replace CHANGEME with SECRET_KEY (only the first occurrence)
        file_contents = config_file.read_text().replace(CHANGEME, secret_key, 1)

        # Write the results to the file:
        config_file.seek(0)
        config_file.write(file_contents)
        config_file.truncate()
      • The use of replace(CHANGEME, secret_key, 1) means that if the template unexpectedly contains two or more instances of __CHANGEME__, only the first is secured.
    • Security Test Case:
      1. Modify the project’s .env.template file to include several instances of __CHANGEME__ (for example, on separate lines or within multiple settings).
      2. Run the cookiecutter project generation process.
      3. Open and inspect the generated config/.env file and verify whether all occurrences of __CHANGEME__ have been replaced with a random string.
      4. Confirm that one or more instances remain unchanged.
      5. Attempt to use the insecure value in a controlled Django environment (for example, by forging session cookies) to demonstrate that predictable secret keys are still in use; this confirms the potential impact from the vulnerability.

  • Vulnerability: Missing Rate Limiting in Caddy Configuration

    • Description: The default Caddy configuration for production deployments in wemake-django-template does not include rate limiting. This absence allows external attackers to perform brute-force attacks against login forms, API endpoints, or other sensitive application areas without significant impediment. By sending a high volume of requests in a short timeframe, attackers can attempt to guess credentials or exploit vulnerabilities, as there are no mechanisms to block or throttle their access attempts.
    • Impact: High. The lack of rate limiting exposes the application to brute-force attacks, potentially leading to unauthorized access, account compromise, and data breaches. Successful brute-force attacks can severely impact confidentiality, integrity, and availability of the application and its data.
    • Vulnerability Rank: High
    • Currently Implemented Mitigations: None in the default Caddy configuration provided by the template. While the template's Dockerfile for Caddy includes the rate limiting plugin, it is not activated or configured in the default setup.
    • Missing Mitigations: Rate limiting should be implemented and enabled by default in the Caddy configuration to protect against brute-force attacks. A basic rate limiting configuration should be included in the Caddyfile template.
    • Preconditions:
      • The application must be deployed using the production Docker Compose setup as provided by the wemake-django-template.
      • The deployed application must be publicly accessible over the internet (e.g., ports 80 and 443 are exposed).
      • The application must have some form of authentication or an endpoint susceptible to brute-force attacks (e.g., a login form, API authentication).
    • Source Code Analysis:

      1. Dockerfile ({{cookiecutter.project_name}}/docker/caddy/Dockerfile):

        • The Dockerfile for Caddy is designed to include the caddy-ratelimit plugin.
        • 

          • 

        

        FROM caddy:2.8.4-builder AS builder
        

        Add rate_limit plugin
        

        RUN xcaddy build --with github.com/mholt/caddy-ratelimit
        

        FROM caddy:2.8.4
        

        Copy custom Caddy
        

        COPY --from=builder /usr/bin/caddy /usr/bin/caddy
        

        - This confirms that the capability for rate limiting is built into the custom Caddy image.

        

        2. 

      2. 

        Caddy Configuration ({{cookiecutter.project_name}}/docker/caddy/Caddyfile):
        

          

        • The template does not include a default Caddyfile within the project files.
          • 

        • The docker-compose.prod.yml file mounts ./docker/caddy/Caddyfile:/etc/caddy/Caddyfile, expecting a configuration file to be present at that location.
          • 

        • Without a default Caddyfile provided by the template, users must manually create and configure it, including rate limiting.
          • 

        • Visualization:
          • 

        

        [User] ---Requests---> [Public Internet] ---Port 80/443---> [Caddy (No Rate Limit Config)] ---Proxy---> [Django Application]

        

          

        • The diagram illustrates the request flow reaching the Django application through Caddy, but without any rate limiting implemented in Caddy in the default template configuration.
          • 

        

        3. 

      3. 

        Docker Compose Production Configuration ({{cookiecutter.project_name}}/docker-compose.prod.yml):
        

          

        • The production Docker Compose setup uses the custom Caddy image built with the rate limiting plugin.
          • 

        • It mounts a Caddyfile from the host to /etc/caddy/Caddyfile in the container.
          • 

        • However, it relies on the user to provide this Caddyfile with the necessary rate limiting directives.
          • 

        

        4. 

      

      • 

    • Security Test Case:

        

      1. Deploy the generated project in production mode using docker compose -f docker-compose.yml -f docker/docker-compose.prod.yml up --build. Ensure the application is accessible via a public IP or domain.
        2. 

      2. Identify a publicly accessible endpoint that requires authentication or could be targeted for brute-force attacks. For example, the Django admin login page /admin/.
        3. 

      3. Use a brute-force tool such as hydra or Burp Suite Intruder. Configure the tool to send a rapid series of login requests with incorrect credentials to the target endpoint (e.g., /admin/login/).
        4. 

      4. Monitor the server's response. Observe that the server processes and responds to each request without blocking or delaying subsequent requests, even when thousands of failed attempts are made in quick succession.
        5. 

      5. Expected result: The server should respond to all brute-force attempts, demonstrating the absence of rate limiting. This confirms the vulnerability as an attacker can continue to make unlimited login attempts.
        6. 

      6. To verify mitigation (after implementing rate limiting): Repeat steps 1-3 after configuring rate limiting in Caddyfile.
        7. 

      7. Expected mitigated result: After a certain number of failed attempts from the same IP within a defined time window, Caddy should start to delay or block requests, preventing effective brute-force attacks. For example, requests after exceeding the limit might be rejected with a 429 "Too Many Requests" error, or significantly delayed.
        8. 

      

      • 

    

    •