S4E

Mako Exposure Scanner

This scanner detects the use of Mako Log Exposure in digital assets. It identifies potential log exposure vulnerabilities in Mako templates that could expose sensitive information and errors to unauthorized users.

Short Info


Level

Informational

Single Scan

Single Scan

Can be used by

Asset Owner

Estimated Time

10 seconds

Time Interval

8 days 15 hours

Scan only one

URL

Toolbox

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Mako is a template language used primarily for Python applications. It allows developers to generate HTML, XML, and other text-based formats seamlessly. Mako is popular for its speed and syntax simplicity, making it preferred in web application development. It is commonly used by developers in web frameworks to dynamically generate web page content. While providing a powerful templating engine, it is crucial for developers to handle errors appropriately to avoid unintended information disclosure. Mako is designed for projects that need highly scalable and maintainable template-driven text generation.

Log exposure vulnerabilities, such as those detectable in Mako templates, occur when server or application errors inadvertently reveal sensitive information via logs. These errors often include stack traces, application paths, or other details useful to attackers. Detecting log exposure is crucial, as attackers can use this information for reconnaissance or to further exploit vulnerabilities. Mako Runtime Errors, when exposed in logs, can provide insights into the application's internal workings. This vulnerability is a result of improper handling of exceptions and lack of sanitization of error messages.

In the context of Mako templates, log exposure typically occurs at runtime when the templates encounter errors. Mako’s error-handling mechanism can sometimes relay detailed stack traces and debugging information back to the user. The vulnerability is often found when exception messages from Mako are not adequately filtered or masked before reaching the client. Endpoints serving templates without sufficient error management practices are highly susceptible. Specific parameters or functions within the templates that handle dynamic data can become points of failure, leading to log exposure.

If exploited, log exposure can lead to significant information leakage, potentially revealing sensitive information such as configuration paths, system architecture, and underlying framework details. Attackers gaining this insight can exploit other application vulnerabilities more effectively. It also presents risks related to gaining insights into access patterns, server configurations, and possible authentication details embedded within error messages. Moreover, exposed logs may aid in mapping out the server environment, making it easier to plan further attacks or conduct malicious activities.

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