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Tensorflow Tensorboard - Unauthenticated Access

By kannthu

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What is "Tensorflow Tensorboard - Unauthenticated Access?"

The "Tensorflow Tensorboard - Unauthenticated Access" module is designed to detect a misconfiguration in the Tensorboard software. Tensorboard is a web-based tool provided by TensorFlow, an open-source machine learning framework. This module focuses on identifying instances where Tensorboard is accessible without authentication, which can pose a significant security risk.

This module has a severity level of high, indicating the potential impact of unauthenticated access to Tensorboard.


Unauthenticated access to Tensorboard can allow unauthorized individuals to view sensitive information and potentially exploit the system. This can lead to unauthorized data access, data leakage, and potential security breaches.

How the module works?

The "Tensorflow Tensorboard - Unauthenticated Access" module works by sending a specific HTTP request to the target system and analyzing the response. It checks for the presence of certain keywords, such as "scalars," "loading_mechanism," and "custom_scalars," in the response body. Additionally, it verifies that the response status code is 200 (OK).

By matching these conditions, the module can determine if the target system has misconfigured Tensorboard, allowing unauthenticated access.

For example, the module may send a GET request to the path "/data/plugins_listing" and expect a response with the keywords mentioned above and a status code of 200.

If the conditions are met, the module will report a vulnerability, indicating that the target system is at risk of unauthenticated access to Tensorboard.

Module preview

Concurrent Requests (1)
1. HTTP Request template
Matching conditions
word: scalars, loading_mechanism, custom_scala...and
status: 200
Passive global matcher
No matching conditions.
On match action
Report vulnerability