Doccano Default Login Scanner
This scanner detects the use of Doccano in digital assets. It checks for default administrator credentials that could allow unauthorized administrative access.
Short Info
Level
Single Scan
Single Scan
Can be used by
Asset Owner
Estimated Time
10 seconds
Time Interval
12 days 20 hours
Scan only one
Domain, Subdomain, IPv4
Toolbox
The Doccano platform is used by data scientists and developers to label data for machine learning projects. It is an open-source data labeling and annotation tool that is widely used across various industries due to its user-friendly interface and extensive features. Organizations use Doccano to streamline the data annotation process, enhancing the accuracy of machine learning models. Developed by Doccano team, it is favored in academia, healthcare, and tech industries for tagging and categorizing data at scale.
The detected vulnerability involves the platform utilizing default login credentials which poses significant security risks. Attackers can exploit these default credentials to gain full administrative access. The vulnerability is common in systems that have not gone through proper configuration checks post-deployment. This detection aims to identify such overlooked setups, emphasizing the importance of secure authentication practices. Understanding default vulnerabilities is crucial for maintaining secure environments against unauthorized access.
In technical terms, the vulnerability exists because the system uses hard-coded credentials ('admin:password') that are easily exploitable. Malicious actors can perform unauthorized actions due to the lack of secure authentication processes. The scanner sends specific HTTP requests to determine if default credentials are in use. By evaluating response codes and body contents, it confirms whether unauthorized access is possible. The detection script scrutinizes key endpoints responsible for transmitting sensitive authentication tokens, ensuring proper validation against administration roles.
If exploited, unauthorized individuals can manipulate or steal sensitive tagged data, misleading machine learning models. Such actors can alter administrative settings, impacting system operations and potentially breaching confidential datasets. In a business context, this might lead to data breaches, financial losses, reputational damage, and legal implications. Therefore, identifying and mitigating such vulnerabilities is critical to limiting exposure to fraudulent activities.
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