Autolab is a course management service that enables auto-graded programming assignments. From Autolab versions v.3.0.0 onward students can download all assignments from another student, as long as they are logged in, using the download_all_submissions feature. This can allow for leakage of submissions to unauthorized users, such as downloading submissions from other students in the class, or even instructor test submissions, given they know their user IDs. This issue has been patched in commit 1aa4c769 which is not yet in a release version, but is expected to be included in version 3.0.3. Users are advised to either manually patch or to wait for version 3.0.3. As a workaround administrators can disable the feature.
The product does not properly prevent a person’s private, personal information from being accessed by actors who either (1) are not explicitly authorized to access the information or (2) do not have the implicit consent of the person about whom the information is collected.
There are many types of sensitive information that products must protect from attackers, including system data, communications, configuration, business secrets, intellectual property, and an individual’s personal (private) information. Private personal information may include a password, phone number, geographic location, personal messages, credit card number, etc. Private information is important to consider whether the person is a user of the product, or part of a data set that is processed by the product. An exposure of private information does not necessarily prevent the product from working properly, and in fact the exposure might be intended by the developer, e.g. as part of data sharing with other organizations. However, the exposure of personal private information can still be undesirable or explicitly prohibited by law or regulation. Some types of private information include:
Some of this information may be characterized as PII (Personally Identifiable Information), Protected Health Information (PHI), etc. Categories of private information may overlap or vary based on the intended usage or the policies and practices of a particular industry. Sometimes data that is not labeled as private can have a privacy implication in a different context. For example, student identification numbers are usually not considered private because there is no explicit and publicly-available mapping to an individual student’s personal information. However, if a school generates identification numbers based on student social security numbers, then the identification numbers should be considered private.