Exposure of Private Personal Information to an Unauthorized Actor
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:
Government identifiers, such as Social Security Numbers
Contact information, such as home addresses and telephone numbers
Geographic location - where the user is (or was)
Financial data - such as credit card numbers, salary, bank accounts, and debts
Pictures, video, or audio
Behavioral patterns - such as web surfing history, when certain activities are performed, etc.
Relationships (and types of relationships) with others - family, friends, contacts, etc.
Communications - e-mail addresses, private messages, text messages, chat logs, etc.
Health - medical conditions, insurance status, prescription records
Account passwords and other credentials
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.
The following examples help to illustrate the nature of this weakness and describe methods or techniques which can be used to mitigate the risk.
Note that the examples here are by no means exhaustive and any given weakness may have many subtle varieties, each of which may require different detection methods or runtime controls.
The following code contains a logging statement that tracks the contents of records added to a database by storing them in a log file. Among other values that are stored, the getPassword() function returns the user-supplied plaintext password associated with the account.
The code in the example above logs a plaintext password to the filesystem. Although many developers trust the filesystem as a safe storage location for data, it should not be trusted implicitly, particularly when privacy is a concern.
This code uses location to determine the user's current US State location.
First the application must declare that it requires the ACCESS_FINE_LOCATION permission in the application's manifest.xml:
During execution, a call to getLastLocation() will return a location based on the application's location permissions. In this case the application has permission for the most accurate location possible:
While the application needs this information, it does not need to use the ACCESS_FINE_LOCATION permission, as the ACCESS_COARSE_LOCATION permission will be sufficient to identify which US state the user is in.
In 2004, an employee at AOL sold approximately 92 million private customer e-mail addresses to a spammer marketing an offshore gambling web site [REF-338]. In response to such high-profile exploits, the collection and management of private data is becoming increasingly regulated.
Weaknesses in this category are related to the rules and recommendations in the Input Output (FIO) section of the SEI CERT Oracle Secure Coding Standard for Java.
Weaknesses in this category are related to the A3 category in the OWASP Top Ten 2017.
Weaknesses in this category are related to the design and architecture of a system's authorization components. Frequently these deal with enforcing that agents have th...
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This view outlines the SMM representation of the Automated Source Code Data Protection Measurement specifications, as identified by the Consortium for Information & So...
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