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.

Primary

Resultant

Privacy violation

Privacy leak / Privacy leakage

PPI
acronym for Private Personal Information

PII
acronym for Personally Identifiable Information

PHI
acronym for Protected Health Information

Architecture and Design
OMISSION: This weakness is caused by missing a security tactic during the architecture and design phase.

Implementation

Operation

Confidentiality
Read Application Data

Architecture or Design Review

Private personal data can enter a program in a variety of ways:

Directly from the user in the form of a password or personal information
Accessed from a database or other data store by the application
Indirectly from a partner or other third party

If the data is written to an external location - such as the console, file system, or network - a privacy violation may occur.

High

Automated Static Analysis
Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect "sources" (origins of input) with "sinks" (destinations where the data interacts with external components, a lower layer such as the OS, etc.)
High

Automated Static Analysis
Tools are available to analyze documents
(such as PDF, Word, etc.) to look for private information
such as names, addresses, etc.

Requirements

Identify and consult all relevant regulations for personal privacy. An organization may be required to comply with certain federal and state regulations, depending on its location, the type of business it conducts, and the nature of any private data it handles. Regulations may include Safe Harbor Privacy Framework [REF-340], Gramm-Leach Bliley Act (GLBA) [REF-341], Health Insurance Portability and Accountability Act (HIPAA) [REF-342], General Data Protection Regulation (GDPR) [REF-1047], California Consumer Privacy Act (CCPA) [REF-1048], and others.

Architecture and Design

Carefully evaluate how secure design may interfere with privacy, and vice versa. Security and privacy concerns often seem to compete with each other. From a security perspective, all important operations should be recorded so that any anomalous activity can later be identified. However, when private data is involved, this practice can in fact create risk. Although there are many ways in which private data can be handled unsafely, a common risk stems from misplaced trust. Programmers often trust the operating environment in which a program runs, and therefore believe that it is acceptable store private information on the file system, in the registry, or in other locally-controlled resources. However, even if access to certain resources is restricted, this does not guarantee that the individuals who do have access can be trusted.

Implementation
Operation
Attack Surface Reduction

Some tools can automatically analyze
documents to redact, strip, or "sanitize" private
information, although some human review might be
necessary. Tools may vary in terms of which document
formats can be processed.

When calling an external program to automatically
generate or convert documents, invoke the program with
any available options that avoid generating sensitive
metadata. Some formats have well-defined fields that
could contain private data, such as Exchangeable image
file format (Exif), which can contain potentially
sensitive metadata such as geolocation, date, and time
[REF-1515] [REF-1516].

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.

pass = GetPassword();...dbmsLog.WriteLine(id + ":" + pass + ":" + type + ":" + tstamp);

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:

<uses-permission android:name="android.permission.ACCESS_FINE_LOCATION"/>

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:

locationClient = new LocationClient(this, this, this);locationClient.connect();Location userCurrLocation;userCurrLocation = locationClient.getLastLocation();deriveStateFromCoords(userCurrLocation);

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.

CVE-2023-29850
Library management product does not strip Exif data from images
https://www.cve.org/CVERecord?id=CVE-2023-29850

CVE-2020-26220
Customer relationship management (CRM) product does not strip Exif data from images
https://www.cve.org/CVERecord?id=CVE-2020-26220

CVE-2005-0406
Some image editors modify a JPEG image, but the original EXIF thumbnail image is left intact within the JPEG. (Also an interaction error).
https://www.cve.org/CVERecord?id=CVE-2005-0406

Privacy Violation

FIO13-J
Do not log sensitive information outside a trust boundary

Allowed
This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.

This entry overlaps many other
entries that are not organized around the kind of
sensitive information that is exposed, such as CWE-212:
Improper Removal of Sensitive Information Before Storage
or Transfer. However, because privacy is treated with
such importance due to regulations and other factors, and
it may be useful for weakness-finding tools to highlight
capabilities that detect personal private information
instead of system information, it is not clear whether -
or how - this entry should be deprecated.

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 (PPI) 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)
Employment history
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.

7 Pernicious Kingdoms
2006-07-19
Draft 3
2006-07-19

Eric Dalci
Cigital
2008-07-01
updated Time_of_Introduction

CWE Content Team
MITRE
2008-09-08
updated Relationships, Other_Notes, Taxonomy_Mappings

CWE Content Team
MITRE
2009-03-10
updated Other_Notes

CWE Content Team
MITRE
2009-07-27
updated Demonstrative_Examples

CWE Content Team
MITRE
2009-12-28
updated Other_Notes, References

CWE Content Team
MITRE
2010-02-16
updated Other_Notes, References

CWE Content Team
MITRE
2011-03-29
updated Other_Notes

CWE Content Team
MITRE
2011-06-01
updated Common_Consequences, Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2011-09-13
updated Other_Notes, References

CWE Content Team
MITRE
2012-05-11
updated Related_Attack_Patterns, Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2013-02-21
updated Applicable_Platforms, References

CWE Content Team
MITRE
2014-02-18
updated Alternate_Terms, Demonstrative_Examples, Description, Name, Other_Notes, References

CWE Content Team
MITRE
2014-07-30
updated Relationships

CWE Content Team
MITRE
2017-11-08
updated Modes_of_Introduction, References, Relationships

CWE Content Team
MITRE
2018-03-27
updated Relationships

CWE Content Team
MITRE
2019-01-03
updated Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2020-02-24
updated Alternate_Terms, Applicable_Platforms, Demonstrative_Examples, Description, Detection_Factors, Maintenance_Notes, Name, Potential_Mitigations, References, Relationships, Type

CWE Content Team
MITRE
2020-08-20
updated Related_Attack_Patterns

CWE Content Team
MITRE
2020-12-10
updated Relationships

CWE Content Team
MITRE
2021-03-15
updated References

CWE Content Team
MITRE
2021-10-28
updated Relationships

CWE Content Team
MITRE
2023-01-31
updated Related_Attack_Patterns

CWE Content Team
MITRE
2023-04-27
updated Detection_Factors, References, Relationships

CWE Content Team
MITRE
2023-06-29
updated Mapping_Notes

CWE Content Team
MITRE
2024-11-19
4.16
2024-11-19
updated Description, Diagram, Other_Notes

CWE Content Team
MITRE
2025-09-09
4.18
2025-09-09
updated References

CWE Content Team
MITRE
2025-12-11
4.19
2025-12-11
updated Alternate_Terms, Detection_Factors, Maintenance_Notes, Observed_Examples, Other_Notes, Potential_Mitigations, References, Relationships, Weakness_Ordinalities

Michal Biesiada
2023-02-08
4.19
2025-12-11
suggested covering PPI in documents such as PDFs

Michal Biesiada
2023-03-29
4.19
2025-12-11
suggested covering Exif in images

Privacy Violation
Exposure of Private Information ('Privacy Violation')