Exposure of Private Personal Information to an Unauthorized Actor

Opis

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.

Inne nazwy

Privacy violation

Privacy leak / Privacy leakage

PPI

acronym for Private Personal Information

PII

acronym for Personally Identifiable Information

PHI

acronym for Protected Health Information

Skutki

• Confidentiality

  Read Application Data

Zapobieganie

• 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].

Przykłady podatności

• CVE-2023-29850 Library management product does not strip Exif data from images
• CVE-2020-26220 Customer relationship management (CRM) product does not strip Exif data from images
• 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).

Słabość nadrzędna

• CWE-200 Exposure of Sensitive Information to an Unauthorized Actor

Kiedy powstaje

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

Dotyczy

Not Language-Specific, Mobile

Uwagi

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.

Źródła

1. Seven Pernicious Kingdoms: A Taxonomy of Software Security Errors — Katrina Tsipenyuk, Brian Chess, Gary McGraw, NIST, 2005
2. AOL man pleads guilty to selling 92m email addies — J. Oates, The Register, 2005
3. Guide to Protecting the Confidentiality of Personally Identifiable Information (SP 800-122) — NIST, 2010
4. Safe Harbor Privacy Framework — U.S. Department of Commerce
5. Financial Privacy: The Gramm-Leach Bliley Act (GLBA) — Federal Trade Commission
6. Health Insurance Portability and Accountability Act (HIPAA) — U.S. Department of Human Services
7. California SB-1386 — Government of the State of California, 2002
8. FIPS PUB 140-2: SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES — Information Technology Laboratory, National Institute of Standards and Technology, 2001
9. Mobile App Top 10 List — Chris Wysopal, 2010
10. General Data Protection Regulation — Wikipedia
11. California Consumer Privacy Act (CCPA) — State of California Department of Justice, Office of the Attorney General
12. What to Know About EXIF Data, a More Subtle Cybersecurity Risk — Chester Avey, ISACA, 2025
13. McAfee's Rookie Mistake Gives Away His Location — Ben Weitzenkorn, Scientific American, 2012