Home > CAPEC List > CAPEC-383: Harvesting Information via API Event Monitoring (Version 3.0)  

CAPEC-383: Harvesting Information via API Event Monitoring

Attack Pattern ID: 383
Abstraction: Detailed
Status: Draft
Presentation Filter:
+ Description
An adversary hosts an event within an application framework and then monitors the data exchanged during the course of the event for the purpose of harvesting any important data leaked during the transactions. One example could be harvesting lists of usernames or userIDs for the purpose of sending spam messages to those users. One example of this type of attack involves the adversary creating an event within the sub-application. Assume the adversary hosts a "virtual sale" of rare items. As other users enter the event, the attacker records via MITM proxy the user_ids and usernames of everyone who attends. The adversary would then be able to spam those users within the application using an automated script.
+ Typical Severity

Low

+ Relationships

The table(s) below shows the other attack patterns and high level categories that are related to this attack pattern. These relationships are defined as ChildOf, ParentOf, MemberOf and give insight to similar items that may exist at higher and lower levels of abstraction. In addition, relationships such as CanFollow, PeerOf, and CanAlsoBe are defined to show similar attack patterns that the user may want to explore.

+ Relevant to the view "Mechanisms of Attack" (CAPEC-1000)
NatureTypeIDName
ChildOfDeprecatedDeprecated567DEPRECATED: Obtain Data via Utilities
+ Prerequisites
The target software is utilizing application framework APIs
+ Consequences

The table below specifies different individual consequences associated with the attack pattern. The Scope identifies the security property that is violated, while the Impact describes the negative technical impact that arises if an adversary succeeds in their attack. The Likelihood provides information about how likely the specific consequence is expected to be seen relative to the other consequences in the list. For example, there may be high likelihood that a pattern will be used to achieve a certain impact, but a low likelihood that it will be exploited to achieve a different impact.

ScopeImpactLikelihood
Confidentiality
Read Data
+ Mitigations
Leverage encryption techniques during information transactions so as to protect them from attack patterns of this kind.
+ References
[REF-327] Tom Stracener and Sean Barnum. "So Many Ways [...]: Exploiting Facebook and YoVille". Defcon 18. 2010.
+ Content History
Submissions
Submission DateSubmitterOrganization
2014-06-23CAPEC Content TeamThe MITRE Corporation
Modifications
Modification DateModifierOrganization
2018-07-31CAPEC Content TeamThe MITRE Corporation
Updated Attack_Motivation-Consequences, Attack_Prerequisites, Description Summary, Related_Attack_Patterns, Resources_Required, Solutions_and_Mitigations
Previous Entry Names
Change DatePrevious Entry Name
2018-07-31Harvesting Usernames or UserIDs via Application API Event Monitoring

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Page Last Updated or Reviewed: July 31, 2018