CAPEC-383: Harvesting Information via API Event Monitoring
Attack Pattern ID: 383
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.
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.
Standard Attack Pattern - A standard level attack pattern in CAPEC is focused on a specific methodology or technique used in an attack. It is often seen as a singular piece of a fully executed attack. A standard attack pattern is meant to provide sufficient details to understand the specific technique and how it attempts to accomplish a desired goal. A standard level attack pattern is a specific type of a more abstract meta level attack pattern.
The target software is utilizing application framework APIs
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.
Leverage encryption techniques during information transactions so as to protect them from attack patterns of this kind.
A Related Weakness relationship associates a weakness with this attack pattern. Each association implies a weakness that must exist for a given attack to be successful. If multiple weaknesses are associated with the attack pattern, then any of the weaknesses (but not necessarily all) may be present for the attack to be successful. Each related weakness is identified by a CWE identifier.
Harvesting Usernames or UserIDs via Application API Event Monitoring
More information is available — Please select a different filter.
Page Last Updated or Reviewed:
July 31, 2018