The adversary incites a behavior from the target by manipulating something of influence. This is commonly associated with financial, social, or ideological incentivization. Examples include monetary fraud, peer pressure, and preying on the target's morals or ethics. The most effective incentive against one target might not be as effective against another, therefore the adversary must gather information about the target's vulnerability to particular incentives.
Likelihood Of Attack
This table shows the other attack patterns and high level categories that are related to this attack pattern. These relationships are defined as ChildOf and ParentOf, 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.
Meta Attack Pattern - A meta level attack pattern in CAPEC is a decidedly abstract characterization of a specific methodology or technique used in an attack. A meta attack pattern is often void of a specific technology or implementation and is meant to provide an understanding of a high level approach. A meta level attack pattern is a generalization of related group of standard level attack patterns. Meta level attack patterns are particularly useful for architecture and design level threat modeling exercises.
The adversary must have the means and knowledge of how to communicate with the target in some manner.The adversary must have knowledge of the incentives that would influence the actions of the specific target.
The adversary requires strong inter-personal and communication skills.
This table 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.
An organization should provide regular, robust cybersecurity training to its employees to prevent social engineering attacks.