Common Attack Pattern Enumeration and Classification
A Community Resource for Identifying and Understanding Attacks
An adversary actively probes the target in a manner that is designed to solicit information that could be leveraged for malicious purposes. This is achieved by exploring the target via ordinary interactions for the purpose of gathering intelligence about the target, or by sending data that is syntactically invalid or non-standard in an attempt to produce a response that contains the desired data. As a result of these interactions, the adversary is able to obtain information from the target that aids the attacker in making inferences about its security, configuration, or potential vulnerabilities. Examplar exchanges with the target may trigger unhandled exceptions or verbose error messages that reveal information like stack traces, configuration information, path information, or database design. This type of attack also includes the manipulation of query strings in a URI to produce invalid SQL queries, or by trying alternative path values in the hope that the server will return useful information.
The table below 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.
The table below shows the views that this attack pattern belongs to and top level categories within that view.
A tool, such as a MITM Proxy or a fuzzer, that is capable of generating and injecting custom inputs to be used in the attack.
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.
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.
Large quantities of data is often moved from the target system to some other adversary controlled system. Data found on a target system might require extensive resources to be fully analyzed. Using these resources on the target system might enable a defender to detect the adversary. Additionally, proper analysis tools required might not be available on the target system.
This attack differs from Data Interception and other data collection attacks in that the attacker actively queries the target rather than simply watching for the target to reveal information.
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