Common Attack Pattern Enumeration and Classification
A Community Resource for Identifying and Understanding Attacks
An attacker uses deceptive methods to cause a user or an automated process to download and install dangerous code believed to be a valid update that originates from an attacker controlled source. Although there are several variations to this strategy of attack, the attack methods are united in that all rely on the ability of an attacker to position and disguise malicious content such that it masquerades as a legitimate software update which is then processed by a program, undermining application integrity. As such the attack employs 'spoofing' techniques augmented by psychological or technological mechanisms to disguise the update and/or its source. Virtually all software requires frequent updates or patches, giving the attacker immense latitude when structuring the attack, as well as many targets of opportunity. Attacks involving malicious software updates can be targeted or untargeted in reference to a population of users, and can also involve manual and automatic means of payload installation. Untargeted attacks rely upon a mass delivery system such as spamming, phishing, or trojans/botnets to distribute emails or other messages to vast populations of users. Targeted attacks aim at a particular demographic or user population. Corporate Facebook or Myspace pages make it easy to target users of a specific company or affiliation without relying on email address harvesting or spamming. One phishing-assisted variation on this attack involves hosting what appears to be a software update, then harvesting actual email addresses for an organization, or generating commonly used email addresses, and then sending spam, phishing, or spear-phishing emails to the organization's users requesting that they manually download and install the malicious software update. This type of attack has also been conducted using an Instant Messaging virus payload, which harvests the names from a user's contact list and sends instant messages to those users to download and apply the update. While both methods involve a high degree of automated mechanisms to support the attack, the primary vector for achieving the installation of the update remains a manual user-directed process, although clicking a link within an IM client or web application may initiate the update. Other class of attacks focus on firmware, where malicious updates are made to the core system firmware or BIOS. Since this occurs outside the controls of the operating system, the OS detection and prevention mechanisms do not aid, thus allowing an adversary to evade defenses as well as gain persistence on the target's system. Automated attacks involving malicious software updates require little to no user-directed activity and are therefore advantageous because they avoid the complex preliminary setup stages of manual attacks, which must effectively 'hook' users while avoiding countermeasures such as spam filters or web security filters.
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.
Manual or user-assisted attacks require deceptive mechanisms to trick the user into clicking a link or downloading and installing software. Automated update attacks require the attacker to host a payload and then trigger the installation of the payload code.
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.
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