How Imperva’s New Attack Crowdsourcing Secures Your Business’s Applications

Attacks on applications can be divided into two types: targeted attacks and “spray and pray” attacks. Targeted attacks require planning and usually include a reconnaissance phase, where attackers learn all they can about the target organization’s IT stack and application layers. Targeted application attacks are vastly outnumbered by spray and pray attacks. The perpetrators of spray and pray attacks are less discriminating about their victims. Their goal is to find and steal anything that can be leveraged or sold on the dark web. Sometimes spray and pray attacks are used for reconnaissance, and later develop into a targeted attack.

One famous wave of spray and pray attacks took place against Drupal, the popular open-source content management system (CMS). In March 2018, Drupal reported a highly critical vulnerability (CVE-2018-7600) that earned the nickname, Drupalgeddon 2. This vulnerability enables an attacker to run arbitrary code on common Drupal versions, affecting millions of websites. Tools exploiting this weakness became widely available, which caused the number of attacks on Drupal sites to explode.

The ability to identify spray and pray attacks is an important insight for security personnel. It can help them prioritize which attacks to investigate, evaluate the true risk to their application, and/or identify a sniffing attack that could be a precursor to a more serious targeted one.

Identifying Spray and Pray Attacks in Attack Analytics

Attack Analytics, launched in May 2018, aims to crush the maddening pace of alerts that security teams receive. For security analysts unable to triage this alert avalanche, Attack Analytics condenses thousands upon thousands of alerts into a handful of relevant, investigate-able incidents. Powered by artificial intelligence, Attack Analytics automates what would take a team of security analysts days to investigate and cuts that investigation time down to a matter of minutes.

We recently updated Attack Analytics to provide a list of spray and pray attacks that may hit your business as part of a larger campaign. We researched these attacks using crowdsourced attack data gathered with permission from our customers. This insight is now presented in our Attack Analytics dashboard, as can be seen in the red circled portion of Figure 1 below.

Figure 1: Attack Analytics Dashboard

Clicking on the Similar Incidents Insights section shows more detail on the related attacks (Figure 2). An alternative way to get the list of spray and pray incidents potentially affecting the user is to login to the console and use the “How common” filter.

Figure 2: Attack Analytics Many Customers Filter

 

A closer view of the incidents will tell you the common attributes of the attack affecting other users (Figure 3).

Figure 3: Attack Analytics Incident Insights

How Our Algorithm Works

The algorithm that identifies spray and pray attacks examines incidents across Attack Analytics customers. When there are similar incidents across a large number of customers in a close amount of time, we identify this as a likely spray and pray attack originating from the same source. Determining the similarity of incidents requires domain knowledge, and is based on a combination of factors, such as:

  • The attack source: Network source (IP/Subnet), Geographic location
  • The attack target: URL, Host, Parameters
  • The attack time: Duration, Frequency
  • The attack type: Triggered rule
  • The attack tool: Tool name, type & parameters

In some spray and pray attacks, the origin of the attack is the most valuable piece of information connecting multiple incidents. When it is a distributed attack, the origin of the attack is not relevant, while other factors are relevant. In many cases, a spray and pray attack will be aimed at the same group of URLs.

Another significant common factor is the attack type, in particular, a similar set of rules that were violated in the Web Application Firewall (WAF). Sometimes, the same tools are observed, or the tools belong to the same type of attacks. The time element is also key, especially the duration of the attack or the frequency.

Results and Findings

The Attack Analytics algorithm is designed to identify groups of cross-account incidents. Each group has a set of common features that ties the incidents together. When we reviewed the results and the characteristics of various groupings, we discovered interesting patterns. First, most attacks (83.3%) were common among customers (Figure 4). Second, most attacks (67.4%) belong to groups with single source, meaning the attack came from the same IP address. Third, Bad Bot attacks still have a significant presence (41.1%). In 14.8% of the attacks, a common resource (like a URL) is attacked.

Figure 4: Spray & Pray Incidents Spread

Here’s an interesting example – a spray and pray attack from a single IP that attacked 1,368 customers in the same 3 consecutive days with the same vulnerability scanner, LTX71. We’ve also seen Bad Bots illegally accessing resources, attacking from the same subnet located in Illinois using a Trustwave vulnerability scanner. These bots performed a URLs scan on our customers resources – an attack which was blocked by our Web Application Firewall (WAF). Another attack involved a German IP trying to access the same WordPress-created system files  on more than 50 different customers with a cURL. And the list goes on.

Focusing on single-source spray and pray incidents has shown that these attacks affect a significant percentage of our customers. For example, in Figure 5 we see that the leading attack came from one Ukrainian IP that hit at least 18.49% of our customers. Almost every day, one malicious IP would attack a significant percentage of our customers.

Figure 5: Single Source Spray & Pray Accounts Affected

More Actionable Insights Coming

Identifying spray and pray attacks is a great example of using the intelligence from Imperva’s customer community to create insights that will help speed up your security investigations. Spray and pray attacks are not the only way of adding insights from community knowledge. Using machine-learning algorithms combined with domain knowledge, we plan to add more security insights like these to our Attack Analytics dashboard in the near future.

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