Manufacturing Compromise: The Emergence of Exploit-as-a-Service

This post is based on research conducted in collaboration with Google, to appear in CCS 2012. A pdf is available under my publications. Any views or opinions discussed herein are my own and not those of Google.

Driveby downloads — webpages that attempt to exploit a victim’s browser or plugins (e.g. Flash, Java) — have emerged as one of the dominant vectors for infecting hosts with malware. This revolution in the underground ecosystem has been fueled by the exploit-as-a-service marketplace, where exploit kits such as Blackhole and Incognito provide easily configurable tools that handle all of the “dirty work” of exploiting a victim’s browser in return for a fee. This business model follows in the footsteps of a dramatic evolution in the world of for-profit malware over the last five years, where host compromise is now decoupled from host monetization. Specifically, the means by which a host initially falls under an attacker’s control are now independent of the means by which an(other) attacker abuses the host in order to realize a profit, such as sending spam, information theft, or fake anti-virus.

In the case of exploit kits, attackers can funnel traffic from compromised sites or SEO boosted content to exploit kits, taking control of a victim’s machine without any knowledge of the complexities surrounding browser and plugin vulnerabilities. These hosts can in turn be sold to the pay-per-install marketplace or directly monetized by the attacker. From the perspective of Google Chrome, driveby downloads outstrip social engineering as the most prominent threat, while Microsoft’s latest security intelligence report (SIRv12) highlights the growing threat of driveby downloads, shown below:

In order to understand the impact of the exploit-as-a-service paradigm on the malware ecosystem, we performed a detailed analysis of:

  1. The prevalence of exploit kits across malicious URLs
  2. The families of malware installed upon a successful browser exploit, compared to executable found in email spam, software torrents, the pay-per-install market, and live network traffic
  3. The traffic volume, lifetime, and popularity of malicious websites.

To carry out this study, we analyzed 77,000 malicious URLs provided to us by Google, along with a crowd-sourced feed of blacklisted URLs known to direct to exploit kits. These URLs led to over 10,000 distinct binaries, which we ran in a contained environment (i.e. no side-effects visible to the outside world) to determine the family of malware as well as its monetization approach. We also aggregated and executed over 50,000 distinct binaries pulled from email spam, software and warez torrents, pay-per-install distribution sites, and live network traffic containing malware from corporate settings.

Anatomy of Driveby Download

From the time a victim accesses a malicious website up to the installation of malware on their system, there is a complex chain of events that underpins a successful driveby download. The infection chain for a real driveby that appeared in our study is shown below, where I obfuscate only the compromised website that launched the attack:

In this particular case, victims that visited a compromised website [1] were funneled through a chain of redirects [2] before finally being exposed to an exploit kit [3]. Depending on the time the compromised site was visited, either Blackhole or a yet unknown exploit kit would attempt to exploit the victim’s browser. If successful, different malware including SpyEye (information stealer), ZeroAccess (information stealer), and Rena (fake anti-virus) supplied by third-parties [4] would be installed on the victim’s machine [5]. This chain highlights the multiple actors involved in the exploit-as-a-service market: attackers’s purchasing installs, exploit kit developers, and miscreants compromising websites and redirecting traffic to exploit kits. Depending an attacker’s preference, all three roles can be conducted by a single party or outsourced to the underground marketplace.

Popular Exploit Kits

Of the 77,000 URLs we received from Google’s Safe Browsing list, over 47% of initial domains tied to driveby downloads terminate at an exploit kit. Of the remaining domains, 49% lead directly to executables without a pack, and 4% could not be classified. The table below provides a detailed breakdown of the kits we identified:

Rank Exploit Kit Initial Domains Final Domains
1 Blackhole 28% 29%
2 Incognito 10% 13%
3 Unknown.1 4% 2%
4 Sakura 2% 3%
5 Crimepack 1% <1%
6 Unknown.2 1% 1%
7 Bleeding Life 1% <1%
8 Phoenix <1% 2%
9 Elenore <1% <1%
- Executable 49% 45%
The most popular exploit kit is Blackhole which anecdotally based on screenshots like the one below has a success rate of 7-12% at compromising a victim's browser. Incognito follows in popularity along with a short list of other kits.
Our results show that exploit kits play a vital role in the driveby ecosystem. Surprisingly only a handful of kits exist making them one of the weakest links in the exploit-as-a-service marketplace. These types of bottlenecks are far more attractive for disruption compared to taking down the 6 300 unique domains hosting driveby exploits in our dataset (just a fraction of malicious sites in the wild).

Malware Dropped by Kits

We collect the unique binaries installed upon a successful exploit for each of the driveby domains in our dataset (10 308 binaries in total). During the same time period we also acquire a feed of executables found in email spam attachments (2 817 binaries) pay-per-install programs (2 691 binaries from the droppers that install a client's software) warez and torrents (17 182 binaries and compressed files) and live network traffic (28 300 binaries from Arbor ASERT). We execute all of these binaries in a contained environment which prohibits outgoing network traffic other than for manually crafted whitelist policies in order to allow test connections and guide execution.
Through a combination of automated clustering and manual labeling by analysts we classify the vast majority of binaries in our dataset with the most prominent families per infection vector shown below. (Note: torrents and live traffic contained a number of benign binaries bringing down the total fraction of malicious samples.)
Rank Driveby Dropper Attachment Torrent Live
1 Emit (12%) Clickpotato (6%) Lovegate (44%) Unknown.Adware.A (0.1%) TDSS (2%)
2 Fake WinZip (8%) Palevo (3%) MyDoom (6%) Sefnit (0.07%) Clickpotato (1%)
3 ZeroAccess (5%) NGRBot (2%) Bagle (1%) OpenCandy (0.07%) NGRBot (1%)
4 SpyEye (4%) Gigabid (2%) Sality (0.5%) Unknown.Adware.B (0.06%) Toggle Adware (0.5%)
5 Windows Custodian (4%) ZeroAccess (2%) TDSS (0.1%) ZeroAccess (.01%) ZeroAccess (0.3%)
6 Karagany (4%) Emit (1%) (0.03%) Whitesmoke (0.01%) Gigabid (0.2%)
Total 32 families 19 families 6 families 6 families 40 families

Through passive DNS data collected from a number of ISPs (details available in the paper), we are able to determine which families are installed most frequently by driveby domains. This provides a more meaningful ranking than using unique MD5 sums, which only measures polymorphism. We also compare whether any of the families installed by drivebys appear in our other feeds: (D)roppers, (A)ttachments, (L)ive, and (T)orrents.

Family Monetization Fraction of Installs Other Feeds
ZeroAccess Dropper 35% D;T;L
Windows Custodian Fake AV 10.3%
Karagany Dropper 9.5% D
SpyEye Information Stealer 8% D
TDSS Information Stealer 5.6% A;L
Cluster A Browser Hijacking 5.1%
Zbot Information Stealer 5% D
Multi Installer Dropper 3%
Medfos Browser Hijacking 2.6%
Cluster B Fake AV 2.2%
Clickpotato Adware 2.1% D;L
Perfect Keylogger Information Stealer 1.9% D;L
Emit Dropper 1.8% D;A;L
Sality Dropper 1.7% A
Votwup Denial of Service 1.6%
Fake Rena Fake AV 1.5%
Cluster C Information Stealer 0.7%

Variants including ZeroAccess and Emit rely on multiple infection vectors, while many of the other prominent variants are distributed solely through drivebys. Given that we identify 32 variants from drivebys and 19 from droppers, compared to only 6 from attachments and torrents, it is clear that the exploit-as-a-service and pay-per-install marketplace dominate the underground economy as a source of installs.

Catch Me If You Can

Using passive DNS data, we measure the time that a domain used to host an exploit kit receives traffic. We find malicious domains survive for a median of 2.5 hours before going dark, with 43% of compromised pages that siphon traffic towards exploit kits linking to more than one final domain. As such, attempting to detect sites hosting exploit kits is a losing battle where domain registration far outstrips the pace of detection. Instead, detection should concentrate on identifying compromised sites. Such detection should also occur in-browser in order to circumvent the challenges associated with cloaking or time of crawl vs. time of use variations.

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