Hunting for Exploit Kits
E-mail spam and browser exploitation are two very popular avenues used by criminals to compromise computers. Most compromises result from human error, such as clicking a malicious link or downloading and opening an attachment within an email and enabling macros. Email filtering can offer effective protections against the delivery of widespread malicious spam, and user training can be reasonably effective in reducing the number of employees willing to open an unknown document, enable macros, and self-infect.
Protection against browser exploitation is more difficult, which occurs simply by visiting a website. Users are prone to clicking links to malicious sites, and much worse, criminals actively exploit well trafficked sites (directly through web server exploitation or indirectly via domain squatting, ad injections, or other techniques) and cause the user’s browser to covertly visit an exploit server. The user’s browser gets hit, a malicious payload such as ransomware is installed, and the user has a bad day.
The hardest part of this for attackers is the exploitation code itself. Fortunately for criminals, a thriving underground market for exploit kits is available. These occasionally contain zero days, but more often, exploit kit authors rapidly weaponize new vulnerabilities which will allow for exploitation of users who fail to rapidly patch their systems.
Exploit kits are often key components of crimeware campaigns, which is estimated to be a $400 billion global market. Capturing these often evasive exploit kits is essential to advance research into protecting against them, but samples are hard to obtain for researchers. To solve this problem, we created Maxwell, an automated exploit kit collection and detection tool that crawls the web hunting for exploits. For researchers, Maxwell significantly decreases the time it takes to find exploit kits samples, and instead enables us to focus on the detection and prevention capabilities necessary to counter the growing criminal threat of exploit kits.
Exploit Kits in the Wild
The Angler exploit kit - responsible for a variety of malvertising and ransomware compromises - is indicative of just how lucrative these exploit kits can be. By some estimates, Angler was the most lucrative compromise platform for crimeware, reeling in $60 million annually in ransomware alone. Earlier this year, a Russian criminal group was arrested in connection with the Lurk Trojan. This coincided with an end of Angler exploit kit activity. A battle for market share has ensued since, with RIG and Neutrino EK jockeying for the market leading position.
A typical business model for exploit kit authors is malware as a service. The authors rent access to their exploit kits to other criminals for several thousand dollars a month, on average.
Other criminal groups instead opt to focus more on traffic distribution services or gates, and set out to compromise as many web servers as possible. Once compromised, they can insert iframe re-directions to websites of their choosing. The users of exploit kits can pay for this as a service to increase the amount of traffic their exploit kits receive and drive up infections.
The Exploitation Process
The graphic below depicts a high-level overview of the six core steps of the exploitation process. There are numerous existing case studies on exploit kits, such as one on Nuclear, that provide additional, very low level details on this process.
- A user visits a legitimate web page.
- If the user’s configuration matches a vulnerability to a particular exploit (e.g., an outdated Flash version), the user’s browser will be directed to load the exploit.
- The exploit’s routines run, and gain code execution on the user’s machine.
- The exploit downloads and executes the chosen malware payload. Today, this is usually ransomware, but it can also be banking trojans, click fraud, or other malware.
How to Catch an Exploit Kit: Introducing Maxwell
There are numerous motivations for collecting and analyzing exploit kits. As a blue teamer, you may want to test your defenses against the latest and greatest threat in the wild. As a red teamer, you may want to do adversary emulation with one of the big named exploit kits (e.g., Neutrino, RIG, Magnitude). Or maybe you have some other cool research initiative. How would you go about collecting new samples and tracking activity? If you work for a large enterprise or AV company, it is relatively easy as your fellow employees or customers will provide all the samples you need. You can simply set up packet collection and some exploit kit detections at your boundary and sit back and watch. But what if you are a researcher, without access to that treasure trove of data? That’s where Maxwell comes in. Maxwell is an automated system for finding exploit kit activity on the internet. It crawls websites with an army of virtual machines to identify essential information, such as the responsible exploit kit, as well as related IPs and domains.
The Maxwell Architecture
Maxwell consists of a central server, which is basically the conductor or brains of the operation, connecting to a vSphere or other cloud architecture to spin up and down virtual machines. RabbitMQ and ElasticSearch provide the means for message queuing and indexing the malicious artifacts. The virtual machine consists of a variety of Python agent scripts to enable iterative development, as well as a pipe server that receives messages from the instrumentation library, filters those that match a whitelist, and forwards the remaining messages to a RabbitMQ server.
Flux is our instrumentation library, which is a DLL loaded to new processes with a simple AppInit DLL key. Flux hooks the usual functions for dropping files, creating registry keys, process creation, etc. The hooking is done only in user-mode at the Nt function level. The hooks must be at the lowest possible level in order to capture the most data. Flux also has some exploit detection capabilities built in, and shellcode capturing, which will be discussed shortly.
Moving to outside the virtual machine, the controller is a Python script that listens on a RabbitMQ channel for new jobs, including basic information like the website to visit, a uuid, and basic config information. Once a new job is received, the controller is responsible for spinning up a virtual machine and sending the job information and the plugin to execute (which is currently only Flux). The controller uses ssh to copy files into the virtual machine. The results server is also a Python script that listens on a different RMQ channel. This script receives data from the virtual machines during execution. The data is forwarded to an Elasticsearch index for permanent storage and querying. Once a job has completed, the results server determines if any malicious activity has occurred. If so, it executes post processing routines. Finally, all extracted data and signature information is sent in a notification to the researcher. An important design decision worth noting, is to stream events out of the virtual machine during execution, as opposed to all at once after a timeout. The latter is susceptible to losing information after ransomware wreaks havoc in the virtual machine.
When configuring your virtual machine, it’s important to make it an attractive target for attackers, who work by market share and target the most popular software, such as Windows 7, Internet Explorer, and Flash. Be sure to also remove vmtools and any drivers that get dropped by VMware. You can browse to the drivers folder and sort by publisher to find VMware drivers. Finally, you should consider patch levels, and pick plugin versions that are exploitable by all major exploit kits, while also disabling any additional protections, such as IE protected mode.
Exploit Detection in Maxwell
As mentioned earlier, Maxwell automates exploit detection. While previously ROP detection was reliable enough, it is no longer effective at detecting modern exploit kits. The same is true for stack pivot, which basically checks ESP to see if it points to the heap instead of the stack, and is easily evaded by Angler and other exploit kits.
In Flux, we throw guard pages not only on the export address table, but also the IAT and MZ header of critical modules. We also use a small whitelist instead of a blacklisting technique, enabling us to catch shellcode that is designed to evade EMET. Even better, we can detect memory disclosure routines that execute before the shellcode. When a guard page violation is hit, we also save the shellcode associated with it for later inspection.
If malicious activity is detected, the post-processing step is activated by executing tcpflow on the PCAP to extract all sessions and files. Next, regular expressions are searched across the GET/POST requests to identify traffic redirectors (such as EITEST), EK landing pages, and payload beacons. Finally, any dropped files, shellcode, and files extracted from the PCAP are scanned with Yara. The shellcode scanning allows for exploit kit tagging based on the specific shellcode routines used in each kit, which are very long lasting signatures.
If you are protecting a network with snort rules for a component of the EK process, you need to know when Maxwell stops flagging a signature. Building robust signatures limits the necessity to frequently update them. There are a few tricks for writing robust signatures, such as comparing samples over time to extract commonalities or creating signatures from Flash exploits themselves. Both of these may result in longer lasting signatures. You can also take advantage of social media, and compare samples in Maxwell against those posted on Twitter by researchers, such as @kafeine, @malware_traffic and @BroadAnalysis.
Hunting for Exploit Kits
With the architecture and detection capabilities in place, it’s time to start hunting. But which websites should be explored to find evil stuff? A surprisingly effective technique is to continually cycle through the Alexa top 25,000 or top 100,000 websites, which can be streamlined by browsing five websites at a time instead of one, and get a 5x boost on your processing capability. In less than 24 hours, you can crawl through 25,000 websites with just a handful of virtual machines. The only down side is losing the ability to know exactly which of the five websites was compromised without manually looking through the PCAP. If you have a good traffic anonymizing service, you can just reprocess each of the five websites.
At DerbyCon 6.0 Recharge, the Maxwell research was presented for the first time and we released the code under an MIT license. You can find it on GitHub. We look forward to to comments, contributions, and suggestions for advancements. Maxwell has proven extremely useful in fully automating the detection and analysis of exploit kits and watering holes. Ideally, Maxwell can help both red and blue teamers test an organization’s defenses without requiring extensive resources or significant time. It also greatly simplifies a key pain point for researchers - actually collecting the samples. By hunting for exploit kits with Maxwell, researchers can spend more time analyzing and building defenses against exploit kits, instead of searching for them.