Category Archives: FireEye

The Advanced Persistent Threat files: APT10

We’ve heard a lot about Advanced Persistent Threats (APTs) over the past few years. As a refresher, APTs are prolonged, aimed attacks on specific targets with the intention to compromise their systems and gain information from or about that target. While the targets may be anyone or anything—a person, business, or other organization—APTs are often associated with government or military operations, as they tend to be the organizations with the resources necessary to conduct such an attack. Starting with Mandiant’s APT1 report in 2013, there’s been a continuous stream of exposure of nation-state hacking at scale.

Cybersecurity companies have gotten relatively good at observing and analyzing the tools and tactics of nation-state threat actors; they’re less good at placing these actions in context sufficient enough for defenders to make solid risk assessments. So we’re going to take a look at a few APT groups from a broader perspective and see how they fit into the larger threat landscape.

Today, we’re beginning with APT10. (Note: These groups have a panoply of different names, but for simplicity’s sake, we’re going to borrow Mandiant’s naming conventions for Chinese groups.)

Who is APT10?

First observed in 2009, APT10 is most commonly attributed via open source research to the Chinese Ministry of State Security (MSS). MSS attacks are typically, but not limited to: intelligence targets surrounding trade negotiations, research and development in competition with Chinese commercial entities, and high value counter intelligence targets overseas. As an example of a trade negotiation op, Fidelis Security observed a watering hole attack in February 2017 targeting members of the National Foreign Trade Council, a US trade lobby group.

A commonly-used tool of APT10 is Scanbox, which is a form of malware that can offer insights into their targeting priorities. Scanbox has been observed on assorted industrial sector targets in the US and Japan, but also on Uighur dissidents overseas. While this supports the thesis of APT10 being a government threat group, we caution defenders against associating any one piece of malware exclusively with one group. Countries maintain multiple threat groups, all of whom are fully capable of collaborating and sharing TTPs.

Malware commonly deployed

APT10 is known for deploying the following malware:

Note: Variants of PlugX and Poison Ivy were developed and deployed by Chinese state-sponsored actors. They have since been sold and resold to individual threat actors across multiple nations. At time of writing, it is inappropriate to attribute an attack to Chinese threat actors based on PlugX or Poison Ivy deployment alone.

Should you be worried?

That depends on the type of organization you run. APT10 has been observed to most commonly target construction, engineering, aerospace, and regional telecoms, as well as traditional government targets. If your company exists outside these verticals, it’s unlikely that APT10 would expend the time and resources to target you. For companies outside the targeting profile, it’s much more cost effective to spend defense budgets on common vulnerabilities that are most leveraged by common attackers.

What might they do next?

Like most APTs, APT10 has traditionally targeted at scale when attacking commercial enterprise. However, a more recent report by Price Waterhouse Cooper and BAE Systems suggests that they’ve begin devoting a portion of their operations to targeting Managed Service Providers (MSPs), most likely in an attempt to exfiltrate sensitive client data. Given that there’s been increasing awareness of advanced threats by high-value targets, continuing to target MSPs in this way is a plausible means of obtaining the same desired data at a lesser cost.

Further resources

If you’d like to do some additional reading on APTs, and specifically APT10, take a look at the following resources:

The post The Advanced Persistent Threat files: APT10 appeared first on Malwarebytes Labs.

BIOS Boots What? Finding Evil in Boot Code at Scale!

The second issue is that reverse engineering all boot records is impractical. Given the job of determining if a single system is infected with a bootkit, a malware analyst could acquire a disk image and then reverse engineer the boot bytes to determine if anything malicious is present in the boot chain. However, this process takes time and even an army of skilled reverse engineers wouldn’t scale to the size of modern enterprise networks. To put this in context, the compromised enterprise network referenced in our ROCKBOOT blog post had approximately 10,000 hosts. Assuming a minimum of two boot records per host, a Master Boot Record (MBR) and a Volume Boot Record (VBR), that is an average of 20,000 boot records to analyze! An initial reaction is probably, “Why not just hash the boot records and only analyze the unique ones?” One would assume that corporate networks are mostly homogeneous, particularly with respect to boot code, yet this is not the case. Using the same network as an example, the 20,000 boot records reduced to only 6,000 unique records based on MD5 hash. Table 1 demonstrates this using data we’ve collected across our engagements for various enterprise sizes.

Enterprise Size (# hosts)

Avg # Unique Boot Records (md5)







Table 1 – Unique boot records by MD5 hash

Now, the next thought might be, “Rather than hashing the entire record, why not implement a custom hashing technique where only subsections of the boot code are hashed, thus avoiding the dynamic data portions?” We tried this as well. For example, in the case of Master Boot Records, we used the bytes at the following two offsets to calculate a hash:

md5( offset[0:218] + offset[224:440] )

In one network this resulted in approximately 185,000 systems reducing to around 90 unique MBR hashes. However, this technique had drawbacks. Most notably, it required accounting for numerous special cases for applications such as Altiris, SafeBoot, and PGPGuard. This required small adjustments to the algorithm for each environment, which in turn required reverse engineering many records to find the appropriate offsets to hash.

Ultimately, we concluded that to solve the problem we needed a solution that provided the following:

  • A reliable collection of boot records from systems
  • A behavioral analysis of boot records, not just static analysis
  • The ability to analyze tens of thousands of boot records in a timely manner

The remainder of this post describes how we solved each of these challenges.

Collect the Bytes

Malicious drivers insert themselves into the disk driver stack so they can intercept disk I/O as it traverses the stack. They do this to hide their presence (the real bytes) on disk. To address this attack vector, we developed a custom kernel driver (henceforth, our “Raw Read” driver) capable of targeting various altitudes in the disk driver stack. Using the Raw Read driver, we identify the lowest level of the stack and read the bytes from that level (Figure 1).

Figure 1: Malicious driver inserts itself as a filter driver in the stack, raw read driver reads bytes from lowest level

This allows us to bypass the rest of the driver stack, as well as any user space hooks. (It is important to note, however, that if the lowest driver on the I/O stack has an inline code hook an attacker can still intercept the read requests.) Additionally, we can compare the bytes read from the lowest level of the driver stack to those read from user space. Introducing our first indicator of a compromised boot system: the bytes retrieved from user space don’t match those retrieved from the lowest level of the disk driver stack.

Analyze the Bytes

As previously mentioned, reverse engineering and static analysis are impractical when dealing with hundreds of thousands of boot records. Automated dynamic analysis is a more practical approach, specifically through emulating the execution of a boot record. In more technical terms, we are emulating the real mode instructions of a boot record.

The emulation engine that we chose is the Unicorn project. Unicorn is based on the QEMU emulator and supports 16-bit real mode emulation. As boot samples are collected from endpoint machines, they are sent to the emulation engine where high-level functionality is captured during emulation. This functionality includes events such as memory access, disk reads and writes, and other interrupts that execute during emulation.

The Execution Hash

Folding down (aka stacking) duplicate samples is critical to reduce the time needed on follow-up analysis by a human analyst. An interesting quality of the boot samples gathered at scale is that while samples are often functionally identical, the data they use (e.g. strings or offsets) is often very different. This makes it quite difficult to generate a hash to identify duplicates, as demonstrated in Table 1. So how can we solve this problem with emulation? Enter the “execution hash”. The idea is simple: during emulation, hash the mnemonic of every assembly instruction that executes (e.g., “md5(‘and’ + ‘mov’ + ‘shl’ + ‘or’)”). Figure 2 illustrates this concept of hashing the assembly instruction as it executes to ultimately arrive at the “execution hash”

Figure 2: Execution hash

Using this method, the 650,000 unique boot samples we’ve collected to date can be grouped into a little more than 300 unique execution hashes. This reduced data set makes it far more manageable to identify samples for follow-up analysis. Introducing our second indicator of a compromised boot system: an execution hash that is only found on a few systems in an enterprise!

Behavioral Analysis

Like all malware, suspicious activity executed by bootkits can vary widely. To avoid the pitfall of writing detection signatures for individual malware samples, we focused on identifying behavior that deviates from normal OS bootstrapping. To enable this analysis, the series of instructions that execute during emulation are fed into an analytic engine. Let's look in more detail at an example of malicious functionality exhibited by several bootkits that we discovered by analyzing the results of emulation.

Several malicious bootkits we discovered hooked the interrupt vector table (IVT) and the BIOS Data Area (BDA) to intercept system interrupts and data during the boot process. This can provide an attacker the ability to intercept disk reads and also alter the maximum memory reported by the system. By hooking these structures, bootkits can attempt to hide themselves on disk or even in memory.

These hooks can be identified by memory writes to the memory ranges reserved for the IVT and BDA during the boot process. The IVT structure is located at the memory range 0000:0000h to 0000:03FCh and the BDA is located at 0040:0000h. The malware can hook the interrupt 13h handler to inspect and modify disk writes that occur during the boot process. Additionally, bootkit malware has been observed modifying the memory size reported by the BIOS Data Area in order to potentially hide itself in memory.

This leads us to our final category of indicators of a compromised boot system: detection of suspicious behaviors such as IVT hooking, decoding and executing data from disk, suspicious screen output from the boot code, and modifying files or data on disk.

Do it at Scale

Dynamic analysis gives us a drastic improvement when determining the behavior of boot records, but it comes at a cost. Unlike static analysis or hashing, it is orders of magnitude slower. In our cloud analysis environment, the average time to emulate a single record is 4.83 seconds. Using the compromised enterprise network that contained ROCKBOOT as an example (approximately 20,000 boot records), it would take more than 26 hours to dynamically analyze (emulate) the records serially! In order to provide timely results to our analysts we needed to easily scale our analysis throughput relative to the amount of incoming data from our endpoint technologies. To further complicate the problem, boot record analysis tends to happen in batches, for example, when our endpoint technology is first deployed to a new enterprise.

With the advent of serverless cloud computing, we had the opportunity to create an emulation analysis service that scales to meet this demand – all while remaining cost effective. One of the advantages of serverless computing versus traditional cloud instances is that there are no compute costs during inactive periods; the only cost incurred is storage. Even when our cloud solution receives tens of thousands of records at the start of a new customer engagement, it can rapidly scale to meet demand and maintain near real-time detection of malicious bytes.

The cloud infrastructure we selected for our application is Amazon Web Services (AWS). Figure 3 provides an overview of the architecture.

Figure 3: Boot record analysis workflow

Our design currently utilizes:

  • API Gateway to provide a RESTful interface.
  • Lambda functions to do validation, emulation, analysis, as well as storage and retrieval of results.
  • DynamoDB to track progress of processed boot records through the system.
  • S3 to store boot records and emulation reports.

The architecture we created exposes a RESTful API that provides a handful of endpoints. At a high level the workflow is:

  1. Endpoint agents in customer networks automatically collect boot records using FireEye’s custom developed Raw Read kernel driver (see “Collect the bytes” described earlier) and return the records to FireEye’s Incident Response (IR) server.
  2. The IR server submits batches of boot records to the AWS-hosted REST interface, and polls the interface for batched results.
  3. The IR server provides a UI for analysts to view the aggregated results across the enterprise, as well as automated notifications when malicious boot records are found.

The REST API endpoints are exposed via AWS’s API Gateway, which then proxies the incoming requests to a “submission” Lambda. The submission Lambda validates the incoming data, stores the record (aka boot code) to S3, and then fans out the incoming requests to “analysis” Lambdas.

The analysis Lambda is where boot record emulation occurs. Because Lambdas are started on demand, this model allows for an incredibly high level of parallelization. AWS provides various settings to control the maximum concurrency for a Lambda function, as well as memory/CPU allocations and more. Once the analysis is complete, a report is generated for the boot record and the report is stored in S3. The reports include the results of emulation and other metadata extracted from the boot record (e.g., ASCII strings).

As described earlier, the IR server periodically polls the AWS REST endpoint until processing is complete, at which time the report is downloaded.

Find More Evil in Big Data

Our workflow for identifying malicious boot records is only effective when we know what malicious indicators to look for, or what execution hashes to blacklist. But what if a new malicious boot record (with a unique hash) evades our existing signatures?

For this problem, we leverage our in-house big data platform engine that we integrated into FireEye Helix following the acquisition of X15 Software. By loading the results of hundreds of thousands of emulations into the engine X15, our analysts can hunt through the results at scale and identify anomalous behaviors such as unique screen prints, unusual initial jump offsets, or patterns in disk reads or writes.

This analysis at scale helps us identify new and interesting samples to reverse engineer, and ultimately helps us identify new detection signatures that feed back into our analytic engine.


Within weeks of going live we detected previously unknown compromised systems in multiple customer environments. We’ve identified everything from ROCKBOOT and HDRoot! bootkits to the admittedly humorous JackTheRipper, a bootkit that spreads itself via floppy disk (no joke). Our system has collected and processed nearly 650,000 unique records to date and continues to find the evil needles (suspicious and malicious boot records) in very large haystacks.

In summary, by combining advanced endpoint boot record extraction with scalable serverless computing and an automated emulation engine, we can rapidly analyze thousands of records in search of evil. FireEye is now using this solution in both our Managed Defense and Incident Response offerings.


Dimiter Andonov, Jamin Becker, Fred House, and Seth Summersett contributed to this blog post.

Bejtlich on the APT1 Report: No Hack Back

Before reading the rest of this post, I suggest reading Mandiant/FireEye's statement Doing Our Part -- Without Hacking Back.

I would like to add my own color to this situation.

First, at no time when I worked for Mandiant or FireEye, or afterwards, was there ever a notion that we would hack into adversary systems. During my six year tenure, we were publicly and privately a "no hack back" company. I never heard anyone talk about hack back operations. No one ever intimated we had imagery of APT1 actors taken with their own laptop cameras. No one even said that would be a good idea.

Second, I would never have testified or written, repeatedly, about our company's stance on not hacking back if I knew we secretly did otherwise. I have quit jobs because I had fundamental disagreements with company policy or practice. I worked for Mandiant from 2011 through the end of 2013, when FireEye acquired Mandiant, and stayed until last year (2017). I never considered quitting Mandiant or FireEye due to a disconnect between public statements and private conduct.

Third, I was personally involved with briefings to the press, in public and in private, concerning the APT1 report. I provided the voiceover for a 5 minute YouTube video called APT1: Exposing One of China's Cyber Espionage Units. That video was one of the most sensitive, if not the most sensitive, aspects of releasing the report. We showed the world how we could intercept adversary communications and reconstruct it. There was internal debate about whether we should do that. We decided to cover the practice in the report, as Christopher Glyer Tweeted:

In none of these briefings to the press did we show pictures or video from adversary laptops. We did show the video that we published to YouTube.

Fourth, I privately contacted former Mandiant personnel with whom I worked during the time of the APT1 report creation and distribution. Their reaction to Mr Sanger's allegations ranged from "I've never heard of that" to "completely false." I asked former Mandiant colleagues, like myself, in the event that current Mandiant or FireEye employees were told not to talk to outsiders about the case.

What do I think happened here? I agree with the theory that Mr Sanger misinterpreted the reconstructed RDP sessions for some sort of "camera access." I have no idea about the "bros" or "leather jackets" comments!

In the spirit of full disclosure, prior to publication, Mr Sanger tried to reach me to discuss his book via email. I was sick and told him I had to pass. Ellen Nakashima also contacted me; I believe she was doing research for the book. She asked a few questions about the origin of the term APT, which I answered. I do not have the book so I do not know if I am cited, or if my message was included.

The bottom line is that Mandiant and FireEye did not conduct any hack back for the APT1 report.

Update: Some of you wondered about Ellen's role. I confirmed last night that she was working on her own project.

Suspected Chinese Cyber Espionage Group (TEMP.Periscope) Targeting U.S. Engineering and Maritime Industries

Intrusions Focus on the Engineering and Maritime Sector

Since early 2018, FireEye (including our FireEye as a Service (FaaS), Mandiant Consulting, and iSIGHT Intelligence teams) has been tracking an ongoing wave of intrusions targeting engineering and maritime entities, especially those connected to South China Sea issues. The campaign is linked to a group of suspected Chinese cyber espionage actors we have tracked since 2013, dubbed TEMP.Periscope. The group has also been reported as “Leviathan” by other security firms.

The current campaign is a sharp escalation of detected activity since summer 2017. Like multiple other Chinese cyber espionage actors, TEMP.Periscope has recently re-emerged and has been observed conducting operations with a revised toolkit. Known targets of this group have been involved in the maritime industry, as well as engineering-focused entities, and include research institutes, academic organizations, and private firms in the United States. FireEye products have robust detection for the malware used in this campaign.

TEMP.Periscope Background

Active since at least 2013, TEMP.Periscope has primarily focused on maritime-related targets across multiple verticals, including engineering firms, shipping and transportation, manufacturing, defense, government offices, and research universities. However, the group has also targeted professional/consulting services, high-tech industry, healthcare, and media/publishing. Identified victims were mostly found in the United States, although organizations in Europe and at least one in Hong Kong have also been affected. TEMP.Periscope overlaps in targeting, as well as tactics, techniques, and procedures (TTPs), with TEMP.Jumper, a group that also overlaps significantly with public reporting on “NanHaiShu.”

TTPs and Malware Used

In their recent spike in activity, TEMP.Periscope has leveraged a relatively large library of malware shared with multiple other suspected Chinese groups. These tools include:

  • AIRBREAK: a JavaScript-based backdoor also reported as “Orz” that retrieves commands from hidden strings in compromised webpages and actor controlled profiles on legitimate services.
  • BADFLICK: a backdoor that is capable of modifying the file system, generating a reverse shell, and modifying its command and control (C2) configuration.
  • PHOTO: a DLL backdoor also reported publicly as “Derusbi”, capable of obtaining directory, file, and drive listing; creating a reverse shell; performing screen captures; recording video and audio; listing, terminating, and creating processes; enumerating, starting, and deleting registry keys and values; logging keystrokes, returning usernames and passwords from protected storage; and renaming, deleting, copying, moving, reading, and writing to files.
  • HOMEFRY: a 64-bit Windows password dumper/cracker that has previously been used in conjunction with AIRBREAK and BADFLICK backdoors. Some strings are obfuscated with XOR x56. The malware accepts up to two arguments at the command line: one to display cleartext credentials for each login session, and a second to display cleartext credentials, NTLM hashes, and malware version for each login session.
  • LUNCHMONEY: an uploader that can exfiltrate files to Dropbox.
  • MURKYTOP: a command-line reconnaissance tool. It can be used to execute files as a different user, move, and delete files locally, schedule remote AT jobs, perform host discovery on connected networks, scan for open ports on hosts in a connected network, and retrieve information about the OS, users, groups, and shares on remote hosts.
  • China Chopper: a simple code injection webshell that executes Microsoft .NET code within HTTP POST commands. This allows the shell to upload and download files, execute applications with web server account permissions, list directory contents, access Active Directory, access databases, and any other action allowed by the .NET runtime.

The following are tools that TEMP.Periscope has leveraged in past operations and could use again, though these have not been seen in the current wave of activity:

  • Beacon: a backdoor that is commercially available as part of the Cobalt Strike software platform, commonly used for pen-testing network environments. The malware supports several capabilities, such as injecting and executing arbitrary code, uploading and downloading files, and executing shell commands.
  • BLACKCOFFEE: a backdoor that obfuscates its communications as normal traffic to legitimate websites such as Github and Microsoft's Technet portal. Used by APT17 and other Chinese cyber espionage operators.

Additional identifying TTPs include:

  • Spear phishing, including the use of probably compromised email accounts.
  • Lure documents using CVE-2017-11882 to drop malware.
  • Stolen code signing certificates used to sign malware.
  • Use of bitsadmin.exe to download additional tools.
  • Use of PowerShell to download additional tools.
  • Using C:\Windows\Debug and C:\Perflogs as staging directories.
  • Leveraging Hyperhost VPS and Proton VPN exit nodes to access webshells on internet-facing systems.
  • Using Windows Management Instrumentation (WMI) for persistence.
  • Using Windows Shortcut files (.lnk) in the Startup folder that invoke the Windows Scripting Host (wscript.exe) to execute a Jscript backdoor for persistence.
  • Receiving C2 instructions from user profiles created by the adversary on legitimate websites/forums such as Github and Microsoft's TechNet portal.


The current wave of identified intrusions is consistent with TEMP.Periscope and likely reflects a concerted effort to target sectors that may yield information that could provide an economic advantage, research and development data, intellectual property, or an edge in commercial negotiations.

As we continue to investigate this activity, we may identify additional data leading to greater analytical confidence linking the operation to TEMP.Periscope or other known threat actors, as well as previously unknown campaigns.







HOMEFRY, a 64-bit Windows password dumper/cracker



MURKYTOP, a command-line reconnaissance tool 



AIRBREAK, a JavaScript-based backdoor which retrieves commands from hidden strings in compromised webpages

Historical Indicators






AIRBREAK, a JavaScript-based backdoor which retrieves commands from hidden strings in compromised webpages



Beacon, a commercially available backdoor



PHOTO, also reported as Derusbi



BADFLICK, backdoor that is capable of modifying the file system, generating a reverse shell, and modifying its command-and-control configuration

APT37 (Reaper): The Overlooked North Korean Actor

On Feb. 2, 2018, we published a blog detailing the use of an Adobe Flash zero-day vulnerability (CVE-2018-4878) by a suspected North Korean cyber espionage group that we now track as APT37 (Reaper).

Our analysis of APT37’s recent activity reveals that the group’s operations are expanding in scope and sophistication, with a toolset that includes access to zero-day vulnerabilities and wiper malware. We assess with high confidence that this activity is carried out on behalf of the North Korean government given malware development artifacts and targeting that aligns with North Korean state interests. FireEye iSIGHT Intelligence believes that APT37 is aligned with the activity publicly reported as Scarcruft and Group123.

Read our report, APT37 (Reaper): The Overlooked North Korean Actor, to learn more about our assessment that this threat actor is working on behalf of the North Korean government, as well as various other details about their operations:

  • Targeting: Primarily South Korea – though also Japan, Vietnam and the Middle East – in various industry verticals, including chemicals, electronics, manufacturing, aerospace, automotive, and healthcare.
  • Initial Infection Tactics: Social engineering tactics tailored specifically to desired targets, strategic web compromises typical of targeted cyber espionage operations, and the use of torrent file-sharing sites to distribute malware more indiscriminately.
  • Exploited Vulnerabilities: Frequent exploitation of vulnerabilities in Hangul Word Processor (HWP), as well as Adobe Flash. The group has demonstrated access to zero-day vulnerabilities (CVE-2018-0802), and the ability to incorporate them into operations.
  • Command and Control Infrastructure: Compromised servers, messaging platforms, and cloud service providers to avoid detection. The group has shown increasing sophistication by improving their operational security over time.
  • Malware: A diverse suite of malware for initial intrusion and exfiltration. Along with custom malware used for espionage purposes, APT37 also has access to destructive malware.

More information on this threat actor is found in our report, APT37 (Reaper): The Overlooked North Korean Actor. You can also register for our upcoming webinar for additional insights into this group.

Attacks Leveraging Adobe Zero-Day (CVE-2018-4878) – Threat Attribution, Attack Scenario and Recommendations

On Jan. 31, KISA (KrCERT) published an advisory about an Adobe Flash zero-day vulnerability (CVE-2018-4878) being exploited in the wild. On Feb. 1, Adobe issued an advisory confirming the vulnerability exists in Adobe Flash Player and earlier versions, and that successful exploitation could potentially allow an attacker to take control of the affected system.

FireEye began investigating the vulnerability following the release of the initial advisory from KISA.

Threat Attribution

We assess that the actors employing this latest Flash zero-day are a suspected North Korean group we track as TEMP.Reaper. We have observed TEMP.Reaper operators directly interacting with their command and control infrastructure from IP addresses assigned to the STAR-KP network in Pyongyang. The STAR-KP network is operated as a joint venture between the North Korean Government's Post and Telecommunications Corporation and Thailand-based Loxley Pacific. Historically, the majority of their targeting has been focused on the South Korean government, military, and defense industrial base; however, they have expanded to other international targets in the last year. They have taken interest in subject matter of direct importance to the Democratic People's Republic of Korea (DPRK) such as Korean unification efforts and North Korean defectors.

In the past year, FireEye iSIGHT Intelligence has discovered newly developed wiper malware being deployed by TEMP.Reaper, which we detect as RUHAPPY. While we have observed other suspected North Korean threat groups such as TEMP.Hermit employ wiper malware in disruptive attacks, we have not thus far observed TEMP.Reaper use their wiper malware actively against any targets.

Attack Scenario

Analysis of the exploit chain is ongoing, but available information points to the Flash zero-day being distributed in a malicious document or spreadsheet with an embedded SWF file. Upon opening and successful exploitation, a decryption key for an encrypted embedded payload would be downloaded from compromised third party websites hosted in South Korea. Preliminary analysis indicates that the vulnerability was likely used to distribute the previously observed DOGCALL malware to South Korean victims.


Adobe stated that it plans to release a fix for this issue the week of Feb. 5, 2018. Until then, we recommended that customers use extreme caution, especially when visiting South Korean sites, and avoid opening suspicious documents, especially Excel spreadsheets. Due to the publication of the vulnerability prior to patch availability, it is likely that additional criminal and nation state groups will attempt to exploit the vulnerability in the near term.

FireEye Solutions Detections

FireEye Email Security, Endpoint Security with Exploit Guard enabled, and Network Security products will detect the malicious document natively. Email Security and Network Security customers who have enabled the riskware feature may see additional alerts based on suspicious content embedded in malicious documents. Customers can find more information in our FireEye Customer Communities post.

APT28: A Window into Russia’s Cyber Espionage Operations?

The role of nation-state actors in cyber attacks was perhaps most widely revealed in February 2013 when Mandiant released the APT1 report, which detailed a professional cyber espionage group based in China. Today we release a new report: APT28: A Window Into Russia’s Cyber Espionage Operations?

This report focuses on a threat group that we have designated as APT28. While APT28’s malware is fairly well known in the cybersecurity community, our report details additional information exposing ongoing, focused operations that we believe indicate a government sponsor based in Moscow.

In contrast with the China-based threat actors that FireEye tracks, APT28 does not appear to conduct widespread intellectual property theft for economic gain. Instead, APT28 focuses on collecting intelligence that would be most useful to a government. Specifically, FireEye found that since at least 2007, APT28 has been targeting privileged information related to governments, militaries and security organizations that would likely benefit the Russian government.

In our report, we also describe several malware samples containing details that indicate that the developers are Russian language speakers operating during business hours that are consistent with the time zone of Russia’s major cities, including Moscow and St. Petersburg. FireEye analysts also found that APT28 has systematically evolved its malware since 2007, using flexible and lasting platforms indicative of plans for long-term use and sophisticated coding practices that suggest an interest in complicating reverse engineering efforts.

We assess that APT28 is most likely sponsored by the Russian government based on numerous factors summarized below:

Table for APT28

FireEye is also releasing indicators to help organizations detect APT28 activity. Those indicators can be downloaded at

As with the APT1 report, we recognize that no single entity completely understands the entire complex picture of intense cyber espionage over many years. Our goal by releasing this report is to offer an assessment that informs and educates the community about attacks originating from Russia. The complete report can be downloaded here: /content/dam/legacy/resources/pdfs/apt28.pdf.