Someone Is Building A Finnish-Themed Twitter Botnet

Finland will hold a presidential election on the 28th January 2018. Campaigning just started, and candidates are being regularly interviewed by the press and on the TV. In a recent interview, one of the presidential candidates, Pekka Haavisto, mentioned that both his Twitter account, and the account of the current Finnish president, Sauli Niinistö had recently been followed by a number of bot accounts. I couldn’t resist investigating this myself.

I wrote a tool to analyze a Twitter account’s followers. The Twitter API only gives me access to the last 5000 accounts that have followed a queried account. However, this was enough for me to find some interesting data.

As I previously wrote, newly created bulk bot accounts often look very similar. I implemented some logic in my follower analysis tool that attempts to identify bots by looking for a combination of the following:

  • Is the account still an “egg” (default profile settings, default picture, etc.)?
  • Does the account follow exactly 21 other accounts?
  • Does the account follow very few accounts (less than 22)?
  • Does the account have a bot-like name (a string of random characters)?
  • Does the account have zero followers?
  • Has the account tweeted zero times?

Each of the above conditions give a score. If the total of all scores exceeds an arbitrary value, I record the name of the account.

I ran this tool against @Haavisto and @niinisto Twitter accounts and found the following:
Matches for @Haavisto account: 399
Matches for @niinisto account: 330

In both cases, the accounts in question were by-and-large under 2 months old.

Haavisto bot account age ranges

Account age ranges for bots following @Haavisto


Niinisto account bot follower age ranges

Account age ranges for bots following @niinisto

Interestingly, I checked the intersection between these two groups of bots. Only 49 of these accounts followed both @Haavisto and @niinisto.

Checking a handful of the flagged accounts manually using the Twitter web client, I quickly noticed that they all follow a similar selection of high-profile Finnish twitter accounts, including accounts such as:

Tuomas Enbuske (@TuomasEnbuske) – a Finnish celebrity
Riku Rantala (@rikurantala) – host of Madventures
Sauli Niinistö (@niinisto) – Finland’s current president
Juha Sipilä (@juhasipila) – Finland’s prime minister
Alexander Stubb (@alexstubb) – Former prime minister of Finland
Pekka Haavisto (@Haavisto) – presidential candidate
YLE (@yleuutiset) – Finland’s equivalent of the BBC
Kauppalehti (@KauppalehtiFi) – a popular Finnish newspaper
Ilta Sanomat (@iltasanomat) – a popular Finnish newspaper
Talous Sanomat (@taloussanomat) – a prominent financial news source
Helsingin Sanomat (@hsfi) – Helsinki’s local newspaper
Ilmatieteen laitos (@meteorologit) – Finnish weather reporting source

What the bots are following

All the bots were following similar popular Finnish Twitter accounts, such as these.

Running the same analysis tool against Riku Rantala’s account yielded similar results. In fact, Riku has been the recipient of 660 new bot followers (although some of them were added on previous waves, judging by the account ages).

Account age ranges for bots following @rikurantala

I have no doubt that the other accounts listed above (and a few more) have recently been followed by several hundred of these bots.

By the way, running the same analysis against the @realDonaldTrump account only found 220 new bots. To verify, I also ran the tool against @mikko yielding a count of 103 bots, and against @rsiilasmaa I found only 38.

It seems someone is busy building a Finnish-themed Twitter botnet. We don’t yet know what it will be used for.

Some Notes On Meltdown And Spectre

The recently disclosed Meltdown and Spectre vulnerabilities can be viewed as privilege escalation attacks that allow an attacker to read data from memory locations that aren’t meant to be accessible. Neither of these vulnerabilities allow for code execution. However, exploits based on these vulnerabilities could allow an adversary to obtain sensitive information from memory (such as credentials, certificates, credit card information, etc.)

Exploits based on the Meltdown and Spectre vulnerabilities work by exploiting a feature of modern processors known as speculative execution (originally proposed by R. M. Tomasulo in 1967). Explained in simple terms, these exploits perform roughly the following four steps:

  • Flush or evict cache lines
  • Run code that causes the processor to perform speculative operations
  • Measure time to access certain cache locations known to contain secret data
  • Infer what that data was from measured access times

What is speculative execution?

Speculative execution is a technique used by high-speed processors in order to increase performance by guessing likely future execution paths and prematurely executing the instructions in them.

Although the results of these speculative execution paths are discarded if program control flow fails to reach them, they can leave behind observable effects in the system. The Meltdown and Spectre white papers primarily detail techniques whereby processor cache lines are examined in order to infer the results of speculative operations. However, the Spectre paper also suggests several other methods (not necessarily related to examining the cache) to do this, and concludes that “virtually any observable effect of speculatively executed code can be leveraged to leak sensitive information”. The primary techniques used to examine processor cache lines in the Meltdown and Spectre papers are Flush+Reload and Evict+Reload.

How do Flush+Reload and Evict+Reload work?

These techniques work by measuring the time it takes to perform a memory read at an address corresponding to an evicted or flushed cache line. If, after an operation, the monitored cache line was recently accessed, data will exist in the cache and access will be fast. If the cache line was not recently accessed, the read will be slow.

Flush+Reload illustration.

Here’s an example of cache line read times in a Flush+Reload attack. Note the downward spike indicating a faster read at one location. (Source:

What is the difference between Flush+Reload and Evict+Reload?

The main difference between these two techniques is the mechanism used for clearing the cache.

Flush+Reload uses a dedicated machine instruction, e.g., x86’s clflush, to evict the cache lines.

Evict+Reload forces contention on the cache set that stores the line, causing the processor to discard the contents of that cache line. Evict+Reload techniques are typically used when access to clflush is unavailable (e.g. from JavaScript).

Example code in the Spectre white paper (written in C) uses _mm_clflush() to perform a Flush+Reload attack (see Appendix A of the paper for details).

An example of JavaScript Evict+Reload can be found here.  Note that this example is not the actual technique described in the Spectre white paper. The researchers note that the accuracy of is intentionally degraded to dissuade timing attacks, so they implemented a more high-resolution timer by spawning separate threads that repeatedly decrement a value in a shared memory location.

Although Meltdown and Spectre utilize the same basic premise, there are a few differences in the details of how they work, and what they can be used for.


A number of proof of concepts exists for Spectre.

One proof of concept uses the Linux kernel’s eBPF mechanism in order to execute a code construct in the context of the OS kernel, thus leading to similar type of privilege escalation as presented by Meltdown, but without using kernel exceptions.

Another Spectre proof of concept allows the host kernel memory space of a KVM (Linux Kernel Virtual Machine) to be exposed. This attack, however, needs admin access to the KVM guest image.

One of the most interesting applications of Spectre is a JavaScript-based proof of concept that can access memory from within the browser process it was run. This exploit contains code that, when translated by a JIT, evaluates to a specific set of machine instructions. Execution of these JavaScript-based exploits happen within the context of the browser, and cannot read memory from outside of the browser process. However, these examples can readily be turned into weaponized exploits designed to extract secrets from within a browser’s memory space (such as credentials, certificates, etc.)

Spectre proof of concepts have been shown to work on Intel, ARM, and AMD processors.


Meltdown exploits scenarios where some CPUs allow out-of-order execution of user instructions to read kernel memory. Meltdown uses exception handlers to achieve this.

  • Currently, Meltdown proof of concepts have only been successfully tested on Intel CPUs.
  • Meltdown requires the attacker to execute code on the victim’s system itself.
  • Meltdown can be used to defeat kernel address space layout randomization (KASLR).
  • Meltdown can also be used to read memory from adjacent virtualized containers (Docker, LXC, OpenVZ) on the same physical host that share the same kernel.
  • The Meltdown vulnerability is effectively shut down by the KAISER patch (see below).


Most operating systems have already received an update that includes the KAISER patch. This patch implements a stronger isolation between kernel and user space, thus breaking the techniques that allow the Meltdown vulnerability to be exploited to read kernel memory. With KAISER in place, it is still possible to break KASLR using Meltdown exploitation techniques. However, this technique becomes non-trivial.

Since some Spectre exploits are likely to target browsers, we expect browser vendors will patch against these attacks in the near future. These patches will likely disrupt scripts’ ability to accurately record timings, and thus break the Evict+Reload portion of the attack. Depending on when you read this, Firefox may have already been patched against some of these attack vectors.

Update: iOS 11.2.2 also patches Safari against Spectre.

KAISER performance concerns

The KAISER patch is known to affect system performance. Performance impacts will be higher on software that performs a lot of system calls. Actual performance impact numbers will depend on the software and environment in question. It is likely that certain server operating environments will be affected the most. Home machines will likely not see any significant impact.

Will the KAISER patch slow down cryptomining?

Mining, whether CPU- or GPU-based shouldn’t be affected (there shouldn’t be any syscalls in mining loops). Monero (a CPU-based miner) network hashrate appears largely unchanged since the patch.

Detection of Meltdown and Spectre

Kernel memory violations are generated relatively infrequently by regular software. However, any process attempting to exploit Meltdown would generate thousands of such violations over a short duration. Capsule8 suggests that a system designed to monitor for an abundance of segmentation violations for kernel memory addresses (especially from the same PID) could be used to detect meltdown exploits in action.

Endgame recommends monitoring for cache timing attacks using hardware performance counters. In their blog, they examine methods to detect signs of Meltdown exploitation using TSX counters, page flush counters, and by counting last-level-cache (LLC) micro operations. They also examine how it might be possible to detect Spectre attacks by recording speculative branch execution leaks.


It is likely that software exploiting either Meltdown or Spectre will gather secrets as an intermediate step of a longer attack chain (e.g. read credentials from memory and use them to elevate a process). Although patches against Meltdown have already been released for current modern operating systems, there are plenty of legacy systems in the wild, and many users wait a long time, or don’t bother patching at all. Just as old SMB vulnerabilities were leveraged by WannaCry in the not-too-distant past, we’d expect Meltdown to be fair game in the future.

In the near future it is possible that new findings arise around speculative execution implementations, especially on the Intel platform.

Don’t Let An Auto-Elevating Bot Spoil Your Christmas

Ho ho ho! Christmas is coming, and for many people it’s time to do some online shopping.
Authors of banking Trojans are well aware of this yearly phenomenon, so it shouldn’t come as a surprise that some of them have been hard at work preparing some nasty surprises for this shopping season.

And that’s exactly what TrickBot has just gone and done. As one of the most prevalent banking malware for Windows nowadays, we’ve recently seen it diversify into attacking Nordic banks. We’ve blogged about that a couple of times already.

As usual, the Trojan is being delivered via spam campaigns. According to this graph, based on our telemetry, most spam was distributed between Tuesday afternoon and Wednesday morning:


The spam emails we’ve seen typically have a generic subject like “Your Payment – 1234”, a body with nothing but “Your Payment is attached”, and indeed an attachment which is a Microsoft Word document with instructions in somewhat poor English…


Clicking the button will not reveal any document content, but launch a macro that will eventually download and run the TrickBot payload.
Same old trick, but some people who have just bought a Christmas gift might still fall for it and end up with another ‘gift’ installed on their computer.

And that ‘gift’ is the most interesting part of this story. The newest payload underwent some changes which are, well, remarkable…


Since its initial appearance during Fall 2016, the actors have been actively developing the malware, and are constantly expanding and changing the targets. Here a short summary of the recently spotted changes:

  • Removed: banks in Australia, New Zealand, Argentina, Italy
  • Changed: a few Spanish, Austrian and Finnish targets are now found in the Dynamic Injection list (adding interception code to the actual web page) instead of using Static Injection (replacing the complete web page)
  • Added: new banks, particularly in France, Belgium and Greece.

Anti-sandbox checks

Up till now, we were not aware of any features in TrickBot that were checking if the malware is run in a virtual machine or a sandboxed environment used for automatic analysis. The new version has introduced a few simple checks against some known sandboxes by calling GetModuleHandle for the following DLLs:


(More info about every DLL can be found here)

If any of these modules are found, the payload just quits.

Interestingly, we have also found a few encrypted strings that seem to indicate detection of the Windows virtual machine images that Microsoft provides for web developers to test their code in Internet Explorer and Edge, however, these strings are not used anywhere (yet). Let’s see if the actors will expand their sandbox evasion attempts in a future version.



But we have saved the best for last. When the payload was running, we noticed that it didn’t run with user rights, as it always did before. Instead, it was running under the SYSTEM account, i.e. with full system privileges. There was no UAC prompt during the infection sequence, so TrickBot must have used an auto-elevation mechanism to gain admin rights.

A little search in the disassembly quickly revealed an obvious clue:


Combined with a few hard-coded CLSIDs …


… we found out that the actors have implemented a UAC bypass which was (as far as we are aware of) publicly disclosed only a few months ago. The original discovery is explained here:
And later implemented as a standalone piece of code, and most likely the main inspiration for the TrickBot coders:

In short: this bypass is a re-implementation of a COM interface to launch ShellExec with admin rights, and it is used in a standard Windows component “Component Manager Administrator Kit” to install network connections on machine level.

It works everywhere from Windows 7 up to the latest Windows 10 version 1709 with default UAC settings, and considering it’s basically a Windows feature, probably hard to address. In other words, perfect for usage in malware, and it wouldn’t surprise us if we’ll see the same bypass in more families soon.

Thanks to Päivi for the spam graph.


Necurs’ Business Is Booming In A New Partnership With Scarab Ransomware

Necurs’ spam botnet business is doing well as it is seemingly acquiring new customers. The Necurs botnet is the biggest deliverer of spam with 5 to 6 million infected hosts online monthly, and is responsible for the biggest single malware spam campaigns. Its service model provides the whole infection chain: from spam emails with malicious malware downloader attachments, to hosting the payloads on compromised websites.


Necurs is contributing a fair bit to the malicious spam traffic we observe.

The Necurs botnet is most renown for distributing the Dridex banking Trojan, Locky ransomware, and “pump-and-dump” penny-stock spam. Since 2016 it has expanded its deliverables beyond these three and have included other families of ransomware, such as GlobeImposter and Jaff, and the banking trojan Trickbot to its customer base, with Locky being its brand-image malware deliverable with multiple malware spam campaigns per week.

This morning at 9AM (Helsinki time, UTC +2) we observed the start of a campaign with malicious .vbs script downloaders compressed with 7zip. The email subject lines are “Scanned from (Lexmark/HP/Canon/Epson)” and the attachment filename is formatted as “image2017-11-23-(7 random digits).7z“.

The final payload (to our surprise) was Scarab ransomware, which we haven’t seen previously delivered in massive spam campaigns. Scarab ransomware is a relatively new ransomware variant first observed last June, and its code is based on the open source “ransomware proof-of-concept” called HiddenTear.

This version doesn’t change the file names, but appends a new file extension to the encrypted files with “.[].scarab”, and drops the following ransom note after the encryption:


The spam campaigns from Necurs are following the same format from campaign to campaign, consisting of social engineering subject line themes varying from financial to office utilities, with very minimal text body contents and spiced up usually with malicious attachments, sometimes just URLs. And as the simple social engineering themes are effective, Necurs tends to re-use the spam themes in its campaigns, sometimes within a rather short cycle. In this particular case, the subject lines used in this spam campaign were last seen in a Locky ransomware campaign exactly two weeks ago, the only difference being the extension of the attached downloader.


This has already given Scarab-ransomware a massive popularity bump, according to ransomware submissions ID ransomware.

We’re interested to see the future affiliations of this massive botnet and observe how it’s able to change the trends and popularity of malware types and certain families. In the meanwhile, we’ll keep blocking these threats, keeping our customers safe.



RickRolled by none other than IoTReaper

IoT_Reaper overview

IoT_Reaper, or the Reaper in short, is a Linux bot targeting embedded devices like webcams and home router boxes. Reaper is somewhat loosely based on the Mirai source code, but instead of using a set of admin credentials, the Reaper tries to exploit device HTTP control interfaces.

It uses a range of vulnerabilities (a total of ten as of this writing), from years 2013-2017. All of the vulnerabilities have been fixed by the vendors, but how well are the actual devices updated is another matter. According to some reports, we are talking about a ballpark of millions of infected devices.

In this blogpost, we just wanted to add up some minor details to good reports already published by Netlab 360 [1], CheckPoint [2], Radware [3] and others.

Execution overview

When the Reaper enters device, it does some pretty intense actions in order to disrupt the devices monitoring capabilities. For example, it just brutally deletes a folder “/var/log” with “rm -rf”.
Another action is to disable the Linux watchdog daemon, if present, by sending a specific IOCTL to watchdog device:


After the initialization, the Reaper spawns a set of processes for different roles:

  • Poll the command and control servers for instructions
  • Start a simple status reporting server listening on port 23 (telnet)
  • Start apparently unused service in port 48099
  • Start scanning for vulnerable devices

All the child processes run with a random name, such as “6rtr2aur1qtrb”.

String obfuscation

The Reaper’s spawned child processes use a trivial form of string obfuscation, which is surprisingly effective. The main process doesn’t use any obfuscation, but all child processes use this simple scheme when they start executing. Basically, it’s a single-byte XOR (0x22), but the way the data is arranged in memory makes it a bit challenging to connect the data to code.

Main process allocates a table in heap and copies the XOR-encoded data in. Later when the child processes want to reference to particular encoded data, it decodes it in heap and references the decoded data with a numeric index. After usage, the data is decoded back to its original form.

The following screenshot is a good presentation of the procedure:


Command and Control

The Reaper polls periodically a fixed set of C2 servers:,, and

The control messages and replies are transmitted over a clear-text HTTP, and the beacons are using the following format:


The protocol is very simple: basically there are only two major functions – shutdown or execute arbitrary payload using the system shell.

Port scanning

One of the child processes starts to scan for vulnerable victims. In addition to randomly generated IP addresses, Reaper uses nine hard-coded addresses for some unkown reason. The addess is scanned with a set of apparently random-looking set of ports, and then with a set of bit more familiar ports:

80, 81, 82, 83, 84, 88, 1080, 3000, 3749, 8001, 8060, 8080, 8081, 8090, 8443, 8880, 10000

In fact, the randomish ports are just byte-swapped presentation of the above port list. So for example, 8880 = 0x22b0 turns to 0xb022 = 45090. The reason for this is still unknown.

It is possible that the author was just lazy and left off some endianness handling code, or maybe it is some other error in the programming logic. Some of the IoT-devices are big-endian, so the ports need to be swapped in order to use them with socket code.

Screenshot of the hard-coded list of ports:


This is the list of hard-coded IP-addresses:


If the Reaper finds promising victim, it next tries to send HTTP-based exploit payload to the target. A total of ten different exploits have been observed so far, and they are related to IoT devices HTTP-based control interface. Here’s a list of the targeted vulnerabilities and HTTP requests associated with them:

1 – Unauthenticated Remote Command Execution for D-Link DIR-600 and DIR-300

Exploit URI: POST /command.php HTTP/1.1


2 – CVE-2017-8225: exploitation of custom GoAhead HTTP server in several IP cameras

GET /system.ini?loginuse&loginpas HTTP/1.1


3 – Exploiting Netgear ReadyNAS Surveillance unauthenticated Remote Command Execution vulnerability

GET /upgrade_handle.php?cmd=writeuploaddir&uploaddir=%%27echo+nuuo+123456;%%27 HTTP/1.1


4 – Exploiting of Vacron NVR through Remote Command Execution

GET /board.cgi?cmd=cat%%20/etc/passwd HTTP/1.1


5 – Exploiting an unauthenticated RCE to list user accounts and their clear text passwords on D-Link 850L wireless routers

POST /hedwig.cgi HTTP/1.1


6 – Exploiting a Linksys E1500/E2500 vulnerability caused by missing input validation

POST /apply.cgi HTTP/1.1


7 – Exploiting of Netgear DGN DSL modems and routers using an unauthenticated Remote Command Execution

GET /setup.cgi?next_file=netgear.cfg&todo=syscmd&curpath=/&currentsetting.htm=1cmd=echo+dgn+123456 HTTP/1.1


8 – Exploiting of AVTech IP cameras, DVRs and NVRs through an unauthenticated information leak and authentication bypass

GET /cgi-bin/user/Config.cgi?.cab&action=get&category=Account.* HTTP/1.1


9 – Exploiting DVRs running a custom web server with the distinctive HTTP Server header ‘JAWS/1.0’.

GET /shell?echo+jaws+123456;cat+/proc/cpuinfo HTTP/1.1


10 – Unauthenticated remote access to D-Link DIR-645 devices

POST /getcfg.php HTTP/1.1


Other details and The Roll

  • Reaper makes connection checks to google DNS server It won’t run without this connectivity.
  • There is no hard-coded payload functionality in this variant. The bot is supposedly receiving the actual functionality, like DDoS instructions, over the control channel.
  • The code contains an unused rickrolling link (yes, I was rickrolled)

Output from IDAPython tool that dumps encoded strings (rickrolling is the second one):


Sample hash

Analysis on this post is based on a single version of the Reaper (md5:37798a42df6335cb632f9d8c8430daec)



Facebook Phishing Targeted iOS and Android Users from Germany, Sweden and Finland

Two weeks ago, a co-worker received a message in Facebook Messenger from his friend. Based on the message, it seemed that the sender was telling the recipient that he was part of a video in order to lure him into clicking it.

Facebook Messenger message and the corresponding Facebook Page

The shortened link was initially redirecting to, but was later on changed to redirect to yet another shortened link –

Changes in the Picsee short link

The shortened link supported two types of redirection links – original link and smart links. If the device that accessed the URL was running in iOS or Android, it was redirected to the shortened link, otherwise it was redirected to

The short link with the smart links

So for the iOS and Android users, they were served with the following phishing page:

Phishing page for short link

For the rest of the devices, the users ended up with the link that went through several redirections which eventually led to That page contained an ad-affiliate URL which redirected to, a mobile advertising company.

Phishing page’s ad-affiliate URL

Based on the data from the links, the campaign began last October 15th when it targeted mostly Swedish users. On the 17th, it moved to targeting Finnish users. Then from 19th onwards, it mostly went after German users.

The total number of clicks for the entire campaign reached almost 200,000, where close to 80% of the visitors were from Germany, Sweden and Finland.

Statistics from tracking page

The campaign ran for two weeks with a main motive of stealing Facebook credentials from iOS and Android users. The cybercriminals used those stolen credentials to spread the malicious links, and subsequently gather more credentials. However, while in the process of stealing the credentials, the cybercriminals also attempted to earn from other non-iOS and non-Android users through ad-fraud.

This practice of using email addresses in place of unique names as account credentials creates a big opportunity for phishers. Just by launching this Facebook phishing campaign, they can mass harvest email and password credentials that are later on used for secondary attacks such as gaining access to other systems or services that could have a bigger monetary value because of password reuse.

We highly recommend the affected users to change their passwords as soon as possible, including other systems and services where the same compromised password was used.


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  • hxxp://lnk[.]pics/18JDK
  • hxxp://lnk[.]pics/196OV
  • hxxp://lnk[.]pics/18XH7
  • hxxp://lnk[.]pics/196PN
  • hxxp://lnk[.]pics/19LBP
  • hxxp://lnk[.]pics/18YZV
  • hxxp://lnk[.]pics/18QZW
  • hxxp://lnk[.]pics/196PA
  • hxxp://lnk[.]pics/19XK7
  • hxxp://lnk[.]pics/18HFX
  • hxxp://lnk[.]pics/19S3L
  • hxxp://lnk[.]pics/18J7S
  • hxxp://lnk[.]pics/19XKF
  • hxxp://lnk[.]pics/19K94
  • hxxp://lnk[.]pics/19LBW
  • hxxp://pics[.]ee/188g7
  • hxxp://pics[.]ee/18cdl
  • hxxp://po[.]st/ORyChA
  • hxxp://smarturl[.]it/02xuof
  • hxxp://utm[.]io/290459
  • hxxp://at.contenidoviral[.]net

The big difference with Bad Rabbit

Bad Rabbit is the new bunny on the ransomware scene. While the security community has concentrated mainly on the similarities between Bad Rabbit and EternalPetya, there’s one notable difference which has not yet gotten too much attention. The difference is that Bad Rabbit’s disk encryption works.

EternalPetya re-used the custom disk encryption method from the original Petya. Although it didn’t implement the actual ECDH key delivery mechanism, it installed the Petya boot loader, and effectively just rendered the machine useless.

Petya’s disk encryption had one specific weakness: it only encrypted some parts of the key file system structures, not the whole disk. This design obviously lead to speculations about whether it is possible to recover the disk using a known clear-text attack, and in fact researchers have made significant progress in investigating this recovery technique.

At least on the surface, things look quite different with Bad Rabbit. Instead of using a custom encryption mechanism, it follows the current trend in the ransomware community of leveraging known legitimate encryption tools.

Bad Rabbit uses DiskCryptor, a full disk partition encryption software for locking the user disk. The ransomware ships with an unmodified DiskCryptor driver (borrowed from ReactOS) and implements relevant parts of the DiskCryptor user-mode code for communicating with the driver. The ReactOS driver is a signed, valid driver, so it gets loaded by the Windows cleanly with the elevated privileges required by Bad Rabbit’s fake Flash installer dropper.

Key generation

Bad Rabbit uses Windows’ crypto API to generate random key data for the disk encryption. This key data is converted to a human-readable encryption key, which is a 32-bytes long ASCII encoded string, presenting around 165 bits of entropy for the stream cipher AES used by DiskCryptor.

The following screenshot represents the random disk encryption key as it is being generated by the malware:


Running dispci.exe under user-mode debugger

Along with the random key, Bad Rabbit packages some other information like the victim computer name and domain, then forms the so-called installation key that will be presented after the reboot. In Petya, this code was relatively short code that was protected by the public EC key. In Bad Rabbit, it is a much longer blob of data that is protected by the RSA public key shipped with the malware installer.

After packaging the installation key, the random key data is just discarded. The installation key is written to the disk so that the Bad Rabbit boot loader can present it on the boot screen:


Bad Rabbit’s boot loader

The user is expected to grab the installation key from the boot screen, paste it to the attacker’s TOR site, which then will use its own private RSA key to extract the 32-bytes password (typed in the screenshot above).

After the boot screen

Because the disk encryption software used is pretty much similar to any other disk encryption used by businesses around the globe, like TrueCrypt or Microsoft BitLocker, the disk is in fact *still* encrypted, although it has been mounted by the DiskCryptor driver transparently.

So if the user now reboots the machine, the same password prompt will be presented as long as the disk decryption routine is initiated. The DECRYPT tool referenced by the boot loader is actually just a shortcut to the dispci.exe tool dropped by the malware. This tool borrows code from legit DiskCryptor sources for implementing the relevant parts of the code for communicating with the disk encryption driver.

Even though all this is quite apparent by just looking at the code, we wanted to demonstrate the encryption scheme by catching the password inline, at the time it was generated by Bad Rabbit. When this password is used for unlocking the machine, it is possible to install the real DiskCryptor GUI tool and initiate the disk decryption process.


Using DiskCryptor to verify encrypted volume

DiskCryptor identifies the disk presented by the virtual machine (QEMU HARDDISK in the above screenshot) as an AES-encrypted volume, and accepts the random password.

It works now

We have speculated before that the flawed disk encryption in EternalPetya was due to problems in the malware development process. Or it may be that they just didn’t care. People will pay anyways, right?

Whatever was the reason, they have now fixed this issue (if they are the same group of malware developers, which seems to be the consensus in the research community).

At least the developers of Bad Rabbit have noted the recent developments in research on Petya’s disk encryption weaknesses and decided to use something different.

Recovery considerations

As we have demonstrated in this blog post, Bad Rabbit seems to use a sound principle in its disk encryption, a full disk encryption scheme familiar to all businesses.

We don’t yet have the full details of this scheme, so there might be bugs in the implementation. But at least its design enables a strong mechanism for locking the machine until the correct password is really typed to the boot screen.


For screenshots used in this blog post, we DID NOT go to the attacker TOR site and pay for the recovery key.

The procedures presented in this text DO NOT mean there’s an easy way to unlock the disk protected by the Bad Rabbit. We just present them as proof of the encryption scheme. Catching the password inline to the encryption process is not practical in a general sense, because it requires software that is aware of the exact password generation mechanism prior to the infection. It is used here just for a relatively easily reproducible proof-of-concept.

Following The Bad Rabbit

On October 24th, media outlets reported on an outbreak of ransomware affecting various organizations in Eastern Europe, mainly in Russia and Ukraine. Identified as “Bad Rabbit”, initial reports about the ransomware drew comparisons with the WannaCry and NotPetya (EternalPetya) attacks from earlier this year. Though F-Secure hasn’t yet received any reports of infections from our own customers, we’re actively investigating. And while the investigation is still ongoing, initial results from our analysis did find similarities between Bad Rabbit and the NotPetya ransomware that hit companies late last June.

We think there’s good evidence that suggests the same person or group is responsible for both last June’s NotPetya attacks and what we’re seeing now with Bad Rabbit. Malware authors often learn from what works, so finding the same characteristics in different families is not uncommon. But the similarities we’re seeing here are too much to be just one attacker copying another.

Without getting too technical, here’s a handful of the similarities between NotPetya and Bad Rabbit:

  • Overall code structure is similar
  • File encryption code is VERY similar
  • Similar method of checking existing processes and encrypting files
  • Similar method used to reboot computers
  • Same trick used to launch the malware’s main component as a DLL
  • Identical code used to parse the command line
  • Similar propagation methods, including an identical “library” of other computers found in the network, and use of Mimikatz to gather credentials
  • Out of 113 file extensions used by BadRabbit, 65 are shared with NotPetya (Bad Rabbit has an additional 48)

There are also some notable differences between the two, including:

  • Bad Rabbit doesn’t use EternalBlue/EternalRomance exploit
  • Bad Rabbit doesn’t use PsExec to spread
  • Bad Rabbit also encrypts “home user” files, such as .jpgs
  • Bad Rabbit adds “.encrypted” to the contents of affected files (NotPetya didn’t do this, making it harder to distinguish between encrypted and non-encrypted files)
  • Bad Rabbit’s infection vector is via compromised websites. While NotPetya was reported to be via MeDoc
  • Bad Rabbit brute-forces using a set of predefined credentials to available SMB shares
  • The list of process hashes to be compared to are different from NotPetya. NotPetya compares against Symantec and Kaspersky processes, while Bad Rabbit compares against McAfee and DrWeb

Like NotPetya, Bad Rabbit will display the two ransom note – one for MBR encryption.

Bad Rabbit Message

And a text note for file encryption.

Oops! Your files have been encrypted.

If you see this text, your files are no longer accessible.
You might have been looking for a way to recover your files.
Don't waste your time. No one will be able to recover them without our
decryption service.

We guarantee that you can recover all your files safely. All you
need to do is submit the payment and get the decryption password.

Visit our web service at caforssztxqzf2nm.onion

Your personal installation key#2: [REDACTED]

Users are directed to pay the ransom at a specified payment site, which also provides the amount of the ransom to be paid.

Bad Rabbit Payment Site

A threat description of the Bad Rabbit ransomware is available at Trojan:W32/Rabbad and will be updated as and when more details are confirmed.

In the meantime… our endpoint protection products have a variety of measures baked in that prevent Bad Rabbit infections.

Edited to update: Struckthrough EternalRomance mention above. We have verified the same observations as Cisco Talos Security about EternalRomance exploited by Bad Rabbit.

Twitter Forensics From The 2017 German Election

Over the past month, I’ve pointed Twitter analytics scripts at a set of search terms relevant to the German elections in order to study trends and look for interference.

Germans aren’t all that into Twitter. During European waking hours Tweets in German make up less than 0.5% of all Tweets published.

Data collected from the 1% sample stream (gardenhose)

Over the last month, Twitter activity around German election keywords has hovered at between 2 and 5 Tweets per second. Exceptions only occurred during the TV Debate (Sunday September 3rd 2017) and the day of voting (Sunday September 24th 2017). Surprisingly, Tweets volumes were still low on Saturday September 23rd 2017 – the day before votes were cast.

Here’s how things looked on Friday and Saturday:

Prior to polls closing on Sunday, Tweet volumes reached a sustained 10 Tweets per second. Once exit polls were announced, volumes exploded.

That sudden drop is my script not handling the volume all too well

Over the past month, topics related to the AfD party were pushed rather heavily on Twitter. This snapshot, taken on Thursday 21st September, is pretty similar to every one observed during the whole month.

Here’s an example of another trend I observed throughout the entire month regarding #afd hashtag volumes:

Notice that the Tweet volumes follow an organic pattern, trailing off during night hours. This contrasts what I observed during the French elections earlier this year, where #macronleaks hashtags were pushed by bots, and maintained a constant volume regardless of the time of day. Despite the high volume of AfD-related Twitter content being posted, AfD didn’t show up in Twitter’s own German trends at any point.

The terms “migrant”, “refugee”, “islam” were mentioned a fair bit in Tweets. Here’s what happened over the weekend.

Ben Nimmo of @DFRLab noticed that a hashtag named #wahlbetrug (election fraud) was being amplified by commercial Twitter bots on Saturday. This story was also picked up by the German publication Bild. My scripts also saw the hashtag briefly enter the top 10 during that day.

Here’s a Tweet timeline of this hashtag, since it’s appearance.

Looking at a timeline of Tweets using this hashtag shows the presence of certain more active accounts pushing this message.

Accounts retweeting Tweets containing the #wahlbetrug hashtag

Accounts replying to Tweets containing #wahlbetrug

Shortly after exit polls were published, the hashtag #fckafd surfaced.

The timeline of this particular hashtag is distinctly different.

However a number of highly active amplifiers were involved in both cases.

Highly active amplifiers of the #fckafd hashtag

Highly active amplifiers of the #wahlbetrug hashtag

This data illustrates how tricky it is to automate the discovery of artificially amplified Tweets. While the #wahlbetrug hashtag was indeed amplified by paid commercial botnets, it didn’t make a splash, and Twitter users would have most likely have needed to go searching for pro-AfD Tweets to find it.

A few Twitter users posted very actively during the campaign. Over the weekend, @Teletubbies007, @Jensjehagen, and @nanniag were very active.

@Teletubbies007 was the top tweeter of the #AfD, #btw17, #gehwaehlen, #reconquista, #traudichdeutschland, #wahlbeobachter, #wahlbeobachter, and #weidel hashtags.

Jensjehagen published the most retweets over the weekend.

The @AfD account was the most mentioned Twitter account.

Tools for automating Twitter activity (such as IFTTT) appeared in the top 10 of sources captured during the weekend.

Tweets published by highly active accounts made up about 3.5% of all traffic during the weekend. I had no accurate way of measuring the number of Tweets originating from commercial bot nets, and hence can’t give an estimation as to how much traffic those were responsible for.

My scripts were also configured to look for specific patterns in Tweets and metadata associated with users’ accounts in order to calculate how much activity originated from “alt-right” groups pushing right-wing agenda. Rough calculations from this data suggest that as much as 15% of all Twitter traffic associated with the German election fit this pattern.

Of the roughly 1.2 million Tweets processed between Friday afternoon and Sunday night, about 170,000 Tweets were matched by that bit of logic. These Tweets originated from about 3,300 accounts. This traffic was enough to generate the results seen in this article, but only to those looking at Twitter streams with automated tools, such as the ones I’m using.

Of note were a few videos and URLs that received a fair amount of retweets. The most obvious of these was a video posted by V_of_Europe showing an immigrant removing election campaign posters.

This was the second most retweeted Tweet I could find from the weekend (pertaining to the election itself). This Tweet was also pushed in other languages.

Another notable Tweet that showed up earlier in the weekend was a story about Merkel being booed at her final campaign rally in Munich. It didn’t get a whole lot of traction, though.

Plenty of links were shared to non-authoritative news sources. Here’s just one example…

…which was shared by this account…

Pay close attention to this user’s profile description

And on the same note, during the weekend, I saw plenty of non-German accounts Tweeting in German, and pushing links to questionable news sources.

I also captured plenty of pro-Trump accounts posting in English.

Another interesting story that failed to gain traction was one about an election results leak prior to the end of voting.

Across the duration of my analysis, 200,000 individual URLs were shared. Around 418,000 Tweets contained those URLs. Of those Tweets, only some 500 linked to questionable political content, which were shared in about 4,000 Tweets. Of the 400,000 unique Twitter users observed participating in this discussion, about 3,000 users were responsible for sharing “fake news” links. By and large, the accounts sharing these links didn’t look like bots.

Merkel was the most seen word in Tweets that shared links to political agenda articles.

Overall, German language Tweets made up roughly 60% of all Tweets during the run-up to the election.

At the time of writing, the most retweeted Tweet I can find pertaining to the election is this one, which has already received over 10,000 retweets.

Also, it’s nice to see that Russians themselves have a sense of humor when it comes to all the allegations of election interference.

Given the lack of German participation on Twitter, it seems to me that the heavy right-wing messaging push that’s been going on during the German election cycle has been more about recruiting new members into the alt-right than it’s been about election interference.

TrickBot In The Nordics, Episode II

The banking trojan TrickBot is not retired yet. Not in the least. In a seemingly never ending series of spam campaigns – not via the Necurs botnet this time – we’ve spotted mails written in Norwegian that appear to be sent by DNB, Norway’s largest bank.

Trickbot Mail

The mail wants the recipient to believe that they have received an important “decision letter” and that they should open the attached document for more information. They also suggest that, if there are problems reading the content, you have to click the “Enable Content” button… uh oh, where have we heard that before?

Anyway, let’s take a look at the attachment “SikreDokumenter.doc” (“Secure Document”). Not that much to see here though.

Sikre Dokumenter

“Laster Innhold” translates to “Loading content”, but that content never appears. As if it is waiting for the user to click “Enable Content”, as the mail suggests, no? Unfortunately, clicking this button still never reveals anything (how disappointing!). Instead, a Visual Basic macro launches a PowerShell script which will download and execute the TrickBot loader.

And just like last time we wrote about TrickBot, a large spam campaign often goes hand in hand with a malware update. Now the authors are “celebrating” a brand new list of targets. Here’s a short summary:

  • More targeting of finance related sites which are no traditional banks: American Express, Amazon, …
  • A few banks in Mexico, Argentina and Chile. Middle and South America, some of the last parts of the world that TrickBot hadn’t visited yet.
  • New European countries: Croatia, Slovenia, Hungary, Turkey, …
  • More banks in countries targeted before, such as Belgium, The Netherlands, Luxembourg, Germany, Spain, Italy, Poland, Singapore, Australia, New Zealand, …
  • And last but not least: the Nordic countries are back in the game.

Wait, the Nordic banks were gone? That’s right! They appeared in June, but were removed again early August. Our guess was that attacking the Nordics turned out not that profitable – but now they are back. Which immediately explains the localized spam.

But fear not, our security products were already protecting you against this latest campaign.

Special thanks to Päivi for the help.

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