# New Mozi malware family quietly amasses IoT bots

**blog.centurylink.com/new-mozi-malware-family-quietly-amasses-iot-bots/**

[Black Lotus Labs Posted On April 13, 2020](https://blog.lumen.com/author/black-lotus-labs/)

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## Summary


April 13, 2020


The explosion of Internet of Things (IoT) devices has long served as a breeding ground for malware distribution. The inability for users to patch
many IoT devices has only compounded this problem, as bad actors continue to evolve tactics to leverage botnets for DDoS attacks and other
malicious behavior. Black Lotus Labs tracks malware families that present new or distinct threats to the global community, and recently began
tracking a new malware family called Mozi.

Mozi is evolved from the source code of several known malware families – Gafgyt, Mirai and IoT Reaper – that have been brought together to
form a peer-to-peer (P2P) botnet capable of DDoS attacks, data exfiltration and command or payload execution. The malware targets IoT
devices, predominantly routers and DVRs that are either unpatched or have weak telnet passwords. After a notable traffic increase in
December was mistakenly attributed to other malware families by researchers, Black Lotus Labs reviewed entries in our reputation system for
that timeframe, which revealed a different story. This traffic was not simply increased activity by a known family, but a new family altogether.

## Black Lotus Labs Findings

In December 2019, Black Lotus Labs observed an increase of entries of compromised hosts in our reputation system labeled as IoT Reaper.
Since this malware family has not changed in some time, the increase was unexpected, and led to further investigation of the increase. Upon
review of these entries we began to see a pattern develop, each host had an http server listening on a random port that served a file which
included “Mozi” in the name. File names such as “Mozi.m” and “Mozi.a” were seen throughout all of the identified hosts.

Research revealed that these hosts were part of a growing P2P botnet and were making the Mozi files available for distribution to newly
infected hosts. While the increase in data began in December, our data shows that the use of the Mozi filename began earlier, in September.
Further analysis revealed that the malware had been identified under many of its contributing families, including Mirai, Gafgyt, and IoT Reaper.
The original spike consisted of 69 hosts, but a broader look revealed 4,216 hosts. Shortly after we began our research based on samples
being detected as multiple families, Netlab360 shared their findings on this malware[1] confirming our understanding.


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e o bot et s co p sed o odes t at ut e a d st buted as tab e ( ) o co u cat o, s a to t e code used by o eape
and Hajime. These nodes also host the Mozi.m and Mozi.a malware binary files, passed during the compromise of new hosts, on a randomly
chosen port. The standard DHT protocol is commonly used to store node contact information for torrent and other P2P clients. Using DHT
allows the malware to bypass the use of standard malware command and control servers while hiding behind the large amount of typical DHT
traffic. This makes it harder to track and impact the control infrastructure. As a P2P botnet, Mozi implements its own custom extended DHT
described later.

To enumerate the botnet, Black Lotus Labs implemented a machine learning model trained on the observed unique DHT traffic implementation
utilized by Mozi. This allows us to distinguish between Mozi nodes and benign hosts, and identify the IPs suspected of participating in the
botnet. When we identify a new suspected Mozi node, our software attempts to confirm the suspicion by sending messages proprietary to the
malware’s p2p protocol, and looks for correctly formatted responses. When the correct response is seen, the host is validated as a member of
the Mozi botnet.

## The Mozi botnet is growing as the malware evolves

To get a view on the potential size of this new botnet we continue to review the data within our reputation system, and the IPs of the hosts
confirmed as Mozi botnet nodes.

Looking at unique Mozi nodes hosting the malware during the initial spike witnessed, there were 323 unique nodes, by Dec. 27 unique nodes
had grown to 1,205, then hitting a high point on Feb. 4 with 2,191 nodes. Our review showed a steady increase in the number of compromised
hosts over a span of three months – then a decline beginning in late February. Overall, we have observed over 15,858 unique Mozi nodes over
the last four months.

The geographic distribution of Mozi botnet nodes since September, 2019:


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oug out t e e o t e o bot et, t e bu o t e odes a e bee ocated s a, t C a ost g % o ected osts Outs de o
Asia, the largest footprint has been the US, with 10% of all nodes. A review of the data for just the month of March shows China increasing to
81% of the botnet nodes and the United Stated dropping to less than 1%.

Our first observations of the malware in December found only three distinct samples. As time as has passed, we’ve now seen 34 unique
samples, suggesting the operators are continuing to manipulate the malware.

## A deeper look into the Mozi malware

The Mozi samples analyzed are ELF binaries with versions targeted for MIPS and ARM processor architectures. Once executed, the binary
forks many versions of itself renamed as ‘ssh’ or ‘dropbear’. The forked processes are responsible for setting up the DHT communications and
closing ports to prevent infection by other malware. The forked processes can also set up HTTP on a randomly chosen port to host the Mozi
binary.

Mozi uses a modified DHT protocol for communication. The bot initially DHT pings several nodes hardcoded in the binary to bootstrap the
initial connection to the DHT network (listed below). For the nodes that respond, the bot then sends a DHT ‘find_node’ command to locate
other bots on the Mozi botnet. It often takes several find_node attempts to locate an active Mozi peer.

Example find_node request:

d1:ad2:id20:88888888h\xe3\x90\xe6\xcbT\x1f\x89\x0a[\xda\x896:target20:88888888:ad2:id2bo\xa5\x88e1:q9:find_node1:t4:fn\x00\x00\1:v4oB\xe5

If this request is sent to an active Mozi peer, the peer will often return the config of the contacted peer as the value to the ‘nodes’ key,
alternatively it will return other nodes in the ‘nodes’ key. The config is XOR encrypted with a hardcoded key.

The encoded ‘find_node’ request looks like:

The bytes removed are repeating bytes of the XOR key and will be 0x00 bytes after decryption

Yellow – Bot ID

Blue – Target ID

Purple – Version flag

Orange – Responding nodes ID

Green – Encrypted config

Bytes without color coding – Bencode encoded bytes not specific to Mozi


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e tua y o pee s etu t e O e coded co g O ce dec ypted t e co g co ta s (as desc bed by et ab360) a bot o e a d a
DHT prefix. The bot roles that we have seen in our data are ‘bot’, ‘sk’, ‘ftp’, and ‘sns’. The DHT prefix seems to always be ‘88888888’, except in
the case of the ‘sns’ bot role in which it is just ‘888’.

Decoded config samples:

[ss]sns[/ss][cpu].m[/cpu][hp]888[/hp]

[ss]ftp[/ss][cpu].w[/cpu][hp]88888888[/hp]

[ss]sk[/ss][hp]88888888[/hp][count]http://ia.51[.]la/go1?id=20198527&pu=http%3a%2f%2fbaidu[.]com/[idp][/count]

[ss]bot[/ss][hp]88888888[/hp][count]http://ia.51[.]la/go1?id=19894027&pu=http%3a%2f%2fbaidu[.]com/[idp][/count]

## Mitigation Recommendations

Mozi is a fitting example of a new botnet ramping up virtually unnoticed or misattributed. What was initially viewed as small spike of a single
family was actually a single, much larger, growing botnet. As evidenced by the rapid increase in size of this botnet, there are still large numbers
of vulnerable IoT devices on the Internet. Black Lotus Labs continues to monitor the Mozi botnet and will continue to work to enumerate the
P2P nodes. Black Lotus Labs has reached out to the most active nodes’ upstream providers in an attempt to disrupt and slow the botnet’s
growth.

All the vulnerabilities targeted by the botnet are well known and are prevented by either proper patching or proper password management.
Companies and individuals can follow these security best practices to help prevent these types of compromises in the future:

Implement effective passwords on all IoT devices when possible.
Restrict IoT devices access to the wide-open internet.
Implement proper patching methodologies.
Review this list of affected devices and their vulnerabilities.

Netlab360 shared a table of affected devices and their vulnerabilities:

**Affected Device** **Vulnerability**

Eir D1000 Router [Eir D1000 Wireless Router RCI](https://www.exploit-db.com/exploits/40740)

Vacron NVR devices [Vacron NVR RCE](https://www.exploit-db.com/exploits/6864/)

Devices using the Realtek SDK [CVE-2014-8361](https://www.exploit-db.com/exploits/37169/)

Netgear R7000 and R6400 [Netgear cig-bin Command Injection](https://www.exploit-db.com/exploits/41598/)

DGN1000 Netgear routers [Netgear setup.cgi unauthenticated RCE](https://www.exploit-db.com/exploits/43055)

MVPower DVR [JAWS Webserver unauthenticated shell command execution](https://www.exploit-db.com/exploits/41471/)

Huawei Router HG532 [CVE-2017-17215](https://www.exploit-db.com/exploits/43414/)

D-Link Devices [HNAP SoapAction-Header Command Execution](https://www.exploit-db.com/exploits/37171/)

GPON Routers [CVE-2018-10561, CVE-2018-10562](https://www.exploit-db.com/exploits/44576/)

D-Link Devices [UPnP SOAP TelnetD Command Execution](https://www.exploit-db.com/exploits/28333/)

CCTV DVR [CCTV/DVR Remote Code Execution](https://www.exploit-db.com/exploits/39596/)

## IoCs

Malware Hashes

006965027c1f636295b5011a46905121

1bd4f62fdad18b0c140dce9ad750f6de

2560a86361257837b78d7ba289a031fb

2d2ffa0422db66640561c46b8e428267

2f8d6c0c6a449f3c074cfc0d6c8dbfc6

300f850c0186077550830fa35edddc4e

39434e0d800b62a72e8dfa202e2da9cd


-----

3a 03ab0da d 3ccc9ed d6 de

4dde761681684d7edad4e5e1ffdb940b

5b9b2a796c88da82d75553c48488b63f

635d926cace851bef7df910d8cb5f647

649e482199c9eb826fa0fca7016c325d

68bf06fb2a8cef72a61b01dcd10fd10d

6aa92a03083a19783ddf4e4913c230d3

781228e0a889c0624a5f1d8e9f5b0b30

849b165f28ae8b1cebe0c7430f44aff3

868180d3f78ae330c8ab4e6c20045930

8bfbda4203cfb4bb7aaeafe7afe9748a

8d207e2b6d13ebd5fc4430ef3670558f

8e81f08432ba7d64c67032a2a5580a48

92defd440acfd41595ce20c9107c3262

93be88ab0908a9359d7e5472ace22fe5

9a111588a7db15b796421bd13a949cd4

9c6539c9f5b3e831d5bcb1357d51d049

b08d4099b14e37ceda1923681a2f70f2

c16feda9ad177c8f7e6a07f57d84851f

c46327a65a1f9bf9c367fbda95f1bd22

c89a06d5b3a55a45b7d508e6b9152aa8

d107c5dc752cd262cd4d6c461c8583c4

d2b8a429bcf9e0eca54939e2cd4408dc

d71d01d469414e992ded9038ea761564

d96bbc2b1e5cc6b085bf04a8e487632e

dd4b6f3216709e193ed9f06c37bcc389

eda730498b3d0a97066807a2d98909f3

Top talking botnet nodes from most recent data

67.58.78.157

78.187.40.53

177.128.34.146

122.165.131.7

80.92.189.5

78.187.20.244

85.105.104.74

94.156.57.84

112.196.16.26


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85 0 8

82.209.9.181

187.85.255.194

103.123.46.51

213.174.31.77

103.97.244.22

103.41.56.62

187.85.248.209

103.59.134.156

88.247.16.223

89.160.95.67

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