Mirai-based Botnet – Moobot Targets Hikvision Vulnerability
FortiGuard Labs Threat Research Report
Affected platforms: Hikvision Product
Impact parties: IP Cam/NVR
Impact: Attacker can exploit the vulnerability to launch a command injection attack by sending some messages with malicious commands in the web server
Severity: Critical
Last September 18th, a threat researcher released a write-up about a remote code execution vulnerability that affects various products from Hikvision, one of the largest video surveillance brands in the world. Hikvision is a CVE CNA and quickly assigned the CVE number, CVE-2021-36260 and released a patch for the vulnerability on the same day as the threat researcher’s disclosure. Shortly after, FortiGuard Labs developed an IPS signature to address it.
During our analysis, we observed numerous payloads attempting to leverage this vulnerability to probing the status of devices or extracting sensitive data from victims. One payload in particular caught our attention. It tries to drop a downloader that exhibits infection behavior and that also executes Moobot, which is a DDoS botnet based on Mirai. In this blog we explain how an attacker delivers this payload through the Hikvision vulnerability, along with details of the botnet.
Stage 0 – Exploitation and Propagation
CVE-2021-36260 results from insufficient input validation, allowing unauthenticated users to inject malicious content into a <language> tag to trigger a command injection attack on a Hikvision product. Below is an example of a request leveraging this exploit:
We collected a number of payloads leveraging this vulnerability, and eventually found a downloader. After tracing the traffic capture, the complete payload is shown in the following figure:
First, because the final Moobot will be saved as “macHelper,” it first tries to remove any file already named “macHelper.” It then echoes code into “downloader,” which is a small ELF 32-bit LSB ARM file. After downloader completes downloading, it executes Moobot with the parameter “hikivision”. Finally, it changes commonly used commands, such as “reboot,” to prevent an administrator from invoking reboot on the affected device.
Stage 1 – Downloader
The attacker leverages this vulnerability to drop a downloader (SHA256: 1DCE6F3BA4A8D355DF21A17584C514697EE0C37B51AB5657BC5B3A297B65955F). It has only one job: download the main botnet. It downloads the malware with “/arm5” URI form server 199.195.250[.]233:80 and prints “RAY” if the downloading process was successful. The following image shows the disassembled code:
From the IP address we not only get the moobot variants for different architectures, we also get the historic malware from directory “/h/“.
Stage 2 – Moobot
Based on our analysis, the malware (SHA256: 38414BB5850A7076F4B33BF81BAC9DB0376A4DF188355FAC39D80193D7C7F557) downloaded in the previous stage is Moobot, which is Mirai-based. Its most obvious feature is that it contains the data string “w5q6he3dbrsgmclkiu4to18npavj702f”, which is used in the “rand_alphastr” function. It is used to create random alphanumeric strings with different purposes, such as for a setup process name or to generate data for attacking.
It also has some elements from Satori, which is another Mirai variant botnet. It contains a “downloader” that targets a victim’s IoT devices, and it prints a “9xsspnvgc8aj5pi7m28p” string after execution. This variant also forks itself with the process name “/usr/sbin*” to try to look like a normal process while wiping out the original file, “macHelper”.
Since it is based on Mirai, the botnet also contains a data section to store its configuration. The plaintext configuration can be decoded after XOR with 0x22:
After getting the C2 server (life.zerobytes[.]cc) from its configuration, it starts sending heartbeat (x00x00) packets and then waits for the next control command from the C2 server. Once the victim system receives the command, it starts a DDoS attack to a specific IP address and port number. One example of the DDoS attack traffic is shown below:
The DDoS attack command is 24 bytes and can be seen in the Data section in Figure 8. This detail is illustrated in the following figure, which includes the flood method and the target IP/Port. Except for SYN flood, the C2 server has other attacking commands, such as 0x06 for UDP flood, 0x04 for ACK flood, and 0x05 for ACK+PUSH flood.
The complete attack scenario from trying to infect Hikvision product to deploying Moobot is shown in figure 10:
We also noticed that a DDoS service provider based the packet capture from our machine in Figure 11. We tracked down a telegram channel named “tianrian,” which provides a DDoS service. They use a specific string, “openmeokbye”, in their login interface, shown in Figure 12. This channel was created on June 11, 2021, and started its service in August. From the chatting channel we can see that the service is still updating. Users should always look out for DDoS attacks and apply patches to vulnerable devices.
Conclusion
Hikvision is one the biggest provider of IP cam/NVR products in the global market. CVE-2021-36260 is a critical vulnerability that makes Hikvision products a target for Moobot. In this blog we showed how an attacker can leverage CVE-2021-36260 and elaborated in detail each stage of the process.
Although a patch has been released to address this vulnerability, this IoT botnet will never stop looking for a vulnerable end point. Because of this, users should upgrade affected devices immediately as well as apply FortiGuard protection.
Fortinet Protections
Fortinet released IPS signature Hikvision.Product.SDK.WebLanguage.Tag.Command.Injection for CVE-2021-36260 to proactively protect our customers. The signature is officially released in IPS definition version 18.192.
The downloader and all related malware from that site are detected and blocked by FortiGuard AntiVirus:
ELF/Mirai.AE!tr
ELF/Mirai.BO!tr
ELF/Mirai.D!tr
ELF/Mirai.AYU!tr
ELF/Mirai.WJ!tr
Linux/Mirai.WJ!tr
Both the downloading URL and C2 server have been rated as "Malicious Websites" by the FortiGuard Web Filtering service.
IOCs
SHA256:
1DCE6F3BA4A8D355DF21A17584C514697EE0C37B51AB5657BC5B3A297B65955F
38414BB5850A7076F4B33BF81BAC9DB0376A4DF188355FAC39D80193D7C7F557
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