GorillaBot: Advanced Mirai Variant Targeting IoT Devices with Enhanced DDoS Capabilities

GorillaBot is a sophisticated botnet malware that has been making headlines for its aggressive DDoS attacks. Building on the infamous Mirai botnet framework, this evolved threat targets internet-connected devices with advanced evasion techniques and encryption methods. This analysis breaks down GorillaBot’s technical features, attack vectors, and provides actionable protection measures.

GorillaBot Overview: Key Threat Information

GorillaBot is a recently identified botnet, classified as a variant of the Mirai botnet, which gained notoriety for its role in large-scale Distributed Denial of Service (DDoS) attacks. Mirai, first discovered in 2016, primarily targets internet-connected devices like IoT cameras and routers, exploiting weak or default passwords to build its botnet.

The release of Mirai’s source code has led to numerous variants, with GorillaBot emerging as a significant threat in 2023, launching over 300,000 attacks across more than 100 countries between September 4 and September 27, 2023. These attacks targeted critical sectors including telecommunications, financial institutions, and education.

This malware appears to reuse Mirai’s core logic for DDoS attacks, such as command parsing and communication with control servers. However, it enhances these with custom encryption methods and includes anti-debugging features to evade detection. This makes it a more sophisticated threat compared to the original Mirai.

Technical Analysis of GorillaBot

Security researchers at ANY.RUN have published a detailed analysis of this threat. Our focus here is on the key technical aspects that differentiate GorillaBot from the original Mirai while making it more dangerous and difficult to detect.

Code Reuse and Architecture Support

GorillaBot inherits much of its functionality from Mirai, focusing on DDoS attacks. Analysis of its binary reveals that it supports multiple system architectures:

• ARM – Common in routers and IoT devices
• MIPS – Found in older network devices
• x86_64 – Standard 64-bit PC architecture
• x86 – 32-bit PC architecture

This multi-architecture support enables GorillaBot to infect a wide range of devices, maximizing its potential botnet size and DDoS capability.

Command and Control Infrastructure

The botnet establishes connections with command and control (C2) servers, a practice mirrored from Mirai, but with significant modifications:

• Raw TCP Socket Communication – Uses raw TCP sockets instead of HTTP requests for enhanced stealth
• 32-byte Buffer Protocol – Sends a 32-byte buffer length followed by the buffer itself
• Custom Encryption – Implements proprietary encryption methods not present in original Mirai

The communication protocol between bot and C2 server follows this pattern:

// Simplified representation of GorillaBot's C2 communication
uint32_t buffer_len = htonl(packet_length);
send(fd, &buffer_len, sizeof(uint32_t), MSG_NOSIGNAL);
send(fd, encrypted_packet, packet_length, MSG_NOSIGNAL);

Attack Command Parsing

GorillaBot reuses Mirai’s core logic for parsing attack commands. It implements a function similar to Mirai’s attack_parse that processes incoming commands from the C2 server:

// Pseudocode based on analysis of GorillaBot's attack parsing
void attack_parse(char *buf) {
    int argc = 0;
    char *args[MAX_ARGS+1];
    
    // Tokenize command
    args[argc++] = buf;
    while (argc <= MAX_ARGS) {
        char *delim = strchr(args[argc-1], ' ');
        if (delim == NULL)
            break;
        *delim++ = 0;
        args[argc++] = delim;
    }
    
    // Process attack command
    if (!strcmp(args[0], "UDP"))
        attack_udp(args);
    else if (!strcmp(args[0], "TCP"))
        attack_tcp(args);
    // Additional attack types...
}

This function supports both simple commands and those with extended options, allowing the botnet operators to fine-tune attack parameters.

DDoS Attack Capabilities

GorillaBot’s primary purpose is launching DDoS attacks. It supports up to 19 different attack vectors, significantly more than the original Mirai:

Key Enhancements Over Mirai

Despite its Mirai heritage, GorillaBot introduces several sophisticated enhancements that make it more dangerous and difficult to detect.

Custom Encryption Methods

GorillaBot implements multiple layers of encryption not present in the original Mirai:

• XTEA-like Cipher – Custom implementation with a 128-bit key for C2 communications
• Caesar Cipher – Simple substitution cipher with a shift of 3 for string obfuscation
• SHA-256 Token – Used for authentication with C2 servers

The Caesar cipher implementation for string obfuscation:

// Simplified representation of GorillaBot's Caesar cipher
char* decrypt_string(char* encrypted) {
    char* decrypted = malloc(strlen(encrypted) + 1);
    
    for(int i = 0; encrypted[i] != '\0'; i++) {
        // Shift of 3 in Caesar cipher
        decrypted[i] = encrypted[i] - 3;
    }
    
    decrypted[strlen(encrypted)] = '\0';
    return decrypted;
}

Anti-Analysis and Evasion Techniques

GorillaBot employs several sophisticated techniques to evade detection and analysis:

• Anti-Debugging Checks – Inspects the /proc/self/status file for TracerPid to detect when being analyzed

// Anti-debugging implementation in GorillaBot
int detect_debugger() {
    FILE *f = fopen("/proc/self/status", "r");
    char line[256];
    
    while (fgets(line, sizeof(line), f)) {
        if (strncmp(line, "TracerPid:", 10) == 0) {
            int pid = atoi(line + 10);
            if (pid != 0) {
                fclose(f);
                return 1; // Debugger detected
            }
            break;
        }
    }
    
    fclose(f);
    return 0; // No debugger
}

• Container Detection – Checks /proc/1/cgroup for “kubepods” to detect containerized environments
• Honeypot Evasion – Exits when common sandbox or analysis environments are detected
• Process Name Obfuscation – Disguises its process name to avoid detection by system monitoring tools

Advanced Authentication Mechanism

GorillaBot implements a more sophisticated authentication system than Mirai:

// Pseudocode of GorillaBot's authentication mechanism
int authenticate_with_c2(int sock) {
    uint8_t magic_value[4];
    recv(sock, magic_value, 4, 0);
    
    uint8_t hardcoded_array[32] = { /* 32-byte hardcoded array */ };
    uint8_t token[32];
    
    SHA256_CTX ctx;
    SHA256_Init(&ctx);
    SHA256_Update(&ctx, hardcoded_array, 32);
    SHA256_Update(&ctx, magic_value, 4);
    SHA256_Final(token, &ctx);
    
    send(sock, token, 32, MSG_NOSIGNAL);
    
    uint8_t response;
    recv(sock, &response, 1, 0);
    return response == 0; // 0 means success
}

This authentication process uses a SHA-256 token generated from a 32-byte hardcoded array and a 4-byte magic value received from the C2 server, making it more secure and difficult to impersonate.

Indicators of Compromise (IoCs)

YARA Rule for GorillaBot Detection

The following YARA rule can help detect GorillaBot samples:

rule GorillaBot_Mirai_Variant {
    meta:
        description = "Detects GorillaBot Mirai variant"
        author = "GridinSoft Security Team"
        date = "2023-10-15"
        version = "1.0"
        
    strings:
        $caesar_shift = { 83 ?? 03 } // Caesar cipher with shift of 3
        $proc_status = "/proc/self/status" ascii
        $tracer_pid = "TracerPid:" ascii
        $kubepod_check = "kubepods" ascii
        $attack_cmd1 = "UDP" ascii
        $attack_cmd2 = "TCP" ascii
        $attack_cmd3 = "SYN" ascii
        $attack_cmd4 = "ACK" ascii
        
    condition:
        uint32(0) == 0x464c457f and // ELF header
        $caesar_shift and
        $proc_status and
        $tracer_pid and
        2 of ($attack_cmd*) and
        $kubepod_check
}

How To Protect Against GorillaBot Infection

Given GorillaBot’s sophistication, protecting against such threats requires a multi-layered approach:

For IoT Device Owners

• Change default credentials – Many IoT devices come with factory-set usernames and passwords that are well-known to attackers. Always change these immediately.
• Keep firmware updated – Regularly check for and install firmware updates for all network-connected devices.
• Disable unnecessary services – Turn off Telnet and other unneeded services that could be exploited.
• Implement network segmentation – Place IoT devices on a separate network from critical systems.
• Use strong authentication – Where possible, implement SSH with key-based authentication instead of password login.

For Network Administrators

• Deploy traffic monitoring – Implement systems to detect unusual traffic patterns that could indicate botnet activity.
• Filter vulnerable ports – Block inbound access to commonly exploited ports like Telnet (23) at the network perimeter.
• Implement egress filtering – Prevent compromised devices from participating in DDoS attacks by filtering outbound traffic.
• Configure rate limiting – Implement bandwidth caps or connection rate limits for IoT devices.
• Use intrusion detection systems – Deploy IDS/IPS solutions capable of recognizing botnet command and control traffic.

For End Users

• Use reliable security software – Solutions like GridinSoft Anti-Malware can help detect and remove malware infections.
• Monitor device behavior – Watch for unusual signs like excessive network activity or performance degradation.
• Perform regular security scans – Schedule periodic security scans of all devices connected to your network.
• Update all software – Keep operating systems and applications up-to-date with security patches.

Conclusion: The Evolution of IoT Threats

GorillaBot represents the continued evolution of IoT-targeted malware, building on established frameworks like Mirai while adding sophisticated evasion techniques and enhanced attack capabilities. As IoT devices continue to proliferate across homes and businesses, the threat from such botnets will likely increase.

The most effective defense against these threats remains a combination of basic security hygiene (changing default passwords, keeping devices updated) and advanced protection measures (network monitoring, security software). By implementing these practices, both individual users and organizations can significantly reduce their risk of becoming part of a botnet like GorillaBot.