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March 27, 2025

Python-based Triton RAT Targeting Roblox Credentials

Cado Security Labs (now part of Darktrace) identified Triton RAT, a Python-based open-source tool controlled via Telegram.
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
Tara Gould
Threat Researcher
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27
Mar 2025

Researchers from Cado Security Labs (now part of Darktrace) have identified a Python Remote Access Tool (RAT) named Triton RAT. The open-source RAT is available on GitHub and allows users to remotely access and control a system using Telegram. 

Technical analysis

In the version of the Triton RAT Pastebin. 

Telegram token and chat ID encoded in Base64
Image 1: Telegram token and chat ID encoded in Base64

Features of Triton RAT:

  • Keylogging
  • Remote commands
  • Steal saved passwords
  • Steal Roblox security cookies
  • Change wallpaper
  • Screen recording
  • Webcam access
  • Gather Wifi Information
  • Download/upload file
  • Execute shell commands
  • Steal clipboard data
  • Anti-Analysis
  • Gather system information
  • Data exfiltrated to Telegram Bot

The TritonRAT code contains many functions including the function “sendmessage” which iterates over password stores in AppData, Google, Chrome, User Data, Local, and Local State, decrypts them and saves the passwords in a text file. Additionally, the RAT searches for Roblox security cookies (.ROBLOSECURITY) in Opera, Chrome, Edge, Chromium, Firefox and Brave, if found the cookies are stored in a text file and exfiltrated. A Roblox security cookie is a browser cookie that stores the users’ session and can be used to gain access to the Roblox account bypassing 2FA. 

Function to search for and exfiltrate Roblox security cookies
Image 2: Function used to search for and exfiltrate Roblox security cookies
Function that gathers and exfiltrates system information 
Image 3: Function that gathers and exfiltrates system information 
Secondary payload retrieved from DropBox 
Image 4: Secondary payload retrieved from DropBox 

The Python script also contains code to create a VBScript and a BAT script which are executed with Powershell. The VBScript “updateagent.vbs” disables Windows Defender, creates backups and scheduled tasks for persistence and monitors specified processes. The BAT script “check.bat” retrieves a binary named “ProtonDrive.exe” from DropBox, stores it in a hidden folder and executes it with admin privileges. ProtonDrive is a pyinstaller compiled version of TritonRAT. Presumably the binary is retrieved to set up persistence. Once retrieved, ProtonDrive is stored in a created folder structure “C:\Users\user\AppData\Local\Programs\Proton\Drive”. Three scheduled tasks are created to start on logon of any user.

Tasks created
Image 5: Three tasks created to start on logon of any user

For anti-analysis, Triton RAT contains a function that checks for “blacklisted” processes which include popular tools such as xdbg, ollydbg, FakeNet, and antivirus products. Additionally, the same Git user offers a file resizer as defense evasion as some anti-virus will not check a file over a certain amount of MB.  All the exfiltrated data is sent to Telegram via a Telegram bot, where the user can send commands to the affected machine. At the time of analysis, the Telegram channel/bot had 4549 messages, although it is unknown if these are indicative of the number of infections.  

Conclusion

The emergence of the Python-based Triton RAT highlights how quickly cybercriminals are evolving their tactics to target platforms with large user bases like Roblox. Its persistence mechanisms and reliance on Telegram for data exfiltration make it both resilient and easy for attackers to operate at scale. As threats like this continue to surface, it’s critical for organizations and individuals to reinforce endpoint protection, and promote strong credential security practices to reduce exposure to such attacks.

Indicators of compromise (IoCs)

ProtonDrive.exe

Ea04f1c4016383e0846aba71ac0b0c9c

Related samples:

076dccb222d0869870444fea760c7f2b564481faea80604c02abf74f1963c265

0975fdadbbd60d90afdcb5cc59ad58a22bfdb2c2b00a5da6bb1e09ae702b95e7

1f4e1aa937e81e517bccc3bd8a981553a2ef134c11471195f88f3799720eaa9c

200fdb4f94f93ec042a16a409df383afeedbbc73282ef3c30a91d5f521481f24

29d2a70eeedbe496515c71640771f1f9b71c4af5f5698e2068c6adcac28cc3e0

2b05494926b4b1c79ee0a12a4e7f6c07e04c084a953a4ba980ed7cb9b8bf6bc2

2d1b6bd0b945ddd8261efbd85851656a7351fd892be0fa62cc3346883a8f917e

2dce8fc1584e660a0cba4db2cacdf5ff705b1b3ba75611de0900ebaeaa420bf9

2f27b8987638b813285595762fa3e56fff2213086e9ba4439942cd470fa5669a

3f9ce4d12e0303faa59a307bcfc4366d02ba73e423dbf5bcf1da5178253db64d

4309e6a9abdfedc914df3393110a68bd4acfe922e9cd9f5f24abf23df7022af7

48231f2cf5bda35634fca2f98dc6e8581e8a65a2819d62bc375376fcd501ba2d

49b2ca4c1bd4405aa724ffaef266395be4b4581f1ff38b1fc092eab71e1adb6a

4b32dbd7a6ca7f91e75bacf055f4132be0952385d4d4fcbaf0970913876d64a1

566fc3f32633ce0b9a7154102bc1620a906473d5944dca8dea122cb63cb1bcaa

59793de10ed2d3684d0206f5f69cbebbba61d1f90a79dbd720d26bbf54226695

61a2c53390498716494ffa0b586aa6dc6c67baf03855845e2e3f2539f1f56563

6707ba64cccab61d3a658b23b28b232b1f601e3608b7d9e4767a1c0751bccd05

71fabe5022f613dc8e06d6dfda1327989e67be4e291f3761e84e3a988751caf8

78573a4c23f6ccdcbfce3a467fa93d2a1a49cf2f8dc7b595c0185e16b84828cb

78b246cbd9b1106d01659dd0ab65dc367486855b6b37869673bd98c560b6ff52

7bfdbceded56029bc32d89249e0195ebf47309fecded2b6578b035c52c43460b

7cb501e819fc98a55b9d19ad0f325084f6c4753785e30479502457ac7cb6289c

7fa70e18c414ae523e84c4a01d73e49f86ab816d129e8d7001fb778531adf3a7

8bc29a873b6144b6384a5535df5fc762c0c65e47a2caf0e845382c72f9d6671f

8c1db376bafcd071ffb59130d58ffcde45b2fa8e79dcc44c0a14574b9de55b43

a99ebd095d2ccda69855f2c700048658b8e425c90c916d5880f91c8aba634a2e

b656b7189925b043770a9738d8ae003d7401ac65a58e78c643937f4b44a3bc2c

b8dc2c5921f668f6cf8a355fd1cb79020b6752330be5e0db4bf96ae904d76249

b90af78927c6cb2d767f777d36031c9160aeb6fcd30090c3db3735b71274eb4e

bc1e211206c69fe399505e18380fb0068356d205c7929e2cb3d2fe0b4107d4e0

bf3c84a955f49c02a7f4fbf94dbbf089f26137fc75f5b36ac0b1bace9373d17a

c11d186e6d1600212565786ed481fbe401af598e1f689cf1ce6ff83b5a3b4371

cd42ae47c330c68cc8fd94cf5d91992f55992292b186991605b262ba1f776e8e

e1e2587ae2170d9c4533a6267f9179dff67d03f7adbb6d1fb4f43468d8f42c24

f389a8cbb88dae49559eaa572fc9288c253ed1825b1ce2a61e3d8ae998625e18

fc55895bb7d08e6ab770a05e55a037b533de809196f3019fbff0f1f58e688e5f

MITRE ATT&CK

T1053.005 Scheduled Task/Job: Scheduled Task

T1059.006 Command and Scripting Interpreter: Python

T1082 System Information Discovery

T1016 System Network Configuration Discovery

T1105 Ingress Tool Transfer

T1562.001 Impair Defenses: Disable or Modify Tools

T1132 Data Encoding

T1021 Remote Services

T1056.001 Input Capture: Keylogging

T1555 Credentials from Password Stores

T1539 Steal Web Session Cookie

T1546.015 Event Triggered Execution: Screensaver

T1113 Screen Capture

T1125 Video Capture

T1016 System Network Configuration Discovery

T1105 Ingress Tool Transfer

T1059 Command and Scripting Interpreter

T1115 Clipboard Data

T1497 Virtualization/Sandbox Evasion

T1020 Automated Exfiltration

YARA rule

rule Triton_RAT { 
   meta: 
       description = "Detects Python-based Triton RAT" 
       author = "tgould@cadosecurity.com" 
       date = "2025-03-06" 
   strings: 
       $telegram = "telebot.TeleBot" ascii 
       $extract_data = "def extract_data" ascii 
       $bot_token = "bot_token" ascii 
       $chat_id = "chat_id" ascii 
       $keylogger = "/keylogger" ascii 
       $stop_keylogger = "/stopkeylogger" ascii 
       $passwords = "/passwords" ascii 
       $clipboard = "/clipboard" ascii 
       $roblox_cookie = "/robloxcookie" ascii 
       $wifi_pass = "/wifipass" ascii 
       $sys_commands = "/(shutdown|restart|sleep|altf4|tasklist|taskkill|screenshot|mic|wallpaper|block|unblock)" ascii 
       $win_cmds = /(taskkill \/f \/im|wmic|schtasks \/create|attrib \+h|powershell\.exe -Command|reg add|netsh wlan show profile|net user|whoami|curl ipinfo\.io)/ ascii 
       $startup = "/addstartup" ascii 
       $winblocker = "/winblocker" ascii 
       $startup_scripts = /(C:\\Windows\\System32\\updateagent\.vbs|check\.bat|watchdog\.vbs)/ ascii 
   condition: 
       any of ($telegram, $extract_data, $bot_token, $chat_id) and 
       4 of ($keylogger, $stop_keylogger, $passwords, $clipboard, $roblox_cookie, $wifi_pass, 
             $sys_commands, $win_cmds, $startup, $winblocker, $startup_scripts) 
} 
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
Tara Gould
Threat Researcher

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September 4, 2025

Rethinking Signature-Based Detection for Power Utility Cybersecurity

power utility cybersecurityDefault blog imageDefault blog image

Lessons learned from OT cyber attacks

Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.

2015: BlackEnergy 3 in Ukraine

According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.

2016: Industroyer/CrashOverride

CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.

2017: TRITON Malware

The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.

The broader lessons

These events revealed three enduring truths:

  • Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
  • Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
  • Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.

The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.  

These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.

Strategic risks of overreliance on signatures

  • False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
  • Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
  • Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.

Recommended Alternatives (with real-world OT examples)

 Alternative strategies for detecting cyber attacks in OT
Figure 1: Alternative strategies for detecting cyber attacks in OT

Behavioral and anomaly detection

Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.

Real-world insight:

In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.  

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Threat intelligence sharing & building toward zero-trust philosophy

Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.

Real-world insight:

Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.

This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.

Defense-in-Depth with OT context & unified visibility

OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.

Real-world insight:

Darktrace / OT offers unified AI‑led investigations that break down silos between IT and OT. Smaller teams can see unusual outbound traffic or beaconing from unknown OT devices, swiftly investigate across domains, and get clear visibility into device behavior, even when they lack specialized OT security expertise.  

Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.  

Regulatory alignment and positive direction

Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.

This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.

Conclusion

Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.

A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.

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About the author
Daniel Simonds
Director of Operational Technology

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August 21, 2025

From VPS to Phishing: How Darktrace Uncovered SaaS Hijacks through Virtual Infrastructure Abuse

VPS phishingDefault blog imageDefault blog image

What is a VPS and how are they abused?

A Virtual Private Server (VPS) is a virtualized server that provides dedicated resources and control to users on a shared physical device.  VPS providers, long used by developers and businesses, are increasingly misused by threat actors to launch stealthy, scalable attacks. While not a novel tactic, VPS abuse is has seen an increase in Software-as-a-Service (SaaS)-targeted campaigns as it enables attackers to bypass geolocation-based defenses by mimicking local traffic, evade IP reputation checks with clean, newly provisioned infrastructure, and blend into legitimate behavior [3].

VPS providers like Hyonix and Host Universal offer rapid setup and minimal open-source intelligence (OSINT) footprint, making detection difficult [1][2]. These services are not only fast to deploy but also affordable, making them attractive to attackers seeking anonymous, low-cost infrastructure for scalable campaigns. Such attacks tend to be targeted and persistent, often timed to coincide with legitimate user activity, a tactic that renders traditional security tools largely ineffective.

Darktrace’s investigation into Hyonix VPS abuse

In May 2025, Darktrace’s Threat Research team investigated a series of incidents across its customer base involving VPS-associated infrastructure. The investigation began with a fleet-wide review of alerts linked to Hyonix (ASN AS931), revealing a noticeable spike in anomalous behavior from this ASN in March 2025. The alerts included brute-force attempts, anomalous logins, and phishing campaign-related inbox rule creation.

Darktrace identified suspicious activity across multiple customer environments around this time, but two networks stood out. In one instance, two internal devices exhibited mirrored patterns of compromise, including logins from rare endpoints, manipulation of inbox rules, and the deletion of emails likely used in phishing attacks. Darktrace traced the activity back to IP addresses associated with Hyonix, suggesting a deliberate use of VPS infrastructure to facilitate the attack.

On the second customer network, the attack was marked by coordinated logins from rare IPs linked to multiple VPS providers, including Hyonix. This was followed by the creation of inbox rules with obfuscated names and attempts to modify account recovery settings, indicating a broader campaign that leveraged shared infrastructure and techniques.

Darktrace’s Autonomous Response capability was not enabled in either customer environment during these attacks. As a result, no automated containment actions were triggered, allowing the attack to escalate without interruption. Had Autonomous Response been active, Darktrace would have automatically blocked connections from the unusual VPS endpoints upon detection, effectively halting the compromise in its early stages.

Case 1

Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.
Figure 1: Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.

Initial Intrusion

On May 19, 2025, Darktrace observed two internal devices on one customer environment initiating logins from rare external IPs associated with VPS providers, namely Hyonix and Host Universal (via Proton VPN). Darktrace recognized that these logins had occurred within minutes of legitimate user activity from distant geolocations, indicating improbable travel and reinforcing the likelihood of session hijacking. This triggered Darktrace / IDENTITY model “Login From Rare Endpoint While User Is Active”, which highlights potential credential misuse when simultaneous logins occur from both familiar and rare sources.  

Shortly after these logins, Darktrace observed the threat actor deleting emails referring to invoice documents from the user’s “Sent Items” folder, suggesting an attempt to hide phishing emails that had been sent from the now-compromised account. Though not directly observed, initial access in this case was likely achieved through a similar phishing or account hijacking method.

 Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.
Figure 2: Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.

Case 2

Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.
Figure 3: Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.

In the second customer environment, Darktrace observed similar login activity originating from Hyonix, as well as other VPS providers like Mevspace and Hivelocity. Multiple users logged in from rare endpoints, with Multi-Factor Authentication (MFA) satisfied via token claims, further indicating session hijacking.

Establishing control and maintaining persistence

Following the initial access, Darktrace observed a series of suspicious SaaS activities, including the creation of new email rules. These rules were given minimal or obfuscated names, a tactic often used by attackers to avoid drawing attention during casual mailbox reviews by the SaaS account owner or automated audits. By keeping rule names vague or generic, attackers reduce the likelihood of detection while quietly redirecting or deleting incoming emails to maintain access and conceal their activity.

One of the newly created inbox rules targeted emails with subject lines referencing a document shared by a VIP at the customer’s organization. These emails would be automatically deleted, suggesting an attempt to conceal malicious mailbox activity from legitimate users.

Mirrored activity across environments

While no direct lateral movement was observed, mirrored activity across multiple user devices suggested a coordinated campaign. Notably, three users had near identical similar inbox rules created, while another user had a different rule related to fake invoices, reinforcing the likelihood of a shared infrastructure and technique set.

Privilege escalation and broader impact

On one account, Darktrace observed “User registered security info” activity was shortly after anomalous logins, indicating attempts to modify account recovery settings. On another, the user reset passwords or updated security information from rare external IPs. In both cases, the attacker’s actions—including creating inbox rules, deleting emails, and maintaining login persistence—suggested an intent to remain undetected while potentially setting the stage for data exfiltration or spam distribution.

On a separate account, outbound spam was observed, featuring generic finance-related subject lines such as 'INV#. EMITTANCE-1'. At the network level, Darktrace / NETWORK detected DNS requests from a device to a suspicious domain, which began prior the observed email compromise. The domain showed signs of domain fluxing, a tactic involving frequent changes in IP resolution, commonly used by threat actors to maintain resilient infrastructure and evade static blocklists. Around the same time, Darktrace detected another device writing a file named 'SplashtopStreamer.exe', associated with the remote access tool Splashtop, to a domain controller. While typically used in IT support scenarios, its presence here may suggest that the attacker leveraged it to establish persistent remote access or facilitate lateral movement within the customer’s network.

Conclusion

This investigation highlights the growing abuse of VPS infrastructure in SaaS compromise campaigns. Threat actors are increasingly leveraging these affordable and anonymous hosting services to hijack accounts, launch phishing attacks, and manipulate mailbox configurations, often bypassing traditional security controls.

Despite the stealthy nature of this campaign, Darktrace detected the malicious activity early in the kill chain through its Self-Learning AI. By continuously learning what is normal for each user and device, Darktrace surfaced subtle anomalies, such as rare login sources, inbox rule manipulation, and concurrent session activity, that likely evade traditional static, rule-based systems.

As attackers continue to exploit trusted infrastructure and mimic legitimate user behavior, organizations should adopt behavioral-based detection and response strategies. Proactively monitoring for indicators such as improbable travel, unusual login sources, and mailbox rule changes, and responding swiftly with autonomous actions, is critical to staying ahead of evolving threats.

Credit to Rajendra Rushanth (Cyber Analyst), Jen Beckett (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

References

·      1: https://cybersecuritynews.com/threat-actors-leveraging-vps-hosting-providers/

·      2: https://threatfox.abuse.ch/asn/931/

·      3: https://www.cyfirma.com/research/vps-exploitation-by-threat-actors/

Appendices

Darktrace Model Detections

•   SaaS / Compromise / Unusual Login, Sent Mail, Deleted Sent

•   SaaS / Compromise / Suspicious Login and Mass Email Deletes

•   SaaS / Resource / Mass Email Deletes from Rare Location

•   SaaS / Compromise / Unusual Login and New Email Rule

•   SaaS / Compliance / Anomalous New Email Rule

•   SaaS / Resource / Possible Email Spam Activity

•   SaaS / Unusual Activity / Multiple Unusual SaaS Activities

•   SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

•   SaaS / Access / Unusual External Source for SaaS Credential Use

•   SaaS / Compromise / High Priority Login From Rare Endpoint

•   SaaS / Compromise / Login From Rare Endpoint While User Is Active

List of Indicators of Compromise (IoCs)

Format: IoC – Type – Description

•   38.240.42[.]160 – IP – Associated with Hyonix ASN (AS931)

•   103.75.11[.]134 – IP – Associated with Host Universal / Proton VPN

•   162.241.121[.]156 – IP – Rare IP associated with phishing

•   194.49.68[.]244 – IP – Associated with Hyonix ASN

•   193.32.248[.]242 – IP – Used in suspicious login activity / Mullvad VPN

•   50.229.155[.]2 – IP – Rare login IP / AS 7922 ( COMCAST-7922 )

•   104.168.194[.]248 – IP – Rare login IP / AS 54290 ( HOSTWINDS )

•   38.255.57[.]212 – IP – Hyonix IP used during MFA activity

•   103.131.131[.]44 – IP – Hyonix IP used in login and MFA activity

•   178.173.244[.]27 – IP – Hyonix IP

•   91.223.3[.]147 – IP – Mevspace Poland, used in multiple logins

•   2a02:748:4000:18:0:1:170b[:]2524 – IPv6 – Hivelocity VPS, used in multiple logins and MFA activity

•   51.36.233[.]224 – IP – Saudi ASN, used in suspicious login

•   103.211.53[.]84 – IP – Excitel Broadband India, used in security info update

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

•   Initial Access – T1566 – Phishing

                       T1566.001 – Spearphishing Attachment

•   Execution – T1078 – Valid Accounts

•   Persistence – T1098 – Account Manipulation

                       T1098.002 – Exchange Email Rules

•   Command and Control – T1071 – Application Layer Protocol

                       T1071.001 – Web Protocols

•   Defense Evasion – T1036 – Masquerading

•   Defense Evasion – T1562 – Impair Defenses

                       T1562.001 – Disable or Modify Tools

•   Credential Access – T1556 – Modify Authentication Process

                       T1556.004 – MFA Bypass

•   Discovery – T1087 – Account Discovery

•      Impact – T1531 – Account Access Removal

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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About the author
Rajendra Rushanth
Cyber Analyst
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