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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

The biggest sources of risk in the cloud are misconfigurations, IAM failures, and infrastructure that is unprepared to handle cross-domain threats. Learn how AI-powered cloud security tools can help security teams identify and mitigate these risks.
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
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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06
May 2025

With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

[related-resource]

Tackling the 11 Biggest Cloud Threats

Discover how Darktrace / CLOUD uses AI to help security teams protect against the top cloud risks and most pressing attack types in the white paper

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
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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May 28, 2025

PumaBot: Novel Botnet Targeting IoT Surveillance Devices

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Introduction: PumaBot attacking IoT devices

Darktrace researchers have identified a custom Go-based Linux botnet named “PumaBot” targeting embedded Linux Internet of Things (IoT) devices. Rather than scanning the Internet, the malware retrieves a list of targets from a command-and-control (C2) server and attempts to brute-force SSH credentials. Upon gaining access, it receives remote commands and establishes persistence using system service files. This blog post provides a breakdown of its key functionalities, and explores binaries related to the campaign.

Technical Analysis

Filename: jierui

md5: cab6f908f4dedcdaedcdd07fdc0a8e38

The Go-based botnet gains initial access through brute-forcing SSH credentials across a list of harvested IP addresses. Once it identifies a valid credential pair, it logs in, deploys itself, and begins its replication process.

Overview of Jierui functions
Figure 1: Overview of Jierui functions.

The domain associated with the C2 server did not resolve to an IP address at the time of analysis. The following details are a result of static analysis of the malware.

The malware begins by retrieving a list of IP addresses of likely devices with open SSH ports from the C2 server (ssh.ddos-cc[.]org) via the getIPs() function. It then performs brute-force login attempts on port 22 using credential pairs also obtained from the C2 through the readLinesFromURL(), brute(), and trySSHLogin() functions.

Within trySSHLogin(), the malware performs several environment fingerprinting checks. These are used to avoid honeypots and unsuitable execution environments, such as restricted shells. Notably, the malware checks for the presence of the string “Pumatronix”, a manufacturer of surveillance and traffic camera systems, suggesting potential IoT targeting or an effort to evade specific devices [1].

Fingerprinting of “Pumatronix”.
Figure 2: Fingerprinting of “Pumatronix”.

If the environment passes these checks, the malware executes uname -a to collect basic system information, including the OS name, kernel version, and architecture. This data, along with the victim's IP address, port, username, and password, is then reported back to the C2 in a JSON payload.

Of note, the bot uses X-API-KEY: jieruidashabi, within a custom header when it communicates with the C2 server over HTTP.

The malware writes itself to /lib/redis, attempting to disguise itself as a legitimate Redis system file. It then creates a persistent systemd service in /etc/systemd/system, named either redis.service or mysqI.service (note the spelling of mysql with a capital I) depending on what has been hardcoded into the malware. This allows the malware to persist across reboots while appearing benign.

[Unit]
Description=redis Server Service

[Service]
Type=simple
Restart=always
RestartSec=1
User=root
ExecStart=/lib/redis e

[Install]
WantedBy=multi-user.target

In addition to gaining persistence with a systemd service, the malware also adds its own SSH keys into the users’ authorized_keys file. This ensures that access can be maintained, even if the service is removed.

A function named cleankill() contains an infinite loop that repeatedly attempts to execute the commands “xmrig” and “networkxm”. These are launched without full paths, relying on the system's PATH variable suggesting that the binaries may be downloaded or unpacked elsewhere on the system. The use of “time.Sleep” between attempts indicates this loop is designed to ensure persistence and possibly restart mining components if they are killed or missing.

During analysis of the botnet, Darktrace discovered related binaries that appear to be part of a wider campaign targeting Linux systems.

Filename: ddaemon
Md5: 48ee40c40fa320d5d5f8fc0359aa96f3

Ddaemon is a Go-based backdoor. The malware begins by parsing command line arguments and if conditions are met, enters a loop where it periodically verifies the MD5 hash of the binary. If the check fails or an update is available, it downloads a new version from a C2 server (db.17kp[.]xyz/getDdaemonMd5), verifies it and replaces the existing binary with a file of the same name and similar functionality (8b37d3a479d1921580981f325f13780c).

The malware uses main_downloadNetwork() to retrieve the binary “networkxm” into /usr/src/bao/networkxm. Additionally, the bash script “installx.sh” is also retrieved from the C2 and executed. The binary ensures persistence by writing a custom systemd service unit that auto starts on boot and executes ddaemon.

Filename: networkxm
Md5: be83729e943d8d0a35665f55358bdf88

The networkxm binary functions as an SSH brute-force tool, similar to the botnet. First it checks its own integrity using MD5 hashes and contacts the C2 server (db.17kp[.]xyz) to compare its hash with the latest version. If an update is found, it downloads and replaces itself.

Part of networkxm checking MD5 hash.
Figure 3: Part of networkxm checking MD5 hash.
MD5 hash
Figure 4: MD5 hash

After verifying its validity, it enters an infinite loop where it fetches a password list from the C2 (/getPassword), then attempts SSH connections across a list of target IPs from the /getIP endpoint. As with the other observed binaries, a systemd service is created if it doesn’t already exist for persistence in /etc/systemd/system/networkxm.service.

Bash script installx.sh.
Figure 5: Bash script installx.sh.

Installx.sh is a simple bash script used to retrieve the script “jc.sh” from 1.lusyn[.]xyz, set permissions, execute and clear bash history.

Figure 6: Snippet of bash script jc.sh.

The script jc.sh starts by detecting the operating system type Debian-based or Red Hat-based and determines the location of the pam_unix.so file. Linux Pluggable Authentication Modules (PAM) is a framework that allows for flexible and centralized user authentication on Linux systems. PAM allows system administrators to configure how users are authenticated for services like login, SSH, or sudo by plugging in various authentication modules.

Jc.sh then attempts to fetch the current version of PAM installed on the system and formats that version to construct a URL. Using either curl or wget, the script downloads a replacement pam_unix.so file from a remote server and replaces the existing one, after disabling file immutability and backing up the original.

The script also downloads and executes an additional binary named “1” from the same remote server. Security settings are modified including enabling PAM in the SSH configuration and disabling SELinux enforcement, before restarting the SSH service. Finally, the script removes itself from the system.

Filename: Pam_unix.so_v131
md5: 1bd6bcd480463b6137179bc703f49545

Based on the PAM version that is retrieved from the bash query, the new malicious PAM replaces the existing PAM file. In this instance, pam_unix.so_v131 was retrieved from the server based on version 1.3.1. The purpose of this binary is to act as a rootkit that steals credentials by intercepting successful logins. Login data can include all accounts authenticated by PAM, local and remote (SSH). The malware retrieves the logged in user, the password and verifies that the password is valid. The details are stored in a file “con.txt” in /usr/bin/.

Function storing logins to con.txt
Figure 7: Function storing logins to con.txt

Filename: 1

md5: cb4011921894195bcffcdf4edce97135

In addition to the malicious PAM file, a binary named “1” is also retrieved from the server http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/jc/1. The binary “1” is used as a watcher for the malicious PAM file using inotify to monitor for “con.txt” being written or moved to /usr/bin/.

Following the daemonize() function, the binary is run daemonized ensuring it runs silently in the background. The function read_and_send_files() is called which reads the contents of “/usr/bin/con.txt”, queries the system IP with ifconfig.me, queries SSH ports and sends the data to the remote C2 (http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/api/).

Command querying SSH ports.
Figure 8: Command querying SSH ports.

For persistence, a systemd service (my_daemon.service) is created to autostart the binary and ensure it restarts if the service has been terminated. Finally, con.txt is deleted, presumably to remove traces of the malware.

Conclusion

The botnet represents a persistent Go-based SSH threat that leverages automation, credential brute-forcing, and native Linux tools to gain and maintain control over compromised systems. By mimicking legitimate binaries (e.g., Redis), abusing systemd for persistence, and embedding fingerprinting logic to avoid detection in honeypots or restricted environments, it demonstrates an intent to evade defenses.

While it does not appear to propagate automatically like a traditional worm, it does maintain worm-like behavior by brute-forcing targets, suggesting a semi-automated botnet campaign focused on device compromise and long-term access.

Recommendations

  1. Monitor for anomalous SSH login activity, especially failed login attempts across a wide IP range, which may indicate brute-force attempts.
  2. Audit systemd services regularly. Look for suspicious entries in /etc/systemd/system/ (e.g., misspelled or duplicate services like mysqI.service) and binaries placed in non-standard locations such as /lib/redis.
  3. Inspect authorized_keys files across user accounts for unknown SSH keys that may enable unauthorized access.
  4. Filter or alert on outbound HTTP requests with non-standard headers, such as X-API-KEY: jieruidashabi, which may indicate botnet C2 communication.
  5. Apply strict firewall rules to limit SSH exposure rather than exposing port 22 to the internet.

Appendices

References

1.     https://pumatronix.com/

Indicators of Compromise (IoCs)

Hashes

cab6f908f4dedcdaedcdd07fdc0a8e38 - jierui

a9412371dc9247aa50ab3a9425b3e8ba - bao

0e455e06315b9184d2e64dd220491f7e - networkxm

cb4011921894195bcffcdf4edce97135 - 1
48ee40c40fa320d5d5f8fc0359aa96f3 - ddaemon
1bd6bcd480463b6137179bc703f49545 - pam_unix.so_v131

RSA Key

ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC0tH30Li6Gduh0Jq5A5dO5rkWTsQlFttoWzPFnGnuGmuF+fwIfYvQN1z+WymKQmX0ogZdy/CEkki3swrkq29K/xsyQQclNm8+xgI8BJdEgTVDHqcvDyJv5D97cU7Bg1OL5ZsGLBwPjTo9huPE8TAkxCwOGBvWIKUE3SLZW3ap4ciR9m4ueQc7EmijPHy5qds/Fls+XN8uZWuz1e7mzTs0Pv1x2CtjWMR/NF7lQhdi4ek4ZAzj9t/2aRvLuNFlH+BQx+1kw+xzf2q74oBlGEoWVZP55bBicQ8tbBKSN03CZ/QF+JU81Ifb9hy2irBxZOkyLN20oSmWaMJIpBIsh4Pe9 @root

Network

http://ssh[.]ddos-cc.org:55554

http://ssh[.]ddos-cc.org:55554/log_success

http://ssh[.]ddos-cc.org:55554/get_cmd

http://ssh[.]ddos-cc.org:55554/pwd.txt

https://dow[.]17kp.xyz/

https://input[.]17kp.xyz/

https://db[.]17kp[.]xyz/

http://1[.]lusyn[.]xyz

http://1[.]lusyn[.]xyz/jc/1

http://1[.]lusyn[.]xyz/jc/jc.sh

http://1[.]lusyn[.]xyz/jc/aa

http://1[.]lusyn[.]xyz/jc/cs

http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/api

http://dasfsdfsdfsdfasfgbczxxc[.]lusyn[.]xyz/jc

Detection Rule

rule Linux_PumaBot

{

  meta:

      description = "Rule to match on PumaBot samples"

      author = "tgould@cadosecurity.com"

  strings:

      $xapikey = "X-API-KEY" ascii

      $get_ips = "?count=5000" ascii

      $exec_start = "ExecStart=/lib/redis" ascii

      $svc_name1 = "redis.service" ascii

      $svc_name2 = "mysqI.service" ascii

      $uname = "uname -a" ascii

      $pumatronix = "Pumatronix" ascii

  condition:

      uint32(0) == 0x464c457f and

      all of (

          $xapikey,

          $uname,

          $get_ips,

          $exec_start

      ) and any of (

          $svc_name1,

          $svc_name2

      ) and $pumatronix

}

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About the author
Tara Gould
Threat Researcher

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May 27, 2025

From Rockstar2FA to FlowerStorm: Investigating a Blooming Phishing-as-a-Service Platform

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What is FlowerStorm?

FlowerStorm is a Phishing-as-a-Service (PhaaS) platform believed to have gained traction following the decline of the former PhaaS platform Rockstar2FA. It employs Adversary-in-the-Middle (AitM) attacks to target Microsoft 365 credentials. After Rockstar2FA appeared to go dormant, similar PhaaS portals began to emerge under the name FlowerStorm. This naming is likely linked to the plant-themed terminology found in the HTML titles of its phishing pages, such as 'Sprout' and 'Blossom'. Given the abrupt disappearance of Rockstar2FA and the near-immediate rise of FlowerStorm, it is possible that the operators rebranded to reduce exposure [1].

External researchers identified several similarities between Rockstar2FA and FlowerStorm, suggesting a shared operational overlap. Both use fake login pages, typically spoofing Microsoft, to steal credentials and multi-factor authentication (MFA) tokens, with backend infrastructure hosted on .ru and .com domains. Their phishing kits use very similar HTML structures, including randomized comments, Cloudflare turnstile elements, and fake security prompts. Despite Rockstar2FA typically being known for using automotive themes in their HTML titles, while FlowerStorm shifted to a more botanical theme, the overall design remained consistent [1].

Despite these stylistic differences, both platforms use similar credential capture methods and support MFA bypass. Their domain registration patterns and synchronized activity spikes through late 2024 suggest shared tooling or coordination [1].

FlowerStorm, like Rockstar2FA, also uses their phishing portal to mimic legitimate login pages such as Microsoft 365 for the purpose of stealing credentials and MFA tokens while the portals are relying heavily on backend servers using top-level domains (TLDs) such as .ru, .moscow, and .com. Starting in June 2024, some of the phishing pages began utilizing Cloudflare services with domains such as pages[.]dev. Additionally, usage of the file “next.php” is used to communicate with their backend servers for exfiltration and data communication. FlowerStorm’s platform focuses on credential harvesting using fields such as email, pass, and session tracking tokens in addition to supporting email validation and MFA authentications via their backend systems [1].

Darktrace’s coverage of FlowerStorm Microsoft phishing

While multiple suspected instances of the FlowerStorm PhaaS platform were identified during Darktrace’s investigation, this blog will focus on a specific case from March 2025. Darktrace’s Threat Research team analyzed the affected customer environment and discovered that threat actors were accessing a Software-as-a-Service (SaaS) account from several rare external IP addresses and ASNs.

Around a week before the first indicators of FlowerStorm were observed, Darktrace detected anomalous logins via Microsoft Office 365 products, including Office365 Shell WCSS-Client and Microsoft PowerApps.  Although not confirmed in this instance, Microsoft PowerApps could potentially be leveraged by attackers to create phishing applications or exploit vulnerabilities in data connections [2].

Darktrace’s detection of the unusual SaaS credential use.
Figure 1: Darktrace’s detection of the unusual SaaS credential use.

Following this initial login, Darktrace observed subsequent login activity from the rare source IP, 69.49.230[.]198. Multiple open-source intelligence (OSINT) sources have since associated this IP with the FlowerStorm PhaaS operation [3][4].  Darktrace then observed the SaaS user resetting the password on the Core Directory of the Azure Active Directory using the user agent, O365AdminPortal.

Given FlowerStorm’s known use of AitM attacks targeting Microsoft 365 credentials, it seems highly likely that this activity represents an attacker who previously harvested credentials and is now attempting to escalate their privileges within the target network.

Darktrace / IDENTITY’s detection of privilege escalation on a compromised SaaS account, highlighting unusual login activity and a password reset event.
Figure 2: Darktrace / IDENTITY’s detection of privilege escalation on a compromised SaaS account, highlighting unusual login activity and a password reset event.

Notably, Darktrace’s Cyber AI Analyst also detected anomalies during a number of these login attempts, which is significant given FlowerStorm’s known capability to bypass MFA and steal session tokens.

Cyber AI Analyst’s detection of new login behavior for the SaaS user, including abnormal MFA usage.
Figure 3: Cyber AI Analyst’s detection of new login behavior for the SaaS user, including abnormal MFA usage.
Multiple login and failed login events were observed from the anomalous source IP over the month prior, as seen in Darktrace’s Advanced Search.
Figure 4: Multiple login and failed login events were observed from the anomalous source IP over the month prior, as seen in Darktrace’s Advanced Search.

In response to the suspicious SaaS activity, Darktrace recommended several Autonomous Response actions to contain the threat. These included blocking the user from making further connections to the unusual IP address 69.49.230[.]198 and disabling the user account to prevent any additional malicious activity. In this instance, Darktrace’s Autonomous Response was configured in Human Confirmation mode, requiring manual approval from the customer’s security team before any mitigative actions could be applied. Had the system been configured for full autonomous response, it would have immediately blocked the suspicious connections and disabled any users deviating from their expected behavior—significantly reducing the window of opportunity for attackers.

Figure 5: Autonomous Response Actions recommended on this account behavior; This would result in disabling the user and blocking further sign-in activity from the source IP.

Conclusion

The FlowerStorm platform, along with its predecessor, RockStar2FA is a PhaaS platform known to leverage AitM attacks to steal user credentials and bypass MFA, with threat actors adopting increasingly sophisticated toolkits and techniques to carry out their attacks.

In this incident observed within a Darktrace customer's SaaS environment, Darktrace detected suspicious login activity involving abnormal VPN usage from a previously unseen IP address, which was subsequently linked to the FlowerStorm PhaaS platform. The subsequent activity, specifically a password reset, was deemed highly suspicious and likely indicative of an attacker having obtained SaaS credentials through a prior credential harvesting attack.

Darktrace’s prompt detection of these SaaS anomalies and timely notifications from its Security Operations Centre (SOC) enabled the customer to mitigate and remediate the threat before attackers could escalate privileges and advance the attack, effectively shutting it down in its early stages.

Credit to Justin Torres (Senior Cyber Analyst), Vivek Rajan (Cyber Analyst), Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Alert Detections

·      SaaS / Access / M365 High Risk Level Login

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

·      SaaS / Compromise / Login from Rare High-Risk Endpoint

·      SaaS / Compromise / SaaS Anomaly Following Anomalous Login

·      SaaS / Compromise / Unusual Login and Account Update

·      SaaS / Unusual Activity / Unusual MFA Auth and SaaS Activity

Cyber AI Analyst Coverage

·      Suspicious Access of Azure Active Directory  

·      Suspicious Access of Azure Active Directory  

List of Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

69.49.230[.]198 – Source IP – Malicious IP Associated with FlowerStorm, Observed in Login Activity

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique  

Cloud Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078.004 - T1078

Cloud Service Dashboard - DISCOVERY - T1538

Compromise Accounts - RESOURCE DEVELOPMENT - T1586

Steal Web Session Cookie - CREDENTIAL ACCESS - T1539

References:

[1] https://news.sophos.com/en-us/2024/12/19/phishing-platform-rockstar-2fa-trips-and-flowerstorm-picks-up-the-pieces/

[2] https://learn.microsoft.com/en-us/security/operations/incident-response-playbook-compromised-malicious-app

[3] https://www.virustotal.com/gui/ip-address/69.49.230.198/community

[4] https://otx.alienvault.com/indicator/ip/69.49.230.198

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About the author
Justin Torres
Cyber Analyst
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