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
Wayne Racey
Manager of IT Operations, City of St Catharines (Guest Contributor)
Share
08
Aug 2022
The City of St. Catharines is the largest city in Ontario, Canada’s Niagara Region. We strive to meet the needs of our over 140,000 residents. Cyber disruption could stop our municipality from functioning, so having a strong security stack is critical to our mission.
Globally, 44% of ransomware attacks target municipalities. In Canada, smaller cities have had to deal with increased attempts by threat actors to access information, without significant increases in security staff or budgets.
Data breaches incur an average cost totaling $6.35 million CAD because of ransomware payments, fines for leaked personally identifiable information, or recovery costs. That number does not quantify the additional reputational damage, PR setbacks, and other repercussions. Instead of resigning ourselves to accepting a greater cyber-risk, we turned to Darktrace to protect our network, email, and Microsoft 365 Suite.
How Self-Learning AI buys back time
I’m sure we as a municipality are grappling with the same issues that other cities of a similar size face from a budgetary standpoint. We do not have enough boots on the ground and our IT team is stretched thin. Investigating cyber security incidents takes a lot of time. We must find correlations between several old systems and manually go through security event logs to determine which incidents require follow-up. These factors greatly increased our response time.
When we first implemented Darktrace, we immediately saw that it does all the heavy lifting for us when it comes to the analysis of breach events. The Cyber AI Analyst shows a granular breakdown of the digital traffic coming into and out of the City, all on a single screen. This helps us separate the meaningful data from the noise.
I now start all my investigations with the Cyber AI Analyst. It sets me up with actionable insights that ensure I focus my time and energy in the most productive ways.
Darktrace also saves my team time and labor when it comes to responding to incidents. When it does detect attacks, it autonomously responds in seconds to contain them without interfering with any normal business operations.
We have been able to configure Darktrace’s settings to further streamline our workload. We’ve made several adjustments that reduce the number of helpdesk tickets my team receives, which ensures we’re spending our time on high-value work.
Darktrace not only makes up for the limited resources of our IT team, but also augments us. By simplifying our investigations and autonomously stopping attacks, Darktrace gives us more time to work on our other IT responsibilities without worrying about our security.
Darktrace/Network brings visibility and defense
Before Darktrace, we didn’t have visibility into the east-west traffic on our network. Once installed, it provided a view of traffic we had never anticipated, and we saw connections that we never even knew existed.
Darktrace/Network has insight into every laptop, server, phone, and user. The Self-Learning AI learns the “pattern of life” of our organization, so that it can recognize unusual activity that indicates a cyber-attack. In the case of a serious emerging attack, Darktrace RESPOND can take precise actions to stop it while otherwise allowing normal digital operations.
Darktrace/Network maps connections made within our network, whether between users and servers or between devices. It sorts users into groups that behave similarly, making it more obvious if one acts unusually. Darktrace/Email and Darktrace/Apps extend this coverage to our email and Microsoft 365 Suite, respectively. In this way, Darktrace allows us to see comprehensively into end-user traffic.
Darktrace can stop attempts to download malicious software, move malware laterally, upload private data, and everything in between. This means we are protected from attacks that are notoriously difficult to find, such as stealth attacks, machine speed ransomwares, insider threats, and zero-days.
Darktrace brings peace of mind
The Self-Learning AI has transformed my skepticism of AI into enthusiasm. I now see the possibilities with AI are limited only by one’s imagination, and the Darktrace team has harnessed it to create a great security tool.
Darktrace has proven to be the addition we needed to keep our digital landscape secure while contending with the limitations of budget and staffing during a time of increasingly frequent attacks targeting municipalities. My team’s support for Darktrace has been outstanding, and we have no regrets.
Darktrace gives us the assurance that no matter what rules we put in place regarding the flow of traffic on our network, it will always be present to reconfigure our defenses and safeguard our digital assets should an attack occur. It works 24/7, at machine speed, and augments our IT team. That defines peace of mind!
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
Wayne Racey
Manager of IT Operations, City of St Catharines (Guest Contributor)
Darktrace investigated “PumaBot,” a Go-based Linux botnet targeting IoT devices. It avoids internet-wide scanning, instead using a C2 server to get targets and brute-force SSH credentials. Once inside, it executes remote commands and ensures persistence.
Defending the Frontlines: Proactive Cybersecurity in Local Government
To quickly identify and respond to threats before damage occurs, this local government relies on Darktrace to improve network visibility, stop insider threats, protect its email systems, and accelerate incident investigations.
Catching a RAT: How Darktrace Neutralized AsyncRAT
Darktrace's AI-driven tools identified and disrupted AsyncRAT activity, detecting suspicious connections and blocking them autonomously. This proactive response prevented the compromise from escalating and safeguarded sensitive data from exfiltration.
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.
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].
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.
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.
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.
Figure 3: Part of networkxm checking 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.
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.
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/.
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/).
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
Monitor for anomalous SSH login activity, especially failed login attempts across a wide IP range, which may indicate brute-force attempts.
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.
Inspect authorized_keys files across user accounts for unknown SSH keys that may enable unauthorized access.
Filter or alert on outbound HTTP requests with non-standard headers, such as X-API-KEY: jieruidashabi, which may indicate botnet C2 communication.
Apply strict firewall rules to limit SSH exposure rather than exposing port 22 to the internet.
From Rockstar2FA to FlowerStorm: Investigating a Blooming Phishing-as-a-Service Platform
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].
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.
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.
Figure 3: Cyber AI Analyst’s detection of new login behavior for the SaaS user, including abnormal MFA usage.
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
