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April 5, 2022

How Darktrace Antigena Thwarted Cobalt Strike Attack

Learn how Darktrace's Antigena technology intercepted and delayed a Cobalt Strike intrusion. Discover more cybersecurity news and analyses on Darktrace's blog.
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
Dylan Evans
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05
Apr 2022

In December 2021 several CVEs[1] were issued for the Log4j vulnerabilities that sent security teams into a global panic. Threat actors are now continuously scanning external infrastructure for evidence of the vulnerability to deploy crypto-mining malware.[2] However, through December ‘21 – February ‘22, it was ransomware groups that seized the initiative.

Compromise

In January 2022, a Darktrace customer left an external-facing VMware server unpatched allowing Cobalt Strike to be successfully installed. Several IoCs indicate that Cuba Ransomware operators were behind the attack. Thanks to the Darktrace SOC service, the customer was notified of the active threat on their network, and Antigena’s Autonomous Response was able to keep the attackers at bay before encryption events took place.

Initially the VMware server breached two models relating to an anomalous script download and a new user agent both connecting via HTTP. As referenced in an earlier Darktrace blog, both of these models had been seen in previous Log4j exploits. As with all Darktrace models however, the model deck is not designed to detect only one exploit, infection variant, or APT.

Figure 1: Darktrace models breaching due to the malicious script download

Analyst investigation

A PCAP of the downloaded script showed that it contained heavily obfuscated JavaScript. After an OSINT investigation a similar script was uncovered which likely breached the same Yara rules.

Figure 2: PCAP of the Initial HTTP GET request for the Windows Script component

Figure 3: PCAP of the initial HTTP response containing obfuscated JavaScript

Figure 4: A similar script that has been observed installing additional payloads after an initial infection[3]

While not an exact match, this de-obfuscated code shared similarities to those seen when downloading other banking trojans.

Having identified on the Darktrace UI that this was a VMware server, the analyst isolated the incoming external connections to the server shortly prior to the HTTP GET requests and was able to find an IP address associated with Log4j exploit attempts.

Figure 5: Advanced Search logs showing incoming SSL connections from an IP address linked to Log4j exploits

Through Advanced Search the analyst identified spikes shortly prior and immediately after the download. This suggested the files were downloaded and executed by exploiting the Log4j vulnerability.

Antigena response

Figure 6: AI Analyst reveals both the script downloads and the unusual user agent associated with the connections

Figure 7: Antigena blocked all further connections to these endpoints following the downloads

Cobalt Strike

Cobalt Strike is a popular tool for threat actors as it can be used to perform a swathe of MITRE ATT&CK techniques. In this case the threat actor attempted command and control tactics to pivot through the network, however, Antigena responded promptly when the malware attempted to communicate with external infrastructure.

On Wednesday January 26, the DNS beacon attempted to connect to malicious infrastructure. Antigena responded, and a Darktrace SOC analyst issued an alert.

Figure 8: A Darktrace model detected the suspicious DNS requests and Antigena issued a response

The attacker changed their strategy by switching to a different server “bluetechsupply[.]com” and started issuing commands over TLS. Again, Darktrace detected these connections and AI Analyst reported on the incident (Figure 9, below). OSINT sources subsequently indicated that this destination is affiliated with Cobalt Strike and was only registered 14 days prior to this incident.

Figure 9: AI Analyst summary of the suspicious beaconing activity

Simultaneous to these connections, the device scanned multiple internal devices via an ICMP scan and then scanned the domain controller over key TCP ports including 139 and 445 (SMB). This was followed by an attempt to write an executable file to the domain controller. While Antigena intervened in the file write, another Darktrace SOC analyst was issuing an alert due to the escalation in activity.

Figure 10: AI Analyst summary of the .dll file that Antigena intercepted to the Windows/temp directory of the domain controller

Following the latest round of Antigena blocks, the threat actor attempted to change methods again. The VMware server utilised the Remote Access Tool/Trojan NetSupport Manager in an attempt to install further malware.

Figure 11: Darktrace reveals the attacker changing tactics

Despite this escalation, Darktrace yet again blocked the connection.

Perhaps due to an inability to connect to C2 infrastructure, the attack stopped in its tracks for around 12 hours. Thanks to Antigena and the Darktrace SOC team, the security team had been afforded time to remediate and recover from the active threat in their network. Interestingly, Darktrace detected a final attempt at pivoting from the machine, with an unusual PowerShell Win-RM connection to an internal machine. The modern Win-RM protocol typically utilises port 5985 for HTTP connections however pre-Windows 7 machines may use Windows 7 indicating this server was running an old OS.

Figure 12: Darktrace detects unusual PowerShell usage

Cuba Ransomware

While no active encryption appears to have taken place for this customer, a range of IoCs were identified which indicated that the threat actor was the group being tracked as UNC2596, the operators of Cuba Ransomware.[4]

These IoCs include: one of the initially dropped files (komar2.ps1,[5] revealed by AI Analyst in Figure 6), use of the NetSupport RAT,[6] and Cobalt Strike beaconing.[7] These were implemented to maintain persistence and move laterally across the network.

Cuba Ransomware operators prefer to exfiltrate data to their beacon infrastructure rather than using cloud storage providers, however no evidence of upload activity was observed on the customer’s network.

Concluding thoughts

Unpatched, external-facing VMware servers vulnerable to the Log4j exploit are actively being targeted by threat actors with the aim of ransomware detonation. Without using rules or signatures, Darktrace was able to detect all stages of the compromise. While Antigena delayed the attack, forcing the threat actor to change C2 servers constantly, the Darktrace analyst team relayed their findings to the security team who were able to remediate the compromised machines and prevent a final ransomware payload from detonating.

For Darktrace customers who want to find out more about Cobalt Strike, refer here for an exclusive supplement to this blog.

Appendix

Darktrace model detections

Initial Compromise:

  • Device / New User Agent To Internal Server
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Experimental / Large Number of Suspicious Successful Connections

Breaches from Critical Devices / DC:

  • Device / Large Number of Model Breaches
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Device / SMB Lateral Movement
  • Experimental / Unusual SMB Script Write V2
  • Compliance / High Priority Compliance Model Breach
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Experimental / Possible Cobalt Strike Server IP V2

Lateral Movement:

  • Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Executable Uploaded to DC
  • Experimental / Large Number of Suspicious Failed Connections
  • Compromise / Suspicious Beaconing Behaviour
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Anomalous Connection / High Volume of Connections to Rare Domain
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Network Scan Activity:

  • Device / Suspicious SMB Scanning Activity
  • Experimental / Network Scan V2
  • Device / ICMP Address Scan
  • Experimental / Possible SMB Scanning Activity
  • Experimental / Possible SMB Scanning Activity V2
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Network Scan
  • Compromise / DNS / Possible DNS Beacon
  • Device / Internet Facing Device with High Priority Alert
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

DNS / Cobalt Strike Activity:

  • Experimental / Possible Cobalt Strike Server IP
  • Experimental / Possible Cobalt Strike Server IP V2
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Script from Rare External Location

MITRE ATT&CK techniques observed

IoCs

Thanks to Brianna Leddy, Sam Lister and Marco Alanis for their contributions.

Footnotes

1.

https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44228
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44530
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-45046
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-4104

2. https://www.toolbox.com/it-security/threat-reports/news/log4j-vulnerabilities-exploitation-attempts

3. https://twitter.com/ItsReallyNick/status/899845845906071553

4. https://www.mandiant.com/resources/unc2596-cuba-ransomware

5. https://www.ic3.gov/Media/News/2021/211203-2.pdf

6. https://threatpost.com/microsoft-exchange-exploited-cuba-ransomware/178665/

7. https://www.bleepingcomputer.com/news/security/microsoft-exchange-servers-hacked-to-deploy-cuba-ransomware/

8. https://gist.github.com/blotus/f87ed46718bfdc634c9081110d243166

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
Dylan Evans

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May 29, 2025

Why attack-centric approaches to email security can’t cope with modern threats

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What’s the problem with an attack-centric mindset?

For decades, traditional email security strategies have been built around an attack-centric mindset. Secure Email Gateways (SEGs) and other legacy solutions operate on the principle of identifying and blocking known threats. These systems rely heavily on predefined threat intelligence – blacklists, malware signatures, and reputation-based analysis – to filter out malicious content before it reaches the inbox.

While this approach was sufficient when email threats were relatively static and signature-based, it’s increasingly ineffective against the sophistication of modern attacks. Techniques like spear phishing, business email compromise (BEC), and supply chain attacks often bypass traditional SEG defenses because they lack obvious malicious indicators. Instead, they leverage social engineering, look-alike domains, and finely tuned spoofing tactics that are designed to evade detection.

The challenge extends beyond just legacy SEGs. Many modern email security providers have inherited the same attack-centric principles, even if they've reimagined the technology stack. While some vendors have shifted to API-based deployments and incorporated AI to automate pattern recognition, the underlying approach remains the same: hunting for threats based on known indicators. This methodology, though it’s undergone modernization using AI, still leaves gaps when it comes to novel, hyper-targeted threats that manipulate user behavior rather than deploy predictable malicious signatures. Attack-centric security will always remain one step behind the attacker.

By the way, native email security already covers the basics

One of the most overlooked realities in email security is that native solutions like Microsoft 365’s built-in security already handle much of the foundational work of attack-centric protection. Through advanced threat intelligence, anti-phishing measures, and malware detection, Microsoft 365 actively scans incoming emails for known threats, using global telemetry to identify patterns and block suspicious content before it even reaches the user’s inbox.

This means that for many organizations, a baseline level of protection against more obvious, signature-based attacks is already in place – but many are still disabling these protections in favour of another attack-centric solution. By layering another attack-centric solution on top, they are effectively duplicating efforts without enhancing their security posture. This overlap can lead to unnecessary complexity, higher costs, and a false sense of enhanced protection when in reality, it’s more of the same.

Rather than duplicating attack-centric protections, the real opportunity lies in addressing the gaps that remain: the threats that are specifically crafted to evade traditional detection methods. This is where a business-centric approach becomes indispensable, complementing the foundational security that’s already built into your infrastructure.

Introducing… the business-centric approach

To effectively defend against advanced threats, organizations need to adopt a business-centric approach to email security. Unlike attack-centric models that hunt for known threats, business-centric security focuses on understanding the typical behaviors, relationships, and communication patterns within your organization. Rather than solely reacting to threats as they are identified, this model continuously learns what “normal” looks like for each user and each inbox.

By establishing a baseline of expected behaviors, business-centric solutions can rapidly detect anomalies that suggest compromise, such as sudden changes in sending patterns, unusual login locations, or subtle shifts in communication tone. This proactive detection method is especially powerful against spear phishing, business email compromise (BEC), and supply chain attacks that are engineered to bypass static defenses. This approach also scales with your organization, learning and adapting as new users are onboarded, communication patterns evolve, and external partners are added.

In an era where AI-driven threats are becoming the norm, having email security that knows your users and inboxes better than the attacker does is a critical advantage.

Why native + business-centric email security is the winning formula

By pairing native security with a business-centric model, organizations can cover the full spectrum of threats – from signature-based malware to sophisticated, socially engineered attacks. Microsoft 365’s in-built security manages the foundational risks, while business-centric defense identifies subtle anomalies and targeted threats that legacy approaches miss.

Layering Darktrace on top of your native Microsoft security eliminates duplicate capabilities, costs and workflows without reducing functionality

Rather than layering redundant attack-centric solutions on top of existing protections, the future of email security lies in leveraging what’s already in place and building on it with smarter, behavior-based detection. The Swiss Cheese Model is a useful one to refer to here: by acknowledging that no single defense can offer complete protection, layering defenses that plug each other’s gaps – like slices of Swiss cheese – becomes critical.

This combination also allows security teams to focus their efforts more effectively. With native solutions catching broad-based, known threats, the business-centric layer can prioritize real anomalies, minimizing false positives and accelerating response times. Organizations benefit from reduced overlap, streamlined costs, and a stronger overall security posture.

Download the full guide to take the first step towards achieving your next-generation security stack.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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May 30, 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.

[related-resource]

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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