January 31, 2024

How Darktrace Defeated SmokeLoader Malware

Read how Darktrace's AI identified and neutralized SmokeLoader malware. Gain insights into their proactive approach to cybersecurity.

Written by

Patrick Anjos

Senior Cyber Analyst

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

Patrick Anjos

Senior Cyber Analyst

Jan 2024

What is Loader Malware?

Loader malware is a type of malicious software designed primarily to infiltrate a system and then download and execute additional malicious payloads.

In recent years, loader malware has emerged as a significant threat for organizations worldwide. This trend is expected to continue given the widespread availability of many loader strains within the Malware-as-a-Service (MaaS) marketplace. The MaaS marketplace contains a wide variety of innovative strains which are both affordable, with toolkits ranging from USD 400 to USD 1,650 [1], and continuously improving, aiming to avoid traditional detection mechanisms.

SmokeLoader is one such example of a MaaS strain that has been observed in the wild since 2011 and continues to pose a significant threat to organizations and their security teams.

How does SmokeLoader Malware work?

SmokeLoader’s ability to drop an array of different malware strains onto infected systems, from backdoors, ransomware, cryptominers, password stealers, point-of-sale malware and banking trojans, means its a highly versatile loader that has remained consistently popular among threat actors.

In addition to its versatility, it also exhibits advanced evasion strategies that make it difficult for traditional security solutions to detect and remove, and it is easily distributed via methods like spam emails or malicious file downloads.

Between July and August 2023, Darktrace observed an increasing trend in SmokeLoader compromises across its customer base. The anomaly-based threat detection capabilities of Darktrace, coupled with the autonomous response technology, identified and contained the SmokeLoader infections in their initial stages, preventing attackers from causing further disruption by deploying other malicious software or ransomware.

SmokeLoader Malware Attack Details

PROPagate Injection Technique

SmokeLoader utilizes the PROPagate code injection technique, a less common method that inserts malicious code into existing processes in order to appear legitimate and bypass traditional signature-based security measures [2] [3]. In the case of SmokeLoader, this technique exploits the Windows SetWindowsSubclass function, which is typically used to add or change the behavior of Windows Operation System. By manipulating this function, SmokeLoader can inject its code into other running processes, such as the Internet Explorer. This not only helps to disguise the malware's activity but also allows attackers to leverage the permissions and capabilities of the infected process.

Obfuscation Methods

SmokeLoader is known to employ several obfuscation techniques to evade the detection and analysis of security teams. The techniques include scrambling portable executable files, encrypting its malicious code, obfuscating API functions and packing, and are intended to make the malware’s code appear harmless or unremarkable to antivirus software. This allows attackers to slip past defenses and execute their malicious activities while remaining undetected.

Infection Vector and Communication

SmokeLoader typically spreads via phishing emails that employ social engineering tactics to convince users to unknowingly download malicious payloads and execute the malware. Once installed on target networks, SmokeLoader acts as a backdoor, allowing attackers to control infected systems and download further malicious payloads from command-and-control (C2) servers. SmokeLoader uses fast flux, a DNS technique utilized by botets whereby IP addresses associated with C2 domains are rapidly changed, making it difficult to trace the source of the attack. This technique also boosts the resilience of attack, as taking down one or two malicious IP addresses will not significantly impact the botnet's operation.

Continuous Evolution

As with many MaaS strains, SmokeLoader is continuously evolving, with its developers regularly adding new features and techniques to increase its effectiveness and evasiveness. This includes new obfuscation methods, injection techniques, and communication protocols. This constant evolution makes SmokeLoader a significant threat and underscores the importance of advanced threat detection and response capabilities solution.

Darktrace’s Coverage of SmokeLoader Attack

Between July and August 2023, Darktrace detected one particular SmokeLoader infection at multiple stages of its kill chain on a customer network. This detection was made possible by Darktrace DETECT’s anomaly-based approach and Self-Learning AI that allows it to identify subtle deviations in device behavior.

One of the key components of this process is the classification of endpoint rarity and determining whether an endpoint is new or unusual for any given network. This classification is applied to various aspects of observed endpoints, such as domains, IP addresses, or hostnames within the network. It thereby plays a vital role in identifying SmokeLoader activity, such as the initial infection vector or C2 communication, which typically involve a device contacting a malicious endpoint associated with SmokeLoader.

The First Signs of Infection SmokeLoader Infection

Beginning in July 2023, Darktrace observed a surge in suspicious activities that were assessed with moderate to high confidence to be associated with SmokeLoader malware.

For example on July 30, a device was observed making a successful HTTPS request to humman[.]art, a domain that had never been seen on the network, and therefore classified as 100% rare by DETECT. During this connection, the device in question received a total of 6.0 KiB of data from the unusual endpoint. Open-source intelligence (OSINT) sources reported with high confidence that this domain was associated with the SmokeLoader C2 botnet.

The device was then detected making an HTTP request to another 100% rare external IP, namely 85.208.139[.]35, using a new user agent. This request contained the URI ‘/DefenUpdate.exe’, suggesting a possible download of an executable (.exe) file. This was corroborated by the total amount of data received in this connection, 4.3 MB. Both the file name and its size suggest that the offending device may have downloaded additional malicious tooling from the SmokeLoader C2 endpoint, such as a trojan or information stealer, as reported on OSINT platforms [4].

Figure 1: Device event log showing the moment when a device made its first connection to a SmokeLoader associated domain, and the use of a new user agent. A few seconds later, the DETECT model “Anomalous Connection / New User Agent to IP Without Hostname” breached.

The observed new user agent, “Mozilla/5.0 (Windows NT 10.0; Win64; x64; Trident/7.0; rv:11.0) like Gecko” was identified as suspicious by Darktrace leading to the “Anomalous Connection / New User Agent to IP Without Hostname” DETECT model breach.

As this specific user agent was associated with the Internet Explorer browser running on Windows 10, it may not have appeared suspicious to traditional security tools. However, Darktrace’s anomaly-based detection allows it to identify and mitigate emerging threats, even those that utilize sophisticated evasion techniques.

This is particularly noteworthy in this case because, as discussed earlier, SmokeLoader is known to inject its malicious code into legitimate processes, like Internet Explorer.

Figure 2: Darktrace detecting the affected device leveraging a new user agent and establishing an anomalous HTTP connection with an external IP, which was 100% rare to the network.

C2 Communication

Darktrace continued to observe the device making repeated connections to the humman[.]art endpoint. Over the next few days. On August 7, the device was observed making unusual POST requests to the endpoint using port 80, breaching the ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’ DETECT model. These observed POST requests were observed over a period of around 10 days and consisted of a pattern of 8 requests, each with a ten-minute interval.

Figure 3: Model Breach Event Log highlighting the Darktrace DETECT model breach ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’.

Upon investigating the details of this activity identified by Darktrace DETECT, a particular pattern was observed in these requests: they used the same user-agent, “Mozilla/5.0 (Windows NT 10.0; Win64; x64; Trident/7.0; rv:11.0) like Gecko”, which was previously detected in the initial breach.

Additionally, they the requests had a constantly changing eferrer header, possibly using randomly generated domain names for each request. Further examination of the packet capture (PCAP) from these requests revealed that the payload in these POST requests contained an RC4 encrypted string, strongly indicating SmokeLoader C2 activity.

Figure4: Advanced Search results display an unusual pattern in the requests made by the device to the hostname humman[.]art. This pattern shows a constant change in the referrer header for each request, indicating anomalous behavior.

Figure 5: The PCAP shows the payload seen in these POST requests contained an RC4 encrypted string strongly indicating SmokeLoader C2 activity.

Unfortunately in this case, Darktrace RESPOND was not active on the network meaning that the attack was able to progress through its kill chain. Despite this, the timely alerts and detailed incident insights provided by Darktrace DETECT allowed the customer’s security team to begin their remediation process, implementing blocks on their firewall, thus preventing the SmokeLoader malware from continuing its communication with C2 infrastructure.

Darktrace RESPOND Halting Potential Threats from the Initial Stages of Detection

With Darktrace RESPOND, organizations can move beyond threat detection to proactive defense against emerging threats. RESPOND is designed to halt threats as soon as they are identified by DETECT, preventing them from escalating into full-blown compromises. This is achieved through advanced machine learning and Self-Learning AI that is able to understand the normal ‘pattern of life’ of customer networks, allowing for swift and accurate threat detection and response.

One pertinent example was seen on July 6, when Darktrace detected a separate SmokeLoader case on a customer network with RESPOND enabled in autonomous response mode. Darktrace DETECT initially identified a string of anomalous activity associated with the download of suspicious executable files, triggering the ‘Anomalous File / Multiple EXE from Rare External Locations’ model to breach.

The device was observed downloading an executable file (‘6523.exe’ and ‘/g.exe’) via HTTP over port 80. These downloads originated from endpoints that had never been seen within the customer’s environment, namely ‘hugersi[.]com’ and ‘45.66.230[.]164’, both of which had strongly been linked to SmokeLoader by OSINT sources, likely indicating the initial infection stage of the attack [5].

Figure 6: This figure illustrates Darktrace DETECT observing a device downloading multiple .exe files from rare endpoints and the associated model breach, ‘Anomalous File / Multiple EXE from Rare External Locations’.

Around the same time, Darktrace also observed the same device downloading an unusual file with a numeric file name. Threat actors often employ this tactic in order to avoid using file name patterns that could easily be recognized and blocked by traditional security measures; by frequently changing file names, malicious executables are more likely to remain undetected.

Figure 7: Graph showing the unusually high number of executable files downloaded by the device during the initial infection stage of the attack. The orange and red circles represent the number of model breaches that the device made during the observed activity related to SmokeLoader infection.

Figure 8: This figure illustrates the moment when Darktrace DETECT identified a suspicious download with a numeric file name.

With Darktrace RESPOND active and enabled in autonomous response mode, the SmokeLoader infection was thwarted in the first instance. RESPOND took swift autonomous action by blocking connections to the suspicious endpoints identified by DETECT, blocking all outgoing traffic, and enforcing a pre-established “pattern of life” on offending devices. By enforcing a patten of life on a device, Darktrace RESPOND ensures that it cannot deviate from its ‘normal’ activity to carry out potentially malicious activity, while allowing the device to continue expected business operations.

Figure 9: A total of 8 RESPOND actions were applied, including blocking connections to suspicious endpoints and domains associated with SmokeLoader.

In addition to the autonomous mitigative actions taken by RESPOND, this customer also received a Proactive Threat Notification (PTN) informing them of potentially malicious activity on their network. This prompted the Darktrace Security Operations Center (SOC) to investigate and document the incident, allowing the customer’s security team to shift their focus to remediating and removing the threat of SmokeLoader.

Conclusion

Ultimately, Darktrace showcased its ability to detect and contain versatile and evasive strains of loader malware, like SmokeLoader. Despite its adeptness at bypassing conventional security tools by frequently changing its C2 infrastructure, utilizing existing processes to infect malicious code, and obfuscating malicious file and domain names, Darktrace’s anomaly-based approach allowed it to recognize such activity as deviations from expected network behavior, regardless of their apparent legitimacy.

Considering SmokeLoader’s wide array of functions, including C2 communication that could be used to facilitate additional attacks like exfiltration, or even the deployment of information-stealers or ransomware, Darktrace proved to be crucial in safeguarding customer networks. By identifying and mitigating SmokeLoader at the earliest possible stage, Darktrace effectively prevented the compromises from escalating into more damaging and disruptive compromises.

With the threat of loader malware expected to continue growing alongside the boom of the MaaS industry, it is paramount for organizations to adopt proactive security solutions, like Darktrace DETECT+RESPOND, that are able to make intelligent decisions to identify and neutralize sophisticated attacks.

Credit to Patrick Anjos, Senior Cyber Analyst, Justin Torres, Cyber Analyst

Appendices

Darktrace DETECT Model Detections

- Anomalous Connection / New User Agent to IP Without Hostname

- Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

- Anomalous File / Multiple EXE from Rare External Locations

- Anomalous File / Numeric File Download

List of IOCs (IOC / Type / Description + Confidence)

- 85.208.139[.]35 / IP / SmokeLoader C2 Endpoint

- 185.174.137[.]109 / IP / SmokeLoader C2 Endpoint

- 45.66.230[.]164 / IP / SmokeLoader C2 Endpoint

- 91.215.85[.]147 / IP / SmokeLoader C2 Endpoint

- tolilolihul[.]net / Hostname / SmokeLoader C2 Endpoint

- bulimu55t[.]net / Hostname / SmokeLoader C2 Endpoint

- potunulit[.]org / Hostname / SmokeLoader C2 Endpoint

- hugersi[.]com / Hostname / SmokeLoader C2 Endpoint

- human[.]art / Hostname / SmokeLoader C2 Endpoint

- 371b0d5c867c2f33ae270faa14946c77f4b0953 / SHA1 / SmokeLoader Executable

References:

[1] https://bazaar.abuse.ch/sample/d7c395ab2b6ef69210221337ea292e204b0f73fef8840b6e64ab88595eda45b3/#intel

[2] https://malpedia.caad.fkie.fraunhofer.de/details/win.smokeloader

[3] https://www.darkreading.com/cyber-risk/breaking-down-the-propagate-code-injection-attack

[4] https://n1ght-w0lf.github.io/malware%20analysis/smokeloader/

[5] https://therecord.media/surge-in-smokeloader-malware-attacks-targeting-ukrainian-financial-gov-orgs

MITRE ATT&CK Mapping

Model: Anomalous Connection / New User Agent to IP Without Hostname

ID: T1071.001

Sub technique: T1071

Tactic: COMMAND AND CONTROL

Technique Name: Web Protocols

‍

Model: Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

ID: T1185

Sub technique: -

Tactic: COLLECTION

Technique Name: Man in the Browser

‍

ID: T1071.001

Sub technique: T1071

Tactic: COMMAND AND CONTROL

Technique Name: Web Protocols

‍

Model: Anomalous File / Multiple EXE from Rare External Locations

ID: T1189

Sub technique: -

Tactic: INITIAL ACCESS

Technique Name: Drive-by Compromise

‍

ID: T1588.001

Sub technique: - T1588

Tactic: RESOURCE DEVELOPMENT

Technique Name: Malware

‍

Model: Anomalous File / Numeric File Download

ID: T1189

Sub technique: -

Tactic: INITIAL ACCESS

Technique Name: Drive-by Compromise

‍

ID: T1588.001

Sub technique: - T1588

Tactic: RESOURCE DEVELOPMENT

Technique Name: Malware

Written by

Patrick Anjos

Senior Cyber Analyst

Inside the SOC

Written by

Patrick Anjos

Senior Cyber Analyst

•

March 4, 2026

Nathaniel Bill

Malware Research Engineer

•

February 13, 2026

Watch the NIS2 Webinar

Blog

March 5, 2026

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

Investigating cloud attacks with Darktrace/ Forensic Acquisition & Investigation

Darktrace / Forensic Acquisition & Investigation™ is the industry’s first truly automated forensic solution purpose-built for the cloud. This blog will demonstrate how an investigation can be carried out against a compromised cloud server in minutes, rather than hours or days.

The compromised server investigated in this case originates from Darktrace’s Cloudypots system, a global honeypot network designed to observe adversary activity in real time across a wide range of cloud services. Whenever an attacker successfully compromises one of these honeypots, a forensic copy of the virtual server's disk is preserved for later analysis. Using Forensic Acquisition & Investigation, analysts can then investigate further and obtain detailed insights into the compromise including complete attacker timelines and root cause analysis.

Forensic Acquisition & Investigation supports importing artifacts from a variety of sources, including EC2 instances, ECS, S3 buckets, and more. The Cloudypots system produces a raw disk image whenever an attack is detected and stores it in an S3 bucket. This allows the image to be directly imported into Forensic Acquisition & Investigation using the S3 bucket import option.

As Forensic Acquisition & Investigation runs cloud-natively, no additional configuration is required to add a specific S3 bucket. Analysts can browse and acquire forensic assets from any bucket that the configured IAM role is permitted to access. Operators can also add additional IAM credentials, including those from other cloud providers, to extend access across multiple cloud accounts and environments.

*Figure 1: Forensic Acquisition & Investigation import screen.*

Forensic Acquisition & Investigation then retrieves a copy of the file and automatically begins running the analysis pipeline on the artifact. This pipeline performs a full forensic analysis of the disk and builds a timeline of the activity that took place on the compromised asset. By leveraging Forensic Acquisition & Investigation’s cloud-native analysis system, this process condenses hour of manual work into just minutes.

*Figure 2: Successful import of a forensic artifact and initiation of the analysis pipeline.*

Once processing is complete, the preserved artifact is visible in the Evidence tab, along with a summary of key information obtained during analysis, such as the compromised asset’s hostname, operating system, cloud provider, and key event count.

*Figure 3: The Evidence overview showing the acquired disk image.*

Clicking on the “Key events” field in the listing opens the timeline view, automatically filtered to show system- generated alarms.

The timeline provides a chronological record of every event that occurred on the system, derived from multiple sources, including:

Parsed log files such as the systemd journal, audit logs, application specific logs, and others.
Parsed history files such as .bash_history, allowing executed commands to be shown on the timeline.
File-specific events, such as files being created, accessed, modified, or executables being run, etc.

This approach allows timestamped information and events from multiple sources to be aggregated and parsed into a single, concise view, greatly simplifying the data review process.

Alarms are created for specific timeline events that match either a built-in system rule, curated by Darktrace’s Threat Research team or an operator-defined created at the project level. These alarms help quickly filter out noise and highlight on events of interest, such as the creation of a file containing known malware, access to sensitive files like Amazon Web Service (AWS) credentials, suspicious arguments or commands, and more.

*Figure 4: The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.*

In this case, several alarms were generated for suspicious Base64 arguments being passed to Selenium. Examining the event data, it appears the attacker spawned a Selenium Grid session with the following payload:

"request.payload": "[Capabilities {browserName: chrome, goog:chromeOptions: {args: [-cimport base64;exec(base64...], binary: /usr/bin/python3, extensions: []}, pageLoadStrategy: normal}]"

This is a common attack vector for Selenium Grid. The chromeOptions object is intended to specify arguments for how Google Chrome should be launched; however, in this case the attacker has abused the binary field to execute the Python3 binary instead of Chrome. Combined with the option to specify command-line arguments, the attacker can use Python3’s -c option to execute arbitrary Python code, in this instance, decoding and executing a Base64 payload.

Selenium’s logs truncate the Arguments field automatically, so an alternate method is required to retrieve the full payload. To do this, the search bar can be used to find all events that occurred around the same time as this flagged event.

*Figure 5: Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].*

Scrolling through the search results, an entry from Java’s systemd journal can be identified. This log contains the full, unaltered payload. GCHQ’s CyberChef can then be used to decode the Base64 data into the attacker’s script, which will ultimately be executed.[NJ9]

*Figure 6: Decoding the attacker’s payload in CyberChef.*

In this instance, the malware was identified as a variant of a campaign that has been previously documented in depth by Darktrace.

Investigating Perfectl Malware

This campaign deploys a malware sample known as ‘perfctl to the compromised host. The script executed by the attacker downloads a Go binary named “promocioni.php” from 200[.]4.115.1. Its functionality is consistent with previously documented perfctl samples, with only minor changes such as updated filenames and a new command-and-control (C2) domain.

Perctl is a stealthy malware that has several systems designed to evade detection. The main binary is packed with UPX, with the header intentionally tampered with to prevent unpacking using regular tools. The binary also avoids executing any malicious code if it detects debugging or tracing activity, or if artifacts left by earlier stages are missing.

To further aid its evasive capabilities, perfctl features a usermode rootkit using an LD preload. This causes dynamically linked executables to load perfctl’s rootkit payload before other system modules, allowing it to override functions, such as intercepting calls to list files and hiding output from the returned list. Perctl uses this to hide its own files, as well as other files like the ld.so.preload file, preventing users from identifying that a rootkit is present in the first place.

This also makes it difficult to dynamically analyze, as even analysts aware of the rootkit will struggle to get around it due to its aggressiveness in hiding its components. A useful trick is to use the busybox-static utilities, which are statically linked and therefore immune to LD preloading.

Perctl will attempt to use sudo to escalate its permissions to root if the user it was executed as has the required privileges. Failing this, it will attempt to exploit the vulnerability CVE-2021-4034.

Ultimately, perfctl will attempt to establish a C2 link via Tor and spawn an XMRig miner to mine the Monero cryptocurrency. The traffic to the mining pool is encapsulated within Tor to limit network detection of the mining traffic.

Darktrace’s Cloudypots system has observed 1,959 infections of the perfectl campaign across its honeypot network in the past year, making it one of the most aggressive campaigns seen by Darktrace.

Key takeaways

This blog has shown how Darktrace / Forensic Acquisition & Investigation equips defenders in the face of a real-world attacker campaign. By using this solution, organizations can acquire forensic evidence and investigate intrusions across multiple cloud resources and providers, enabling defenders to see the full picture of an intrusion on day one. Forensic Acquisition & Investigation’s patented data-processing system takes advantage of the cloud’s scale to rapidly process large amounts of data, allowing triage to take minutes, not hours.

Darktrace / Forensic Acquisition & Investigation is available as Software-as-a-Service (SaaS) but can also be deployed on-premises as a virtual application or natively in the cloud, providing flexibility between convenience and data sovereignty to suit any use case.

Support for acquiring traditional compute instances like EC2, as well as more exotic and newly targeted platforms such as ECS and Lambda, ensures that attacks taking advantage of Living-off-the-Cloud (LOTC) strategies can be triaged quickly and easily as part of incident response. As attackers continue to develop new techniques, the ability to investigate how they use cloud services to persist and pivot throughout an environment is just as important to triage as a single compromised EC2 instance.

Credit to Nathaniel Bill (Malware Research Engineer)

About the author

Nathaniel Bill

Malware Research Engineer

Blog

Network

February 19, 2026

CVE-2026-1731: How Darktrace Sees the BeyondTrust Exploitation Wave Unfolding

Note: Darktrace's Threat Research team is publishing now to help defenders. We will continue updating this blog as our investigations unfold.

Background

On February 6, 2026, the Identity & Access Management solution BeyondTrust announced patches for a vulnerability, CVE-2026-1731, which enables unauthenticated remote code execution using specially crafted requests. This vulnerability affects BeyondTrust Remote Support (RS) and particular older versions of Privileged Remote Access (PRA) [1].

A Proof of Concept (PoC) exploit for this vulnerability was released publicly on February 10, and open-source intelligence (OSINT) reported exploitation attempts within 24 hours [2].

Previous intrusions against Beyond Trust technology have been cited as being affiliated with nation-state attacks, including a 2024 breach targeting the U.S. Treasury Department. This incident led to subsequent emergency directives from the Cybersecurity and Infrastructure Security Agency (CISA) and later showed attackers had chained previously unknown vulnerabilities to achieve their goals [3].

Additionally, there appears to be infrastructure overlap with React2Shell mass exploitation previously observed by Darktrace, with command-and-control (C2) domain avg.domaininfo[.]top seen in potential post-exploitation activity for BeyondTrust, as well as in a React2Shell exploitation case involving possible EtherRAT deployment.

Darktrace Detections

Darktrace’s Threat Research team has identified highly anomalous activity across several customers that may relate to exploitation of BeyondTrust since February 10, 2026. Observed activities include:

Outbound connections and DNS requests for endpoints associated with Out-of-Band Application Security Testing; these services are commonly abused by threat actors for exploit validation. Associated Darktrace models include:

Compromise / Possible Tunnelling to Bin Services

Suspicious executable file downloads. Associated Darktrace models include:‍

Anomalous File / EXE from Rare External Location

Outbound beaconing to rare domains. Associated Darktrace models include:

Compromise / Agent Beacon (Medium Period)
Compromise / Agent Beacon (Long Period)
Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
Compromise / Beacon to Young Endpoint
Anomalous Server Activity / Rare External from Server
Compromise / SSL Beaconing to Rare Destination

Unusual cryptocurrency mining activity. Associated Darktrace models include:

Compromise / Monero Mining
Compromise / High Priority Crypto Currency Mining

And model alerts for:

Compromise / Rare Domain Pointing to Internal IP

IT Defenders: As part of best practices, we highly recommend employing an automated containment solution in your environment. For Darktrace customers, please ensure that Autonomous Response is configured correctly. More guidance regarding this activity and suggested actions can be found in the Darktrace Customer Portal.

Appendices

Potential indicators of post-exploitation behavior:

· 217.76.57[.]78 – IP address - Likely C2 server

· hXXp://217.76.57[.]78:8009/index.js - URL - Likely payload

· b6a15e1f2f3e1f651a5ad4a18ce39d411d385ac7 - SHA1 - Likely payload

· 195.154.119[.]194 – IP address – Likely C2 server

· hXXp://195.154.119[.]194/index.js - URL – Likely payload

· avg.domaininfo[.]top – Hostname – Likely C2 server

· 104.234.174[.]5 – IP address - Possible C2 server

· 35da45aeca4701764eb49185b11ef23432f7162a – SHA1 – Possible payload

· hXXp://134.122.13[.]34:8979/c - URL – Possible payload

· 134.122.13[.]34 – IP address – Possible C2 server

· 28df16894a6732919c650cc5a3de94e434a81d80 - SHA1 - Possible payload

References:

1. https://nvd.nist.gov/vuln/detail/CVE-2026-1731

2. https://www.securityweek.com/beyondtrust-vulnerability-targeted-by-hackers-within-24-hours-of-poc-release/

3. https://www.rapid7.com/blog/post/etr-cve-2026-1731-critical-unauthenticated-remote-code-execution-rce-beyondtrust-remote-support-rs-privileged-remote-access-pra/

About the author

Emma Foulger

Global Threat Research Operations Lead

Your data. Our AI.

Elevate your network security with Darktrace AI

Get a demo

How Darktrace Defeated SmokeLoader Malware

What is Loader Malware?

How does SmokeLoader Malware work?

SmokeLoader Malware Attack Details

PROPagate Injection Technique

Obfuscation Methods

Infection Vector and Communication

Continuous Evolution

Darktrace’s Coverage of SmokeLoader Attack

The First Signs of Infection SmokeLoader Infection

C2 Communication

Darktrace RESPOND Halting Potential Threats from the Initial Stages of Detection

Conclusion

Appendices

Darktrace DETECT Model Detections

List of IOCs (IOC / Type / Description + Confidence)

References:

MITRE ATT&CK Mapping

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

How AI is redefining cybersecurity and the role of today’s CIO

Enjoying the blog?

More in this series

CVE-2026-1731: How Darktrace Sees the BeyondTrust Exploitation Wave Unfolding

AI/LLM-Generated Malware Used to Exploit React2Shell

AppleScript Abuse: Unpacking a macOS Phishing Campaign

Blog

March 5, 2026

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

Investigating cloud attacks with Darktrace/ Forensic Acquisition & Investigation

Investigating Perfectl Malware

Key takeaways

Blog

Network

February 19, 2026

CVE-2026-1731: How Darktrace Sees the BeyondTrust Exploitation Wave Unfolding

Background

Darktrace Detections

Appendices

Potential indicators of post-exploitation behavior:

References: