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May 3, 2021

Understanding Modern-Day Cyber Attacks

Discover how Darktrace detects and mitigates threats in IoT ecosystems and globalized supply chains that are constantly evolving.
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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.
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03
May 2021

It’s ten to five on a Friday afternoon. A technician has come in to perform a routine check on an electronic door. She enters the office with no issues – she works for a trusted third-party vendor, employees see her every week. She opens her laptop and connects to the Door Access Control Unit, a small Internet of Things (IoT) device used to operate the smart lock. Minutes later, trojans have been downloaded onto the company network, a crypto-mining operation has begun, and there is evidence of confidential data being exfiltrated. Where did things go wrong?

Threats in a business: A new dawn surfaces

As organizations keep pace with the demands of digital transformation, the attack surface has become broader than ever before. There are numerous points of entry for a cyber-criminal – from vulnerabilities in IoT ecosystems, to blind spots in supply chains, to insiders misusing their access to the business. Darktrace sees these threats every day. Sometimes, like in the real-world example above, which will be examined in this blog, they can occur in the very same attack.

Insider threats can use their familiarity and level of access to a system as a critical advantage when evading detection and launching an attack. But insiders don’t necessarily have to be malicious. Every employee or contractor is a potential threat: clicking on a phishing link or accidentally releasing data often leads to wide-scale breaches.

At the same time, connectivity in the workspace – with each IoT device communicating with the corporate network and the Internet on its own IP address – is an urgent security issue. Access control systems, for example, add a layer of physical security by tracking who enters the office and when. However, these same control systems imperil digital security by introducing a cluster of sensors, locks, alarm systems, and keypads, which hold sensitive user information and connect to company infrastructure.

Furthermore, a significant proportion of IoT devices are built without security in mind. Vendors prioritize time-to-market and often don’t have the resources to invest in baked-in security measures. Consider the number of start-ups which manufacture IoT – over 60% of home automation companies have fewer than ten employees.

Insider threat detected by Cyber AI

In January 2021, a medium-sized North American company suffered a supply chain attack when a third-party vendor connected to the control unit for a smart door.

Figure 1: The attack lasted 3.5 hours in total, commencing 16:50 local time.

The technician from the vendor’s company had come in to perform scheduled maintenance. They had been authorized to connect directly to the Door Access Control Unit, yet were unaware that the laptop they were using, brought in from outside of the organization, had been infected with malware.

As soon as the laptop connected with the control unit, the malware detected an open port, identified the vulnerability, and began moving laterally. Within minutes, the IoT device was seen making highly unusual connections to rare external IP addresses. The connections were made using HTTP and contained suspicious user agents and URIs.

Darktrace then detected that the control unit was attempting to download trojans and other payloads, including upsupx2.exe and 36BB9658.moe. Other connections were used to send base64 encoded strings containing the device name and the organization’s external IP address.

Cryptocurrency mining activity with a Monero (XMR) CPU miner was detected shortly afterwards. The device also utilized an SMB exploit to make external connections on port 445 while searching for vulnerable internal devices using the outdated SMBv1 protocol.

One hour later, the device connected to an endpoint related to the third-party remote access tool TeamViewer. After a few minutes, the device was seen uploading over 15 MB to a 100% rare external IP.

Figure 2: Timeline of the connections made by an example device on the days around an incident (blue). The connections associated with the compromise are a significant deviation from the device’s normal pattern of life, and result in multiple unusual activity events and repeated model breaches (orange).

Security threats in the supply chain

Cyber AI flagged the insider threat to the customer as soon as the control unit had been compromised. The attack had managed to bypass the rest of the organization’s security stack, for the simple reason that it was introduced directly from a trusted external laptop, and the IoT device itself was managed by the third-party vendor, so the customer had little visibility over it.

Traditional security tools are ineffective against supply chain attacks such as this. From the SolarWinds hack to Vendor Email Compromise, 2021 has put the nail in the coffin for signature-based security – proving that we cannot rely on yesterday’s attacks to predict tomorrow’s threats.

International supply chains and the sheer number of different partners and suppliers which modern organizations work with thus pose a serious security dilemma: how can we allow external vendors onto our network and keep an airtight system?

The first answer is zero-trust access. This involves treating every device as malicious, inside and outside the corporate network, and demanding verification at all stages. The second answer is visibility and response. Security products must shed a clear light into cloud and IoT infrastructure, and react autonomously as soon as subtle anomalies emerge across the enterprise.

IoT investigated

Darktrace’s Cyber AI Analyst reported on every stage of the attack, including the download of the first malicious executable file.

Figure 3: Example of Cyber AI Analyst detecting anomalous behavior on a device, including C2 connectivity and suspicious file downloads.

Cyber AI Analyst investigated the C2 connectivity, providing a high-level summary of the activity. The IoT device had accessed suspicious MOE files with randomly generated alphanumeric names.

Figure 4: A Cyber AI Analyst summary of C2 connectivity for a device.

Not only did the AI detect every stage of the activity, but the customer was also alerted via a Proactive Threat Notification following a high scoring model breach at 16:59, just minutes after the attack had commenced.

Stranger danger

Third parties coming in to tweak device settings and adjust the network can have unintended consequences. The hyper-connected world which we’re living in, with the advent of 5G and Industry 4.0, has become a digital playground for cyber-criminals.

In the real-world case study above, the IoT device was unsecured and misconfigured. With rushed creations of IoT ecosystems, intertwining supply chains, and a breadth of individuals and devices connecting to corporate infrastructure, modern-day organizations cannot expect simple security tools which rely on pre-defined rules to stop insider threats and other advanced cyber-attacks.

The organization did not have visibility over the management of the Door Access Control Unit. Despite this, and despite no prior knowledge of the attack type or the vulnerabilities present in the IoT device, Darktrace detected the behavioral anomalies immediately. Without Cyber AI, the infection could have remained on the customer’s environment for weeks or months, escalating privileges, silently crypto-mining, and exfiltrating sensitive company data.

Thanks to Darktrace analyst Grace Carballo for her insights on the above threat find.

Learn more about insider threats

Darktrace model detections:

  • Anomalous File/Anomalous Octet Stream
  • Anomalous Connection/New User Agent to IP Without Hostname
  • Unusual Activity/Unusual External Connectivity
  • Device/Increased External Connectivity
  • Anomalous Server Activity/Outgoing from Server
  • Device/New User Agent and New IP
  • Compliance/Cryptocurrency Mining Activity
  • Compliance/External Windows Connectivity
  • Anomalous File/Multiple EXE from Rare External Locations
  • Anomalous File/EXE from Rare External Location
  • Device/Large Number of Model Breaches
  • Anomalous File/Internet Facing System File Download
  • Device/Initial Breach Chain Compromise
  • Device/SMB Session Bruteforce
  • Device/Network Scan- Low Anomaly Score
  • Device/Large Number of Connections to New Endpoint
  • Anomalous Server Activity/Outgoing from Server
  • Compromise/Beacon to Young Endpoint
  • Anomalous Server Activity/Rare External from Server
  • Device/Multiple C2 Model Breaches
  • Compliance/Remote Management Tool on Server
  • Anomalous Connection/Data Sent to New External Device

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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.
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March 5, 2026

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

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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.

Successful import of a forensic artifact and initiation of the analysis pipeline.
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.

The Evidence overview showing the acquired disk image.
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 rule  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.

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

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”].
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.

Decoding the attacker’s payload in CyberChef.
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 Perfctl 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.

Perfctl 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. Perfctl 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.

Perfctl 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 perfctl 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)

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Nathaniel Bill
Malware Research Engineer

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February 19, 2026

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

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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/

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About the author
Emma Foulger
Global Threat Research Operations Lead
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