Internet of Things (IoT) Security: The Threat Before Us
The Internet of Things (Iot) offers many footholds for attackers to infiltrate organizations through smart devices. but self-learning AI is here to help.
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
Marcus Fowler
CEO of Darktrace Federal and SVP of Strategic Engagements and Threats
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Sep 2021
Attackers are increasingly gaining footholds into corporate environments to conduct ransomware or data theft operations via Internet-connected smart devices. Whether they be printers, lockers, aquariums, or conference rooms, these seemingly innocuous access points to corporate environments can provide attackers the critical initial access to conduct their attacks. These can also often be blind spots for many security teams.
When dropped into an organization’s digital environment for the first time and learning its surroundings, Darktrace often finds 15–20% more devices than anticipated. Most of these unexpected devices and areas of unsecured vulnerability result from an influx in IoT-enabled tech. This growing dependence on IoT devices will only continue to accelerate. There are currently more than 10 billion active IoT devices. This number is estimated to surpass 25.4 billion in 2030, though, by Darktrace’s predictions, it will in fact be much higher. We assess that almost all estimates around IoT usage by 2025 are too low.
As a result of the COVID-19 pandemic and hybrid work, the future workplace environment will only become more hands-free and interconnected. Broad adoption of 5G will not only mean more IoT devices, but also expanded capabilities as they become more efficient and highly connected.
People can walk in with an Internet-connected device on their wrist, or a security problem can enter a company through a newly updated Internet-connected vending machine. IT teams do not always know these devices are “smart” or vet them like they would with standard company technology.
IoT device manufacturers do not have a record of prioritizing the security of their devices, often sacrificing it for access and convenience, placing the burden on company security teams after the fact. Starting with one of these IoT devices that are typically not reinforced with security protocols makes it easier for a hacker to move laterally. Much like the threat from supply chains, it is easier for a hacker to go through an open window than a locked, guarded front door.
IoT compromise frequently appears as a lead threat across Darktrace’s global SOC operations. We have seen IoT devices intentionally brought into a corporate environment and used by an insider because of their small size, low signature, and capabilities, making them a powerful tool to evade traditional security defenses focused on external and known threats. Darktrace has even discovered crypto-mining malware on a door sensor, showcasing how creative attackers can get and all the different ways unsecured IoT can be misused.
IoT security is critical to prevent hackers from moving laterally throughout a company network. If hackers can breach one device within an organization’s digital environment, they can move to more critical devices with more sensitive data.
The good news is that security teams aren’t without resources to defend their environments. The first thing corporations need to have is a policy around IoT usage and adoption. The next and often most challenging step is increasing visibility and understanding of these shadow devices the instant they connect to the network in the first place. To meet this mission, some security teams use AI to identify the device and map ‘normal’ behaviors, then enforce a device’s behavior to disrupt any attacker’s efforts to use that device as an attack platform. Leveraging AI in this way also reduces the workload on already taxed security teams.
From a broader policy perspective, in tandem with internal security efforts, more pressure needs to be put on IoT manufacturers to make security a priority and part of the entire development and upgrade process. Disrupting attacks and hardening environments from attacker access points and attack vectors is everyone’s responsibility.
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
Marcus Fowler
CEO of Darktrace Federal and SVP of Strategic Engagements and Threats
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.
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:
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)
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:
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:
![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”].](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/69a8a18526ca3e653316a596_Screenshot%202026-03-04%20at%201.17.50%E2%80%AFPM.png)
