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April 12, 2023

P2Pinfect - New Variant Targets MIPS Devices

A new P2Pinfect variant compiled for the Microprocessor without Interlocked Pipelined Stages (MIPS) architecture has been discovered. This demonstrates increased targeting of routers, Internet of Things (IoT) and other embedded devices by those behind P2Pinfect.
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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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12
Apr 2023

Introduction: P2PInfect

Since July 2023, researchers at Cado Security Labs (now part of Darktrace) have been monitoring and reporting on the rapid growth of a cross-platform botnet, named “P2Pinfect”. As the name suggests, the malware - written in Rust - acts as a botnet agent, connecting infected hosts in a peer-to-peer topology. In early samples, the malware exploited Redis for initial access - a relatively common technique in cloud environments. 

There are a number of methods for exploiting Redis servers, several of which appear to be utilized by P2Pinfect. These include exploitation of CVE-2022-0543[1] - a sandbox escape vulnerability in the LUA scripting language (reported by Unit42 [2]), and, as reported previously by Cado Security Labs, an unauthorized replication attack resulting in the loading of a malicious Redis module.  

Researchers have since encountered a new variant of the malware, specifically targeting embedded devices based on 32-bit MIPS processors, and attempting to brute force SSH access to these devices. It’s highly likely that by targeting MIPS, the P2Pinfect developers intend to infect routers and IoT devices with the malware. Use of MIPS processors is common for embedded devices and the architecture has been previously targeted by botnet malware, including high-profile families like Mirai [3], and its variants/derivatives.

Not only is this an interesting development in that it demonstrates a widening of scope for the developers behind P2Pinfect (more supported processor architectures equals more nodes in the botnet itself), but the MIPS32 sample includes some notable defense evasion techniques. 

This, combined with the malware’s utilization of Rust (aiding cross-platform development) and rapid growth of the botnet itself, reinforces previous suggestions that this campaign is being conducted by a sophisticated threat actor.

Initial access

Cado researchers encountered the MIPS variant of P2Pinfect after triaging files uploaded via SFTP and SCP to a SSH honeypot. Although earlier variants had been observed scanning for SSH servers, and attempting to propagate the malware via SSH as part of its worming procedure, researchers had yet to observe successful implantation of a P2Pinfect sample using this method - until now.

In keeping with similar botnet families, P2Pinfect includes a number of common username/password pairs embedded within the MIPS binary itself. The malware will then iterate through these pairs, initiating a SSH connection with servers identified during the scanning phase to conduct a brute force attack. 

It was assumed that SSH would be the primary method of propagation for the MIPS variant, due to routers and other embedded devices being more likely to utilize SSH. However, additional research shows that it is in fact possible to run the Redis server on MIPS. This is achievable via an OpenWRT package named redis-server. [4]

It is unclear what use-case running Redis on an embedded MIPS device solves, or whether it is commonly encountered in the wild. If such a device is compromised by P2Pinfect and has the Redis-server package installed, it is perfectly feasible for that node to then be used to compromise new peers via one of the reported P2Pinfect attack patterns, involving exploitation of Redis or SSH brute-forcing.

Static analysis

The MIPS variant of P2Pinfect is a 32-bit, statically-linked, ELF binary with stripped debug information. Basic static analysis revealed the presence of an additional ELF executable, along with a 32-bit Windows DLL in the PE32 format - more on this later. 

This piqued the interest of Cado analysts, as it is unusual to encounter a compiled ELF with an embedded DLL. Consequently, it was a defining feature of the original P2Pinfect samples.

Embedded Windows PE32 executable
Figure 1: Embedded Windows PE32 executable

Further analysis of the host executable revealed a structure named “BotnetConf” with members consistent in naming with the original P2Pinfect samples. 

Example of a partially populated version of the BotnetConf struct 
Figure 2: Example of a partially populated version of the BotnetConf struct 

As the name suggests, this structure defines the configuration of the malware itself, whilst also storing the IP addresses of nodes identified during the SSH and Redis scans. This, in combination with the embedded ELF and DLL, along with the use of the Rust programming language allowed for positive attribution of this sample to the P2Pinfect family.

Updated evasion - consulting tracerpid

One of the more interesting aspects of the MIPS sample was the inclusion of a new evasion technique. Shortly after execution, the sample calls fork() to spawn a child process. 

The child process then proceeds to access /proc using openat(), determines its own Process Identifier (PID) using the Linux getpid() syscall, and then uses this PID to consult the relevant /proc subdirectory and read the status file within that. Note that this is likely achieved in the source code by resolving the symbolic link at /proc/self/status.

Example contents of /proc/pid/status when process not being traced
Figure 3: Example contents of /proc/pid/status when process not being traced

/proc/<pid>/status contains human-readable metadata and other information about the process itself, including memory usage and the name of the command currently being run. Importantly, the status file also contains a field TracerPID:. This field is assigned a value of 0 if the current process is not being traced by dynamic analysis tools, such as strace and ltrace.

Example MIPS disassembly showing reading of /proc/pid/status file
Figure 4: Example MIPS disassembly showing reading of /proc/pid/status file

If this value is non-zero, the MIPS variant of P2Pinfect determines that it is being analyzed and will immediately terminate both the child process and its parent. 

read(5, "Name:\tmips_embedded_p\nUmask:\t002", 32) = 32 
read(5, "2\nState:\tR (running)\nTgid:\t975\nN", 32) = 32 
read(5, "gid:\t0\nPid:\t975\nPPid:\t1\nTracerPid:\t971\nUid:\t0\t0\t0\t0\nGid:\t0\t0\t0\t0", 64) = 64 
read(5, "\nFDSize:\t32\nGroups:\t0 \nNStgid:\t975\nNSpid:\t975\nNSpgid:\t975\nNSsid:\t975\nVmPeak:\t    3200 kB\nVmSize:\t    3192 kB\nVmLck:\t       0 kB\n", 128) = 128 
read(5, "VmPin:\t       0 kB\nVmHWM:\t    1564 kB\nVmRSS:\t    1560 kB\nRssAnon:\t      60 kB\nRssFile:\t    1500 kB\nRssShmem:\t       0 kB\nVmData:\t     108 kB\nVmStk:\t     132 kB\nVmExe:\t    2932 kB\nVmLib:\t       8 kB\nVmPTE:\t      16 kB\nVmSwap:\t       0 kB\nCoreDumping:\t0\nThre", 256) = 256 
mmap2(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77ff1000 
read(5, "ads:\t1\nSigQ:\t0/1749\nSigPnd:\t00000000000000000000000000000000\nShdPnd:\t00000000000000000000000000000000\nSigBlk:\t00000000000000000000000000000000\nSigIgn:\t00000000000000000000000000001000\nSigCgt:\t00000000000000000000000000000600\nCapInh:\t0000000000000000\nCapPrm:\t0000003fffffffff\nCapEff:\t0000003fffffffff\nCapBnd:\t0000003fffffffff\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t0\nSpeculation_Store_Bypass:\tunknown\nCpus_allowed:\t1\nCpus_allowed_list:\t0\nMems_allowed:\t1\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t92\nn", 512) = 512 
mmap2(NULL, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77fef000 
munmap(0x77ff1000, 4096)                = 0 
read(5, "onvoluntary_ctxt_switches:\t0\n", 1024) = 29 
read(5, "", 995)                        = 0 
close(5)                                = 0 
munmap(0x77fef000, 8192)                = 0 
sigaltstack({ss_sp=NULL, ss_flags=SS_DISABLE, ss_size=8192}, NULL) = 0 
munmap(0x77ff4000, 12288)               = 0 
exit_group(-101)                        = ? 
+++ exited with 155 +++ 

Strace output demonstrating TracerPid evasion technique

Updated evasion - disabling core dumps

Interestingly, the sample will also attempt to disable Linux core dumps. This is likely used as an anti-forensics procedure as the memory regions written to disk as part of the core dump can often contain internal information about the malware itself. In the case of P2Pinfect, this would likely include information such as IP addresses of connected peers and the populated BotnetConf structure mentioned previously. 

It is also possible that the sample prevents core dumps from being created to protect the availability of the MIPS device itself. Low-powered embedded devices are unlikely to have much local storage available and core dumps could quickly fill what little storage they do have, affecting performance of the device itself.

A screen shot of a computer codeAI-generated content may be incorrect.
Image 5

This procedure can be observed during dynamic analysis, with the binary utilising the prctl() syscall and passing the parameters PR_SET_DUMPABLE, SUID_DUMP_DISABLE.

munmap(0x77ff1000, 4096)                = 0 
prctl(PR_SET_DUMPABLE, SUID_DUMP_DISABLE) = 0 
prlimit64(0, RLIMIT_CORE, {rlim_cur=0, rlim_max=0}, NULL) = 0 

Example strace output demonstrating disabling of core dumps

Embedded DLL

As mentioned in the Static Analysis section, the MIPS variant of P2Pinfect includes an embedded 64-bit Windows DLL. This DLL acts as a malicious loadable module for Redis, implementing the system.exec functionality to allow the running of shell commands on a compromised host.

Disassembly of the Redis module entrypoint
Figure 6: Disassembly of the Redis module entrypoint, mapping the system.exec command to a handler

This is consistent with the previous examples of P2Pinfect, and demonstrates that the intention is to utilize MIPS devices for the Redis-specific initial access attack patterns mentioned throughout this blog. 

Interestingly, this embedded DLL also includes a Virtual Machine (VM) evasion function, demonstrating the lengths that the P2Pinfect developers have taken to hinder the analysis process. In the DLLs main function, a call can be observed to a function helpfully labelled anti_vm by IDAs Lumina feature.

Decompiler output showing call to anti_vm function
Figure 7: Decompiler output showing call to anti_vm function

Viewing the function itself, it can be seen that researchers Christopher Gardner and Moritz Raabe have identified it as a known VM evasion method in other malware samples.

IDA’s graph view for the anti_vm function showing Lumina annotations
Figure 8: IDA’s graph view for the anti_vm function showing Lumina annotations

Conclusion

P2Pinfect’s continued evolution and broadened targeting appear to be the utilization of a variety of evasion techniques demonstrate an above-average level of sophistication when it comes to malware development. This is a botnet that will continue to grow until it’s properly utilized by its operators. 

While much of the functionality of the MIPS variant is consistent with the previous variants of this malware, the developer’s efforts in making both the host and embedded executables as evasive as possible show a continued commitment to complicating the analysis procedure. The use of anti-forensics measures such as the disabling of core dumps on Linux systems also supports this.

Indicators of compromise (IoCs)

Files SHA256

MIPS ELF 8b704d6334e59475a578d627ae4bcb9c1d6987635089790350c92eafc28f5a6c

Embedded DLL Redis Module  d75d2c560126080f138b9c78ac1038ff2e7147d156d1728541501bc801b6662f

References:

[1] https://nvd.nist.gov/vuln/detail/CVE-2022-0543

[2] https://unit42.paloaltonetworks.com/peer-to-peer-worm-p2pinfect/

[3] https://unit42.paloaltonetworks.com/mirai-variant-iz1h9/

[4] https://openwrt.org/packages/pkgdata/redis-server

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
The Darktrace Community

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October 15, 2025

How a Major Civil Engineering Company Reduced MTTR across Network, Email and the Cloud with Darktrace

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Asking more of the information security team

“What more can we be doing to secure the company?” is a great question for any cyber professional to hear from their Board of Directors. After successfully defeating a series of attacks and seeing the potential for AI tools to supercharge incoming threats, a UK-based civil engineering company’s security team had the answer: Darktrace.

“When things are coming at you at machine speed, you need machine speed to fight it off – it’s as simple as that,” said their Information Security Manager. “There were incidents where it took us a few hours to get to the bottom of what was going on. Darktrace changed that.”

Prevention was also the best cure. A peer organization in the same sector was still in business continuity measures 18 months after an attack, and the security team did not want to risk that level of business disruption.

Legacy tools were not meeting the team’s desired speed or accuracy

The company’s native SaaS email platform took between two and 14 days to alert on suspicious emails, with another email security tool flagging malicious emails after up to 24 days. After receiving an alert, responses often took a couple of days to coordinate. The team was losing precious time.

Beyond long detection and response times, the old email security platform was no longer performing: 19% of incoming spam was missed. Of even more concern: 6% of phishing emails reached users’ inboxes, and malware and ransomware email was also still getting through, with 0.3% of such email-borne payloads reaching user inboxes.

Choosing Darktrace

“When evaluating tools in 2023, only Darktrace had what I was looking for: an existing, mature, AI-based cybersecurity solution. ChatGPT had just come out and a lot of companies were saying ‘AI this’ and ‘AI that’. Then you’d take a look, and it was all rules- and cases-based, not AI at all,” their Information Security Manager.

The team knew that, with AI-enabled attacks on the horizon, a cybersecurity company that had already built, fielded, and matured an AI-powered cyber defense would give the security team the ability to fend off machine-speed attacks at the same pace as the attackers.

Darktrace accomplishes this with multi-layered AI that learns each organization’s normal business operations. With this detailed level of understanding, Darktrace’s Self-Learning AI can recognize unusual activity that may indicate a cyber-attack, and works to neutralize the threat with precise response actions. And it does this all at machine speed and with minimal disruption.

On the morning the team was due to present its findings, the session was cancelled – for a good reason. The Board didn’t feel further discussion was necessary because the case for Darktrace was so conclusive. The CEO described the Darktrace option as ‘an insurance policy we can’t do without’.

Saving time with Darktrace / EMAIL

Darktrace / EMAIL reduced the discovery, alert, and response process from days or weeks to seconds .

Darktrace / EMAIL automates what was originally a time-consuming and repetitive process. The team has recovered between eight and 10 working hours a week by automating much of this process using / EMAIL.

Today, Darktrace / EMAIL prevents phishing emails from reaching employees’ inboxes. The volume of hostile and unsolicited email fell to a third of its original level after Darktrace / EMAIL was set up.

Further savings with Darktrace / NETWORK and Darktrace / IDENTITY

Since its success with Darktrace / EMAIL, the company adopted two more products from the Darktrace ActiveAI Security Platform – Darktrace / NETWORK and Darktrace / IDENTITY.

These have further contributed to cost savings. An initial plan to build a 24/7 SOC would have required hiring and retaining six additional analysts, rather than the two that currently use Darktrace, costing an additional £220,000 per year in salary. With Darktrace, the existing analysts have the tools needed to become more effective and impactful.

An additional benefit: Darktrace adoption has lowered the company’s cyber insurance premiums. The security team can reallocate this budget to proactive projects.

Detection of novel threats provides reassurance

Darktrace’s unique approach to cybersecurity added a key benefit. The team’s previous tool took a rules-based approach – which was only good if the next attack featured the same characteristics as the ones on which the tool was trained.

“Darktrace looks for anomalous behavior, and we needed something that detected and responded based on use cases, not rules that might be out of date or too prescriptive,” their Information Security Manager. “Our existing provider could take a couple of days to update rules and signatures, and in this game, speed is of the essence. Darktrace just does everything we need - without delay.”

Where rules-based tools must wait for a threat to emerge before beginning to detect and respond to it, Darktrace identifies and protects against unknown and novel threats, speeding identification, response, and recovery, minimizing business disruption as a result.

Looking to the future

With Darktrace in place, the UK-based civil engineering company team has reallocated time and resources usually spent on detection and alerting to now tackle more sophisticated, strategic challenges. Darktrace has also equipped the team with far better and more regularly updated visibility into potential vulnerabilities.

“One thing that frustrates me a little is penetration testing; our ISO accreditation mandates a penetration test at least once a year, but the results could be out of date the next day,” their Information Security Manager. “Darktrace / Proactive Exposure Management will give me that view in real time – we can run it daily if needed - and that’s going to be a really effective workbench for my team.”

As the company looks to further develop its security posture, Darktrace remains poised to evolve alongside its partner.

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October 14, 2025

Inside Akira’s SonicWall Campaign: Darktrace’s Detection and Response

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Introduction: Background on Akira SonicWall campaign

Between July and August 2025, security teams worldwide observed a surge in Akira ransomware incidents involving SonicWall SSL VPN devices [1]. Initially believed to be the result of an unknown zero-day vulnerability, SonicWall later released an advisory announcing that the activity was strongly linked to a previously disclosed vulnerability, CVE-2024-40766, first identified over a year earlier [2].

On August 20, 2025, Darktrace observed unusual activity on the network of a customer in the US. Darktrace detected a range of suspicious activity, including network scanning and reconnaissance, lateral movement, privilege escalation, and data exfiltration. One of the compromised devices was later identified as a SonicWall virtual private network (VPN) server, suggesting that the incident was part of the broader Akira ransomware campaign targeting SonicWall technology.

As the customer was subscribed to the Managed Detection and Response (MDR) service, Darktrace’s Security Operations Centre (SOC) team was able to rapidly triage critical alerts, restrict the activity of affected devices, and notify the customer of the threat. As a result, the impact of the attack was limited - approximately 2 GiB of data had been observed leaving the network, but any further escalation of malicious activity was stopped.

Threat Overview

CVE-2024-40766 and other misconfigurations

CVE-2024-40766 is an improper access control vulnerability in SonicWall’s SonicOS, affecting Gen 5, Gen 6, and Gen 7 devices running SonicOS version 7.0.1 5035 and earlier [3]. The vulnerability was disclosed on August 23, 2024, with a patch released the same day. Shortly after, it was reported to be exploited in the wild by Akira ransomware affiliates and others [4].

Almost a year later, the same vulnerability is being actively targeted again by the Akira ransomware group. In addition to exploiting unpatched devices affected by CVE-2024-40766, security researchers have identified three other risks potentially being leveraged by the group [5]:

*The Virtual Office Portal can be used to initially set up MFA/TOTP configurations for SSLVPN users.

Thus, even if SonicWall devices were patched, threat actors could still target them for initial access by reusing previously stolen credentials and exploiting other misconfigurations.

Akira Ransomware

Akira ransomware was first observed in the wild in March 2023 and has since become one of the most prolific ransomware strains across the threat landscape [6]. The group operates under a Ransomware-as-a-Service (RaaS) model and frequently uses double extortion tactics, pressuring victims to pay not only to decrypt files but also to prevent the public release of sensitive exfiltrated data.

The ransomware initially targeted Windows systems, but a Linux variant was later observed targeting VMware ESXi virtual machines [7]. In 2024, it was assessed that Akira would continue to target ESXi hypervisors, making attacks highly disruptive due to the central role of virtualisation in large-scale cloud deployments. Encrypting the ESXi file system enables rapid and widespread encryption with minimal lateral movement or credential theft. The lack of comprehensive security protections on many ESXi hypervisors also makes them an attractive target for ransomware operators [8].

Victimology

Akira is known to target organizations across multiple sectors, most notably those in manufacturing, education, and healthcare. These targets span multiple geographic regions, including North America, Latin America, Europe and Asia-Pacific [9].

Geographical distribution of organization’s affected by Akira ransomware in 2025 [9].
Figure 1: Geographical distribution of organization’s affected by Akira ransomware in 2025 [9].

Common Tactics, Techniques and Procedures (TTPs) [7][10]

Initial Access
Targets remote access services such as RDP and VPN through vulnerability exploitation or stolen credentials.

Reconnaissance
Uses network scanning tools like SoftPerfect and Advanced IP Scanner to map the environment and identify targets.

Lateral Movement
Moves laterally using legitimate administrative tools, typically via RDP.

Persistence
Employs techniques such as Kerberoasting and pass-the-hash, and tools like Mimikatz to extract credentials. Known to create new domain accounts to maintain access.

Command and Control
Utilizes remote access tools including AnyDesk, RustDesk, Ngrok, and Cloudflare Tunnel.

Exfiltration
Uses tools such as FileZilla, WinRAR, WinSCP, and Rclone. Data is exfiltrated via protocols like FTP and SFTP, or through cloud storage services such as Mega.

Darktrace’s Coverage of Akira ransomware

Reconnaissance

Darktrace first detected of unusual network activity around 05:10 UTC, when a desktop device was observed performing a network scan and making an unusual number of DCE-RPC requests to the endpoint mapper (epmapper) service. Network scans are typically used to identify open ports, while querying the epmapper service can reveal exposed RPC services on the network.

Multiple other devices were also later seen with similar reconnaissance activity, and use of the Advanced IP Scanner tool, indicated by connections to the domain advanced-ip-scanner[.]com.

Lateral movement

Shortly after the initial reconnaissance, the same desktop device exhibited unusual use of administrative tools. Darktrace observed the user agent “Ruby WinRM Client” and the URI “/wsman” as the device initiated a rare outbound Windows Remote Management (WinRM) connection to two domain controllers (REDACTED-dc1 and REDACTED-dc2). WinRM is a Microsoft service that uses the WS-Management (WSMan) protocol to enable remote management and control of network devices.

Darktrace also observed the desktop device connecting to an ESXi device (REDACTED-esxi1) via RDP using an LDAP service credential, likely with administrative privileges.

Credential access

At around 06:26 UTC, the desktop device was seen fetching an Active Directory certificate from the domain controller (REDACTED-dc1) by making a DCE-RPC request to the ICertPassage service. Shortly after, the device made a Kerberos login using the administrative credential.

Figure 3: Darktrace’s detection of the of anomalous certificate download and subsequent Kerberos login.

Further investigation into the device’s event logs revealed a chain of connections that Darktrace’s researchers believe demonstrates a credential access technique known as “UnPAC the hash.”

This method begins with pre-authentication using Kerberos’ Public Key Cryptography for Initial Authentication (PKINIT), allowing the client to use an X.509 certificate to obtain a Ticket Granting Ticket (TGT) from the Key Distribution Center (KDC) instead of a password.

The next stage involves User-to-User (U2U) authentication when requesting a Service Ticket (ST) from the KDC. Within Darktrace's visibility of this traffic, U2U was indicated by the client and service principal names within the ST request being identical. Because PKINIT was used earlier, the returned ST contains the NTLM hash of the credential, which can then be extracted and abused for lateral movement or privilege escalation [11].

Flowchart of Kerberos PKINIT pre-authentication and U2U authentication [12].
Figure 4: Flowchart of Kerberos PKINIT pre-authentication and U2U authentication [12]
Figure 5: Device event log showing the Kerberos Login and Kerberos Ticket events

Analysis of the desktop device’s event logs revealed a repeated sequence of suspicious activity across multiple credentials. Each sequence included a DCE-RPC ICertPassage request to download a certificate, followed by a Kerberos login event indicating PKINIT pre-authentication, and then a Kerberos ticket event consistent with User-to-User (U2U) authentication.

Darktrace identified this pattern as highly unusual. Cyber AI Analyst determined that the device used at least 15 different credentials for Kerberos logins over the course of the attack.

By compromising multiple credentials, the threat actor likely aimed to escalate privileges and facilitate further malicious activity, including lateral movement. One of the credentials obtained via the “UnPAC the hash” technique was later observed being used in an RDP session to the domain controller (REDACTED-dc2).

C2 / Additional tooling

At 06:44 UTC, the domain controller (REDACTED-dc2) was observed initiating a connection to temp[.]sh, a temporary cloud hosting service. Open-source intelligence (OSINT) reporting indicates that this service is commonly used by threat actors to host and distribute malicious payloads, including ransomware [13].

Shortly afterward, the ESXi device was observed downloading an executable named “vmwaretools” from the rare external endpoint 137.184.243[.]69, using the user agent “Wget.” The repeated outbound connections to this IP suggest potential command-and-control (C2) activity.

Cyber AI Analyst investigation into the suspicious file download and suspected C2 activity between the ESXI device and the external endpoint 137.184.243[.]69.
Figure 6: Cyber AI Analyst investigation into the suspicious file download and suspected C2 activity between the ESXI device and the external endpoint 137.184.243[.]69.
Packet capture (PCAP) of connections between the ESXi device and 137.184.243[.]69.
Figure 7: Packet capture (PCAP) of connections between the ESXi device and 137.184.243[.]69.

Data exfiltration

The first signs of data exfiltration were observed at around 7:00 UTC. Both the domain controller (REDACTED-dc2) and a likely SonicWall VPN device were seen uploading approximately 2 GB of data via SSH to the rare external endpoint 66.165.243[.]39 (AS29802 HVC-AS). OSINT sources have since identified this IP as an indicator of compromise (IoC) associated with the Akira ransomware group, known to use it for data exfiltration [14].

Cyber AI Analyst incident view highlighting multiple unusual events across several devices on August 20. Notably, it includes the “Unusual External Data Transfer” event, which corresponds to the anomalous 2 GB data upload to the known Akira-associated endpoint 66.165.243[.]39.
Figure 8: Cyber AI Analyst incident view highlighting multiple unusual events across several devices on August 20. Notably, it includes the “Unusual External Data Transfer” event, which corresponds to the anomalous 2 GB data upload to the known Akira-associated endpoint 66.165.243[.]39.

Cyber AI Analyst

Throughout the course of the attack, Darktrace’s Cyber AI Analyst autonomously investigated the anomalous activity as it unfolded and correlated related events into a single, cohesive incident. Rather than treating each alert as isolated, Cyber AI Analyst linked them together to reveal the broader narrative of compromise. This holistic view enabled the customer to understand the full scope of the attack, including all associated activities and affected assets that might otherwise have been dismissed as unrelated.

Overview of Cyber AI Analyst’s investigation, correlating all related internal and external security events across affected devices into a single pane of glass.
Figure 9: Overview of Cyber AI Analyst’s investigation, correlating all related internal and external security events across affected devices into a single pane of glass.

Containing the attack

In response to the multiple anomalous activities observed across the network, Darktrace's Autonomous Response initiated targeted mitigation actions to contain the attack. These included:

  • Blocking connections to known malicious or rare external endpoints, such as 137.184.243[.]69, 66.165.243[.]39, and advanced-ip-scanner[.]com.
  • Blocking internal traffic to sensitive ports, including 88 (Kerberos), 3389 (RDP), and 49339 (DCE-RPC), to disrupt lateral movement and credential abuse.
  • Enforcing a block on all outgoing connections from affected devices to contain potential data exfiltration and C2 activity.
Autonomous Response actions taken by Darktrace on an affected device, including the blocking of malicious external endpoints and internal service ports.
Figure 10: Autonomous Response actions taken by Darktrace on an affected device, including the blocking of malicious external endpoints and internal service ports.

Managed Detection and Response

As this customer was an MDR subscriber, multiple Enhanced Monitoring alerts—high-fidelity models designed to detect activity indicative of compromise—were triggered across the network. These alerts prompted immediate investigation by Darktrace’s SOC team.

Upon determining that the activity was likely linked to an Akira ransomware attack, Darktrace analysts swiftly acted to contain the threat. At around 08:05 UTC, devices suspected of being compromised were quarantined, and the customer was promptly notified, enabling them to begin their own remediation procedures without delay.

A wider campaign?

Darktrace’s SOC and Threat Research teams identified at least three additional incidents likely linked to the same campaign. All targeted organizations were based in the US, spanning various industries, and each have indications of using SonicWall VPN, indicating it had likely been targeted for initial access.

Across these incidents, similar patterns emerged. In each case, a suspicious executable named “vmwaretools” was downloaded from the endpoint 85.239.52[.]96 using the user agent “Wget”, bearing some resemblance to the file downloads seen in the incident described here. Data exfiltration was also observed via SSH to the endpoints 107.155.69[.]42 and 107.155.93[.]154, both of which belong to the same ASN also seen in the incident described in this blog: S29802 HVC-AS. Notably, 107.155.93[.]154 has been reported in OSINT as an indicator associated with Akira ransomware activity [15]. Further recent Akira ransomware cases have been observed involving SonicWall VPN, where no similar executable file downloads were observed, but SSH exfiltration to the same ASN was. These overlapping and non-overlapping TTPs may reflect the blurring lines between different affiliates operating under the same RaaS.

Lessons from the campaign

This campaign by Akira ransomware actors underscores the critical importance of maintaining up-to-date patching practices. Threat actors continue to exploit previously disclosed vulnerabilities, not just zero-days, highlighting the need for ongoing vigilance even after patches are released. It also demonstrates how misconfigurations and overlooked weaknesses can be leveraged for initial access or privilege escalation, even in otherwise well-maintained environments.

Darktrace’s observations further reveal that ransomware actors are increasingly relying on legitimate administrative tools, such as WinRM, to blend in with normal network activity and evade detection. In addition to previously documented Kerberos-based credential access techniques like Kerberoasting and pass-the-hash, this campaign featured the use of UnPAC the hash to extract NTLM hashes via PKINIT and U2U authentication for lateral movement or privilege escalation.

Credit to Emily Megan Lim (Senior Cyber Analyst), Vivek Rajan (Senior Cyber Analyst), Ryan Traill (Analyst Content Lead), and Sam Lister (Specialist Security Researcher)

Appendices

Darktrace Model Detections

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Possible Data Staging and External Upload

Anomalous Connection / Rare WinRM Incoming

Anomalous Connection / Rare WinRM Outgoing

Anomalous Connection / Uncommon 1 GiB Outbound

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Unusual Incoming Long Remote Desktop Session

Anomalous Connection / Unusual Incoming Long SSH Session

Anomalous Connection / Unusual Long SSH Session

Anomalous File / EXE from Rare External Location

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Server Activity / Outgoing from Server

Anomalous Server Activity / Rare External from Server

Compliance / Default Credential Usage

Compliance / High Priority Compliance Model Alert

Compliance / Outgoing NTLM Request from DC

Compliance / SSH to Rare External Destination

Compromise / Large Number of Suspicious Successful Connections

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Device / Anomalous Certificate Download Activity

Device / Anomalous SSH Followed By Multiple Model Alerts

Device / Anonymous NTLM Logins

Device / Attack and Recon Tools

Device / ICMP Address Scan

Device / Large Number of Model Alerts

Device / Network Range Scan

Device / Network Scan

Device / New User Agent To Internal Server

Device / Possible SMB/NTLM Brute Force

Device / Possible SMB/NTLM Reconnaissance

Device / RDP Scan

Device / Reverse DNS Sweep

Device / Suspicious SMB Scanning Activity

Device / UDP Enumeration

Unusual Activity / Unusual External Data to New Endpoint

Unusual Activity / Unusual External Data Transfer

User / Multiple Uncommon New Credentials on Device

User / New Admin Credentials on Client

User / New Admin Credentials on Server

Enhanced Monitoring Models

Compromise / Anomalous Certificate Download and Kerberos Login

Device / Initial Attack Chain Activity

Device / Large Number of Model Alerts from Critical Network Device

Device / Multiple Lateral Movement Model Alerts

Device / Suspicious Network Scan Activity

Unusual Activity / Enhanced Unusual External Data Transfer

Antigena/Autonomous Response Models

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

Antigena / Network / Manual / Quarantine Device

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Significant Anomaly / Repeated Antigena Alerts

List of Indicators of Compromise (IoCs)

·      66.165.243[.]39 – IP Address – Data exfiltration endpoint

·      107.155.69[.]42 – IP Address – Probable data exfiltration endpoint

·      107.155.93[.]154 – IP Address – Likely Data exfiltration endpoint

·      137.184.126[.]86 – IP Address – Possible C2 endpoint

·      85.239.52[.]96 – IP Address – Likely C2 endpoint

·      hxxp://85.239.52[.]96:8000/vmwarecli  – URL – File download

·      hxxp://137.184.126[.]86:8080/vmwaretools – URL – File download

MITRE ATT&CK Mapping

Initial Access – T1190 – Exploit Public-Facing Application

Reconnaissance – T1590.002 – Gather Victim Network Information: DNS

Reconnaissance – T1590.005 – Gather Victim Network Information: IP Addresses

Reconnaissance – T1592.004 – Gather Victim Host Information: Client Configurations

Reconnaissance – T1595 – Active Scanning

Discovery – T1018 – Remote System Discovery

Discovery – T1046 – Network Service Discovery

Discovery – T1083 – File and Directory Discovery

Discovery – T1135 – Network Share Discovery

Lateral Movement – T1021.001 – Remote Services: Remote Desktop Protocol

Lateral Movement – T1021.004 – Remote Services: SSH

Lateral Movement – T1021.006 – Remote Services: Windows Remote Management

Lateral Movement – T1550.002 – Use Alternate Authentication Material: Pass the Hash

Lateral Movement – T1550.003 – Use Alternate Authentication Material: Pass the Ticket

Credential Access – T1110.001 – Brute Force: Password Guessing

Credential Access – T1649 – Steal or Forge Authentication Certificates

Persistence, Privilege Escalation – T1078 – Valid Accounts

Resource Development – T1588.001 – Obtain Capabilities: Malware

Command and Control – T1071.001 – Application Layer Protocol: Web Protocols

Command and Control – T1105 – Ingress Tool Transfer

Command and Control – T1573 – Encrypted Channel

Collection – T1074 – Data Staged

Exfiltration – T1041 – Exfiltration Over C2 Channel

Exfiltration – T1048 – Exfiltration Over Alternative Protocol

References

[1] https://thehackernews.com/2025/08/sonicwall-investigating-potential-ssl.html

[2] https://www.sonicwall.com/support/notices/gen-7-and-newer-sonicwall-firewalls-sslvpn-recent-threat-activity/250804095336430

[3] https://psirt.global.sonicwall.com/vuln-detail/SNWLID-2024-0015

[4] https://arcticwolf.com/resources/blog/arctic-wolf-observes-akira-ransomware-campaign-targeting-sonicwall-sslvpn-accounts/

[5] https://www.rapid7.com/blog/post/dr-akira-ransomware-group-utilizing-sonicwall-devices-for-initial-access/

[6] https://www.ic3.gov/AnnualReport/Reports/2024_IC3Report.pdf

[7] https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-109a

[8] https://blog.talosintelligence.com/akira-ransomware-continues-to-evolve/

[9] https://www.ransomware.live/map?year=2025&q=akira

[10] https://attack.mitre.org/groups/G1024/
[11] https://labs.lares.com/fear-kerberos-pt2/#UNPAC

[12] https://www.thehacker.recipes/ad/movement/kerberos/unpac-the-hash

[13] https://www.s-rminform.com/latest-thinking/derailing-akira-cyber-threat-intelligence)

[14] https://fieldeffect.com/blog/update-akira-ransomware-group-targets-sonicwall-vpn-appliances

[15] https://arcticwolf.com/resources/blog/arctic-wolf-observes-july-2025-uptick-in-akira-ransomware-activity-targeting-sonicwall-ssl-vpn/

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About the author
Emily Megan Lim
Cyber Analyst
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