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
George Kim
Analyst Consulting Lead – AMS
Share
24
Oct 2022
Two of the most popular ways threat actors send malicious emails is through the use of spoofing and impersonation tactics. While spoofed emails are sent on behalf of a trusted domain and obscure the true source of the sender, impersonation emails come from a fake domain, but one that may be visually confused for an authentic one. In order to identify impersonation tactics in a suspicious email, we should first ask why an attacker might utilize an impersonation approach over spoofing.
In contrast to domain spoofing, which lacks validation and can be readily detected by email security gateway softwares, impersonation with a lookalike domain allows attackers to send emails with full SPF and DKIM validation, making them appear legitimate to many security gateways. This blog will explore impersonation tactics and how Darktrace/Email protects against them.
There are two distinct ways to leverage impersonation tactics:
1. Impersonating the domain
2. Impersonating a real user from that domain
Domain impersonation is often implemented with the use of ‘confusable characters’. This involves misspelling through the use of character substitutions which make the domain look as visually similar to the original as possible (eg. m rn, o 0, l I). Threat actors can then also impersonate a real user by adding the the personal field of that user’s email to the new, malicious domain. Comparing impersonation emails with legitimate emails highlights how similar these malicious email addresses are to the real thing (Figure 1).
Figure 1- Email log that highlights the impersonated emails from “Mike Lewis” from the domain “smartercornmerce[.]net”. Along with the impersonated domain, the attackers attempt to impersonate the known user, “Mike Lewis” as well. The use of both distinct types of impersonation categorize the email as what Darktrace/Email refers to as a Double Impersonation email.
Figure 2- Email Summary details of one of the malicious double impersonation emails that was sent by the impersonated sender, “Mike Lewis” from “smartercornmerce[.]net”, that highlights the various anomaly indicators that Darktrace/Email detected, as well the various tags and actions it applied.
Darktrace/Email uses AI which analyses impersonation emails by comparing the ‘From’ header domains of emails against known external domains and generates a percentage score for how likely the domain is to be an imitation of the known domain (Figure 3).
Figure 3- Darktrace compares the external sender, “mike.lewis@smartercornmerce[.]net”, with similar external names and domains that have been observed in different inbound emails on the network.
Impersonation emails are also detected via spoof score metrics such as Domain External Spoof Score and Domain Internal Spoof Score (Figure 4).
Figure 4- Darktrace AI analyzed the malicious double impersonation email from Figure 2 and generated a high Domain External Spoof Score (100) and Spoof Score External (94)
Double Impersonation emails such as the one highlighted in Figure 2 are utilized by threat actors to gain the trust of the recipient and convince them to access malicious payloads such as phishing links and attachments. For example, the malicious double impersonation email from Figure 2 contained a suspicious hidden link to a Wordpress site which could have redirected the user to a phishing endpoint and tricked them into divulging sensitive information (Figure 5). The endpoint itself appears to lead unsuspecting recipients to a false share link posing as a payment-themed Excel file.
Figure 5- Details of the Wordpress link embedded in the suspicious email, which was hidden beneath display text to convince a user to click it without knowledge of where it would lead. The domain has a 100% rarity according to Darktrace AI.
Figure 6- Wordpress webpage that highlights another link for the user to click in order to be redirected to the invoice statement in a Microsoft Excel document.
Various indicators highlighted the webpage as suspicious and potentially malicious. Firstly, the use of ‘SmarterCORNmerce’ in the link to the webpage was at odds with the use of SmarterCOMMERCE throughout the page itself. The link also showed the invoice statement to be an Microsoft Excel file, despite the email suggesting it was a PDF document. Further investigation revealed the link to be associated with a Fleek hosting service and CDN (Figure 7), and that it redirected users to a fake Microsoft page.
Figure 7 - Source code from the Wordpress webpage shows that the fake Microsoft link redirects users to a Fleek hosted page. This page may contain additional javascript content to download malware onto the user’s device.
As well as the domain spoof score metrics highlighted in Figure 4, Darktrace/Email analyses the suspicious payloads embedded in emails and generates scores to indicate the likelihood that a payload may be a phishing attempt.
Figure 8- Additional metrics for the double impersonation email that highlight the high phishing inducement score (96) for the email.
As the DETECT functionality of Darktrace/Email generates high scores metrics such as Domain External Spoof Score and Phishing Inducement, the RESPOND function will fire complementary models which then trigger relevant actions on the various payloads embedded in these emails and even the delivery of the emails themselves. As the impersonation email highlighted in Figure 2 impersonated not only the trusted domain but the known and trusted sender, Darktrace AI triggers the Double Impersonation model. Additional spoofing models such as ‘Basic Known Entity Similarities + Suspicious Content’ and ‘External Domain Similarities + Maximum Similarity’ were also triggered, indicating the high possibility that the suspicious email is a domain and user impersonation email sent by a malicious attacker.
Figure 9- The Email console highlights the different models the email triggered, including the Basic Known Entity Similarities + Suspicious Content and External Domain Similarities + Maximum Similarity model breaches and the various models that triggered significant actions in response to the potentially malicious impersonation email.
When Darktrace/Email detects a malicious double impersonation email, it responds by triggering a Hold action, preventing the email from appearing in the recipient’s inbox. Darktrace/Email’s RESPOND functionality could also take action against the suspicious link payloads embedded in the email with a Double Lock Link action. This will prevent users from attempting to click on malicious phishing links. Such actions highlight how Darktrace/Email excels in using AI to detect and take action against potentially malicious impersonation emails that may be prevalent in any user’s inbox.
Though impersonation is becoming increasingly targeted and efficient, Darktrace/Email has both detection and response capabilities that can ensure customers have secure coverage for their email environments.
Thanks to Ben Atkins for his contributions to this blog.
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.
Journey of a Threat: How Multi-Layered AI Works in Darktrace / EMAIL
Follow a malicious email as it moves through Darktrace / EMAIL’s multi-layered AI system, from raw data to final decision. Each layer works together to detect threats, understand intent, and take autonomous action.
How email-delivered prompt injection attacks can target enterprise AI – and why it matters
Prompt injection is a newly emerging threat, with only a handful of confirmed victims so far – targeting how AI systems use data rather than exploiting traditional software vulnerabilities. As agentic AI becomes embedded across enterprise environments, attackers may attempt to manipulate these systems through hidden instructions in everyday email content.
Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems
Three shifts have reshaped what it means to defend an enterprise securely.
First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.
Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.
Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.
If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.
This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.
A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.
The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.
In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.
This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.
Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.
Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.
CVE
CVE public disclosure date
Darktrace detection date
Days between detection of exploitation and CVE public disclosure
CVE-2025-0994Trimble Cityworks
2025-02-06
2025-01-19
18 days
CVE-2025-24183Apache
2025-03-10
2025-02-18
20 days
CVE-2025-10035Fortra GoAnywhere
2025-09-18
2025-09-11
7 days
CVE-2026-0257PAN-OS
2026-05-13
—
—
Identity is the real control plane
The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.
Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.
This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.
In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however, they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.
Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.
AI accelerates the threat
The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.
The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.
The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.
Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.
1. AI as an Attack Multiplier
In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].
Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.
What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.
Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.
AI as a trusted but dangerous actor
This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.
The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.
In these scenarios, the security challenge shifts from validating access to validating behavior.
This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.
Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.
Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.
The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.
For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.
Conclusion
Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.
In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.
Credit to: Daniel Levy, Threat Hunting Data Scientist
2026年6月12日、DarktraceはLiteLLM-Proxyという名前のAmazon Web Service (AWS) EC2インスタンスから暗号通貨マイニング発生中とみられるアクティビティを観測しました。このインスタンスはLiteLLMアクティビティをサポートしており、Amazon Bedrockリソースへのアクセス権を有するインスタンスプロファイルと関連付けられていました。
.avif)
