August 22, 2022

Emotet Resurgence: Cross-Industry Analysis

Technical insights on the Emotet resurgence in 2022 across various client environments, industries, and regions.

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

Eugene Chua

Cyber Security Analyst

Aug 2022

Introduction

Last year provided further evidence that the cyber threat landscape remains both complex and challenging to predict. Between uncertain attribution, novel exploits and rapid malware developments, it is becoming harder to know where to focus security efforts. One of the largest surprises of 2021 was the re-emergence of the infamous Emotet botnet. This is an example of a campaign that ignored industry verticals or regions and seemingly targeted companies indiscriminately. Only 10 months after the Emotet takedown by law enforcement agencies in January, new Emotet activities in November were discovered by security researchers. These continued into the first quarter of 2022, a period which this blog will explore through findings from the Darktrace Threat Intel Unit.

Dating back to 2019, Emotet was known to deliver Trickbot payloads which ultimately deployed Ryuk ransomware strains on compromised devices. This interconnectivity highlighted the hydra-like nature of threat groups wherein eliminating one (even with full-scale law enforcement intervention) would not rule them out as a threat nor indicate that the threat landscape would be any more secure.

When Emotet resurged, as expected, one of the initial infection vectors involved leveraging existing Trickbot infrastructure. However, unlike the original attacks, it featured a brand new phishing campaign.

Figure 1: Distribution of observed Emotet activities across Darktrace deployments

Although similar to the original Emotet infections, the new wave of infections has been classified into two categories: Epochs 4 and 5. These had several key differences compared to Epochs 1 to 3. Within Darktrace’s global deployments, Emotet compromises associated to Epoch 4 appeared to be the most prevalent. Affected customer environments were seen within a large range of countries (Figure 1) and industry verticals such as manufacturing and supply chain, hospitality and travel, public administration, technology and telecoms and healthcare. Company demographics and size did not appear to be a targeting factor as affected customers had varying employee counts ranging from less than 250, to over 5000.

Key differences between Epochs 1-3 vs 4-5

Based on wider security research into the innerworkings of the Emotet exploits, several key differences were identified between Epochs 4/5 and its predecessors. The newer epochs used:

· A different Microsoft document format (OLE vs XML-based).

· A different encryption algorithm for communication. The new epochs used Elliptic Curve Cryptograph (ECC) [1] with public encryption keys contained in the C2 configuration file [2]. This was different from the previous Rivest-Shamir-Adleman (RSA) key encryption method.

· Control Flow Flattening was used as an obfuscation technique to make detection and reverse engineering more difficult. This is done by hiding a program’s control flow [3].

· New C2 infrastructure was observed as C2 communications were directed to over 230 unique IPs all associated to the new Epochs 4 and 5.

In addition to the new Epoch 4 and 5 features, Darktrace detected unsurprising similarities in those deployments affected by the renewed campaign. This included self-signed SSL connections to Emotet’s new infrastructure as well as malware spam activities to multiple rare external endpoints. Preceding these outbound communications, devices across multiple deployments were detected downloading Emotet-associated payloads (algorithmically generated DLL files).

Emotet Resurgence Campaign

Figure 2: Darktrace’s Detection Timeline for Emotet Epoch 4 and 5 compromises

1. Initial Compromise

The initial point of entry for the resurgence activity was almost certainly via Trickbot infrastructure or a successful phishing attack (Figure 2). Following the initial intrusion, the malware strain begins to download payloads via macro-ladened files which are used to spawn PowerShell for subsequent malware downloads.

Following the downloads, malicious communication with Emotet’s C2 infrastructure was observed alongside activities from the spam module. Within Darktrace, key techniques were observed and documented below.

2. Establish Foothold: Binary Dynamic-link library (.dll) with algorithmically generated filenames

Emotet payloads are polymorphic and contain algorithmically generated filenames . Within deployments, HTTP GET requests involving a suspicious hostname, www[.]arkpp[.]com, and Emotet related samples such as those seen below were observed:

· hpixQfCoJb0fS1.dll (SHA256 hash: 859a41b911688b00e104e9c474fc7aaf7b1f2d6e885e8d7fbf11347bc2e21eaa)

· M0uZ6kd8hnzVUt2BNbRzRFjRoz08WFYfPj2.dll (SHA256 hash: 9fbd590cf65cbfb2b842d46d82e886e3acb5bfecfdb82afc22a5f95bda7dd804)

· TpipJHHy7P.dll (SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b)

These DLL files likely represent the distribution of Emotet loaders which depends on windows processes such as rundll32[.]exe and regsvr32[.]exe to execute.

3. Establish Foothold: Outbound SSL connections to Emotet C2 servers

A clear network indicator of compromise for Emotet’s C2 communication involved self-signed SSL using certificate issuers and subjects which matched ‘CN=example[.]com,OU=IT Department,O=Global Security,L=London,ST=London,C=GB’ , and a common JA3 client fingerprint (72a589da586844d7f0818ce684948eea). The primary C2 communications were seen involving infrastructures classified as Epoch 4 rather than 5. Despite encryption in the communication content, network contextual connection details were sufficient for the detection of the C2 activities (Figure 3).

Figure 3: UI Model Breach logs on download and outbound SSL activities.

Outbound SSL and SMTP connections on TCP ports 25, 465, 587

An anomalous user agent such as, ‘Microsoft Outlook 15.0’, was observed being used for SMTP connections with some subject lines of the outbound emails containing Base64-encoded strings. In addition, this JA3 client fingerprint (37cdab6ff1bd1c195bacb776c5213bf2) was commonly seen from the SSL connections. Based on the set of malware spam hostnames observed across at least 10 deployments, the majority of the TLDs were .jp, .com, .net, .mx, with the Japanese TLD being the most common (Figure 4).

Figure 4: Malware Spam TLDs observed in outbound SSL and SMTP

Plaintext spam content generated from the spam module were seen in PCAPs (Figure 5). Examples of clear phishing or spam indicators included 1) mismatched personal header and email headers, 2) unusual reply chain and recipient references in the subject line, and 3) suspicious compressed file attachments, e.g. Electronic form[.]zip.

Figure 5: Example of PCAP associated to SPAM Module

4. Accomplish Mission

The Emotet resurgence also showed through secondary compromises involving anomalous SMB drive writes related to CobaltStrike. This consistently included the following JA3 hash (72a589da586844d7f0818ce684948eea) seen in SSL activities as well as SMB writes involving the svchost.exe file.

Darktrace Detection

The key DETECT models used to identify Emotet Resurgence activities were focused on determining possible C2. These included:

· Suspicious SSL Activity

· Suspicious Self-Signed SSL

· Rare External SSL Self-Signed

· Possible Outbound Spam

File-focused models were also beneficial and included:

· Zip or Gzip from Rare External Location

· EXE from Rare External Location

Darktrace’s detection capabilities can also be shown through a sample of case studies identified during the Threat Research team’s investigations.

Case Studies

Darktrace’s detection of Emotet activities was not limited by industry verticals or company sizing. Although there were many similar features seen across the new epoch, each incident displayed varying techniques from the campaign. This is shown in two client environments below:

When investigating a large customer environment within the public administration sector, 16 different devices were detected making 52,536 SSL connections with the example[.]com issuer. Devices associated with this issuer were mainly seen breaching the same Self-Signed and Spam DETECT models. Although anomalous incoming octet-streams were observed prior to this SSL, there was no clear relation between the downloads and the Emotet C2 connections. Despite the total affected devices occupying only a small portion of the total network, Darktrace analysts were able to filter against the much larger network ‘noise’ and locate detailed evidence of compromise to notify the customer.

Darktrace also identified new Emotet activities in much smaller customer environments. Looking at a company in the healthcare and pharmaceutical sector, from mid-March 2022 a single internal device was detected making an HTTP GET request to the host arkpp[.]com involving the algorithmically-generated DLL, TpipJHHy7P.dll with the SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b (Figure 6).

Figure 6: A screenshot from VirusTotal, showing that the SHA256 hash has been flagged as malicious by other security vendors.

After the sample was downloaded, the device contacted a large number of endpoints that had never been contacted by devices on the network. The endpoints were contacted over ports 443, 8080, and 7080 involving Emotet related IOCs and the same SSL certificate mentioned previously. Malware spam activities were also observed during a similar timeframe.

The Emotet case studies above demonstrate how autonomous detection of an anomalous sequence of activities - without depending on conventional rules and signatures - can reveal significant threat activities. Though possible staged payloads were only seen in a proportion of the affected environments, the following outbound C2 and malware spam activities involving many endpoints and ports were sufficient for the detection of Emotet.

If present, in both instances Darktrace’s Autonomous Response technology, RESPOND, would recommend or implement surgical actions to precisely target activities associated with the staged payload downloads, outgoing C2 communications, and malware spam activities. Additionally, restriction to the devices’ normal pattern of life will prevent simultaneously occurring malicious activities while enabling the continuity of normal business operations.

Conclusion

· The technical differences between past and present Emotet strains emphasizes the versatility of malicious threat actors and the need for a security solution that is not reliant on signatures.

· Darktrace’s visibility and unique behavioral detection continues to provide visibility to network activities related to the novel Emotet strain without reliance on rules and signatures. Key examples include the C2 connections to new Emotet infrastructure.

· Looking ahead, detection of C2 establishment using suspicious DLLs will prevent further propagation of the Emotet strains across networks.

· Darktrace’s AI detection and response will outpace conventional post compromise research involving the analysis of Emotet strains through static and dynamic code analysis, followed by the implementation of rules and signatures.

Thanks to Paul Jennings and Hanah Darley for their contributions to this blog.

Appendices

Model breaches

· Anomalous Connection / Anomalous SSL without SNI to New External

· Anomalous Connection / Application Protocol on Uncommon Port

· Anomalous Connection / Multiple Connections to New External TCP Port

· Anomalous Connection / Multiple Failed Connections to Rare Endpoint

· Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

· Anomalous Connection / Possible Outbound Spam

· Anomalous Connection / Rare External SSL Self-Signed

· Anomalous Connection / Repeated Rare External SSL Self-Signed

· Anomalous Connection / Suspicious Expired SSL

· Anomalous Connection / Suspicious Self-Signed SSL

· Anomalous File / Anomalous Octet Stream (No User Agent)

· Anomalous File / Zip or Gzip from Rare External Location

· Anomalous File / EXE from Rare External Location

· Compromise / Agent Beacon to New Endpoint

· Compromise / Beacon to Young Endpoint

· Compromise / Beaconing Activity To External Rare

· Compromise / New or Repeated to Unusual SSL Port

· Compromise / Repeating Connections Over 4 Days

· Compromise / Slow Beaconing Activity To External Rare

· Compromise / SSL Beaconing to Rare Destination

· Compromise / Suspicious Beaconing Behaviour

· Compromise / Suspicious Spam Activity

· Compromise / Suspicious SSL Activity

· Compromise / Sustained SSL or HTTP Increase

· Device / Initial Breach Chain Compromise

· Device / Large Number of Connections to New Endpoints

· Device / Long Agent Connection to New Endpoint

· Device / New User Agent

· Device / New User Agent and New IP

· Device / SMB Session Bruteforce

· Device / Suspicious Domain

· Device / Suspicious SMB Scanning Activity

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For Darktrace customers who want to know more about using Darktrace to triage Emotet, refer here for an exclusive supplement to this blog.

References

[1] https://blog.lumen.com/emotet-redux/

[2] https://blogs.vmware.com/security/2022/03/emotet-c2-configuration-extraction-and-analysis.html

[3] https://news.sophos.com/en-us/2022/05/04/attacking-emotets-control-flow-flattening/

Written by

Eugene Chua

Cyber Security Analyst

Inside the SOC

Written by

Eugene Chua

Cyber Security Analyst

VP, Security & AI Strategy, Field CISO

Senior Product Marketing Manager, Email

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Identity

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1. Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

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About the author

Carlos Gray

Senior Product Marketing Manager, Email

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Proactive Security

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

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Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

Detecting threats without relying on CVE disclosure

Abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems. Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

‍For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Often, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender: attack vs patch.

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security. After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

Autonomous Response

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

Related Darktrace blogs:

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*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

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Emotet Resurgence: Cross-Industry Analysis

Introduction

Key differences between Epochs 1-3 vs 4-5

Emotet Resurgence Campaign

Darktrace Detection

Case Studies

Conclusion

Appendices

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Top Eight Threats to SaaS Security and How to Combat Them

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Identity

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

The latest on the identity security landscape

What is SaaS security?

1. Account Takeover (ATO)

2. Privilege escalation

3. Lateral movement

4. Multi-Factor Authentication (MFA) bypass and session hijacking

5. OAuth token abuse

6. Insider threats

7. Application Programming Interface (API)-based attacks

8. Business Email Compromise (BEC) via SaaS

Protecting against these SaaS threats

AI-powered SaaS security stays ahead of the threat landscape

Blog

Proactive Security

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Detecting threats without relying on CVE disclosure

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Autonomous Response

Conclusion

References and further reading:

Related Darktrace blogs: