July 7, 2020

Cryptomining Campaigns & Technical Analysis of Vulnerability

Crypto-mining campaigns stood no chance against Darktrace's AI as it identified the threat in real time. Put your trust in Darktrace's assistance!

Written by

Max Heinemeyer

Global Field CISO

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

Max Heinemeyer

Global Field CISO

Jul 2020

Introduction

The speed with which attackers can weaponize vulnerabilities is steadily increasing. While technology is rapidly evolving and cyber-attacks are becoming more sophisticated, the advantages of exploiting software vulnerabilities over devising a more elaborate and lengthy attack plan have not been overlooked by hackers. These vulnerabilities are also a quick way to gain access into a businesses’ infrastructure. In recent years, attackers have found great benefit and substantial success through quickly weaponizing vulnerabilities in web-facing systems.

Just recently, critical vulnerabilities in Citrix Gateway resulted in a spate of activity targeting Darktrace customers, as reported earlier this year. Without an immediate patch released upon the public announcement of the discovered flaws in Citrix, exploits quickly followed. Similarly, in late April, SaltStack developers reported vulnerabilities in Salt, an open source framework used to monitor and update the state of servers in cloud environments and data centers.

The vulnerabilities found in Salt would allow hackers to bypass authentication and authorization controls and execute code in Salt master servers exposed to the internet. The Salt master is responsible for sending commands to Salt minions and can manage thousands of minions at once. Due to this structure, one exposed Salt master can lead to a compromise of all underlying minions.

On May 2, Darktrace detected successful crypto-miner infections across a number of its customers exploiting the CVE-2020-11651 and CVE-2020-11652 vulnerabilities in SaltStack server management software. In the same weekend, LineageOS — an Android mobile operating system – and Ghost — a blogging platform – both reported suffering a crypto-mining attack due to exposed, unpatched Salt servers. Most notable about these attacks was the sheer speed from a vulnerability being published to a widespread attack campaign.

Timeline

Figure 1: A timeline of events identified by Darktrace on May 3

Technical analysis

Initial compromise

Darktrace initially detected that a number of customer servers running SaltStack were making external connections to endpoints previously not seen on the network. The connections used the curl or wget utilities to download and execute a bash script, which would install a secondary-stage payload containing a cryptocurrency miner.

The systems were targeted directly utilizing 2020-11651 and CVE-2020-11652 vulnerabilities in the ZeroMQ protocol running on SaltStack. These vulnerabilities would allow direct remote code execution as root on the targeted systems, allowing the script to be downloaded and executed successfully with highest system privileges.

The downloader script is almost identical to the one utilized in March in H2Miner infections targeting exposed Docker APIs and Redis instances.

Before downloading the secondary stage payload, the script cleans the target system of a number of pre-existing infections and miners, as well as disabling a number of known security tools and software.

Figure 2: The downloader script

Following the initial clean up, the script would iterate through three functions to download the crypto-miner payload — salt-storer

SHA256 837d768875417578c0b1cab4bd0aa38146147799f643bb7b3c6c6d3d82d7aa2a

— from three different hard-coded servers. An MD5 check for the downloaded executable would be performed prior to execution. The below screenshot illustrates two out of the three downloader functions that would be invoked.

Figure 3: Two of the downloader functions

Second stage payload

Following the cryptographic checks, the downloaded ELF LSB executable kicks into action. No payload analysis was carried out, however it’s execution would result in a crypto-miner being installed and a C2 channel opened.

OSINT indicates that several new versions of the payload were observed carrying additional capabilities, including database dumping and advanced persistence methods. The variants detected by Darktrace’s AI included the more advanced “Version 5” payload purported to have worming capabilities, but in this case they were not observed directly.

Command and control

Upon the execution of an LSB executable, a plaintext HTTP C2 channel would be established, sending basic metadata about the infected host such as processor architecture, available resources, and whether root execution was achieved. This indicates that the C2 mechanisms were likely repurposed from other infections, as this particular infection would execute as root, making the respective component redundant.

Figure 4: A Command and control channel

The complete attack lifecycle was investigated and reported on by Darktrace’s Cyber AI Analyst, which automatically surfaced some crucial details regarding the C2 communication, including other servers that were seen making similar communication patterns, as seen in the bottom right below.

Figure 5: The Cyber AI Analyst automatically generating a natural-language summary of the overall security incident

Figure 6: Further information on the suspicious endpoints

Actions on target

Lastly, devices began mining for cryptocurrency. Cryptocurrency mining demands a substantial proportion of a device’s processing power, such as CPU and GPU, in order to calculate hashes. However, except for the occasional increase in CPU or RAM usage, it can go undetected for months as traditional security products do not normally detect its pattern of behavior as malicious.

Conclusion

Failing to patch vulnerabilities quickly and decisively can have serious consequences. Sometimes, however, the window of opportunity before an attack hits is too short for patching to be feasible. This example demonstrates how quickly unpatched vulnerabilities can be exploited following an initial public disclosure. And yet, even two months after SaltStack published the updates, many Salt servers remain unpatched and run the risk of becoming compromised.

In the case of Citrix, some exploits led to a ransomware attack. Darktrace’s AI-powered Immune System technology not only detected every stage of these ransomware attacks, but its autonomous response was able to halt any anomalous event and contain further damage.

Because new vulnerabilities are, by nature, unexpected, traditional security tools relying on rules and signatures don’t know to look for malicious activity that arises as a result. However, with its constantly evolving understanding of ‘normal’, Darktrace’s AI detects and investigates any unusual behavior, regardless of its origin or whether an attack has been seen before.

Crypto-mining is still favored among many threat actors due to its ability to generate profits, and a successfully infection can have a serious impact on the confidentiality and integrity of the corporate network. The need for Cyber AI that can detect new vulnerabilities and novel threats, and autonomously respond to stop an attack in its tracks, are critical to ensuring businesses remain secure in the face of cyber-criminals who are mobilizing to exploit vulnerabilities more quickly than ever.

IoCs:

IoCComment144.217.129[.]111Likely C2, URIs: /ms /h /s91.215.152[.]69Likely C2, URI: /h89.223.121[.]139Download of payload sa.sh217.12.210[.]192Download of payload sa.sh45.147.201[.]62Destination for crypto-mining217.12.210[.]245Download of payload salt_storer

Darktrace model breaches:

Device / Initial Breach Chain Compromise
Compromise / SSL or HTTP Beacon
Device / Large Number of Model Breaches
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous File / Script from Rare External
Compromise / Beaconing Activity To External Rare
Anomalous Connection / Multiple Failed Connections to Rare Destination
Compromise / Sustained SSL or HTTP Increase
Compliance / Crypto Currency Mining Activity

‍

Written by

Max Heinemeyer

Global Field CISO

Inside the SOC

Written by

Max Heinemeyer

Global Field CISO

•

June 11, 2025

Nicole Wong

Cyber Security Analyst

Product Marketing Manager, OT Security & Compliance

Watch the NIS2 Webinar

Blog

Cloud

June 12, 2025

Breaking Silos: Why Unified Security is Critical in Hybrid World

Hybrid environments demand end-to-end visibility to stop modern attacks

Hybrid environments are a dominant trend in enterprise technology, but they continue to present unique issues to the defenders tasked with securing them. By 2026, Gartner predicts that 75% of organizations will adopt hybrid cloud strategies [1]. At the same time, only 23% of organizations report full visibility across cloud environments [2].

That means a strong majority of organizations do not have comprehensive visibility across both their on-premises and cloud networks. As a result, organizations are facing major challenges in achieving visibility and security in hybrid environments. These silos and fragmented security postures become a major problem when considering how attacks can move between different domains, exploiting the gaps.

For example, an attack may start with a phishing email, leading to the compromise of a cloud-based application identity and then moving between the cloud and network to exfiltrate data. Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.

Given this, unified visibility is essential for security teams to reduce blind spots and detect threats across the entire attack surface.

Risks of fragmented visibility

Silos arise due to separate teams and tools managing on-premises and cloud environments. Many teams have a hand in cloud security, with some common ones including security, infrastructure, DevOps, compliance, and end users, and these teams can all use different tools. This fragmentation increases the likelihood of inconsistent policies, duplicate alerts, and missed threats. And that’s just within the cloud, not even considering the additional defenses involved with network security.

Without a unified security strategy, gaps between these infrastructures and the teams which manage them can leave organizations vulnerable to cyber-attacks. The lack of visibility between on-premises and cloud environments contributes to missed threats and delayed incident response. In fact, breaches involving stolen or compromised credentials take an average of 292 to identify and contain [3]. That’s almost ten months.

The risk of fragmented visibility runs especially high as companies undergo cloud migrations. As organizations transition to cloud environments, they still have much of their data in on-premises networks, meaning that maintaining visibility across both on-premises and cloud environments is essential for securing critical assets and ensuring seamless operations.

Unified visibility is the solution

Unified visibility is achieved by having a single-pane-of-glass view to monitor both on-premises and cloud environments. This type of view brings many benefits, including streamlined detection, faster response times, and reduced complexity.

This can only be accomplished through integrations or interactions between the teams and tools involved with both on-premises security and cloud security.

AI-driven platforms, like Darktrace, are especially well equipped to enable the real-time monitoring and insights needed to sustain unified visibility. This is because they can handle the large amounts of data and data types.

Darktrace accomplishes this by plugging into an organization’s infrastructure so the AI can ingest and analyze data and its interactions within the environment to form an understanding of the organization’s normal behavior, right down to the granular details of specific users and devices. The system continually revises its understanding about what is normal based on evolving evidence.

This dynamic understanding of normal means that the AI engine can identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. This helps reduce noise while surfacing real threats, across cloud and on-prem environments without manual tuning.

In this way, given its versatile AI-based, platform approach, Darktrace empowers security teams with real-time monitoring and insights across both the network and cloud.

Unified visibility in the modern threat landscape

As part of the Darktrace ActiveAI Security Platform™, Darktrace / CLOUD works continuously across public, private, hybrid, and multi-cloud deployments. With real-time Cloud Asset Enumeration and Dynamic Architecture Modeling, Darktrace / CLOUD generates up-to-date architecture diagrams, giving SecOps and DevOps teams a unified view of cloud infrastructures.

It is always on the lookout for changes, driven by user and service activity. For example, unusual user activity can significantly raise the asset’s score, prompting Darktrace’s AI to update its architectural view and keep a living record of the cloud’s ever-changing landscape, providing near real-time insights into what’s happening.

This continuous architectural awareness ensures that security teams have a real-time understanding of cloud behavior and not just a static snapshot.

*Figure 1. Darktrace / CLOUD’s unified view of AWS and Azure cloud posture and compliance over time.*

With this dynamic cloud visibility and monitoring, Darktrace / CLOUD can help unify and secure environments.

Real world example: Remote access supply chain attacks

Sectop Remote Access Trojan (RAT) malware, also known as ‘ArchClient2,’ is a .NET RAT that contains information stealing capabilities and allows threat actors to monitor and control targeted computers. It is commonly distributed through drive-by downloads of illegitimate software via malvertizing.

Darktrace has been able to detect and respond to Sectop RAT attacks using unified visibility and platform-wide coverage. In one such example, Darktrace observed one device making various suspicious connections to unusual endpoints, likely in an attempt to receive C2 information, perform beaconing activity, and exfiltrate data to the cloud.

This type of supply chain attack can jump from the network to the cloud, so a unified view of both environments helps shorten detection and response times, therefore mitigating potential impact. Darktrace’s ability to detect these cross-domain behaviors stems from its AI-driven, platform-native visibility.

Conclusion

Organizations need unified visibility to secure complex, hybrid environments effectively against threats and attacks. To achieve this type of comprehensive visibility, the gaps between legacy security tools across on-premises and cloud networks can be bridged with platform tools that use AI to boost data analysis for highly accurate behavioral prediction and anomaly detection.

Read more about the latest trends in cloud security in the blog “Protecting Your Hybrid Cloud: The Future of Cloud Security in 2025 and Beyond.”

References:

1. Gartner, May 22, 2023, “10 Strategic Data and Analytics Predictions Through 2028”

2. Cloud Security Alliance, February 14, 2024, “Cloud Security Alliance Survey Finds 77% of Respondents Feel Unprepared to Deal with Security Threats”

3. IBM, “Cost of a Data Breach Report 2024”

About the author

Pallavi Singh

Product Marketing Manager, OT Security & Compliance

Blog

OT

June 11, 2025

Proactive OT security: Lessons on supply chain risk management from a rogue Raspberry Pi

Understanding supply chain risk in manufacturing

For industries running Industrial Control Systems (ICS) such as manufacturing and fast-moving consumer goods (FMCG), complex supply chains mean that disruption to one weak node can have serious impacts to the entire ecosystem. However, supply chain risk does not always originate from outside an organization’s ICS network.

The implicit trust placed on software or shared services for maintenance within an ICS can be considered a type of insider threat [1], where defenders also need to look ‘from within’ to protect against supply chain risk. Attackers have frequently mobilised this form of insider threat:

Many ICS and SCADA systems were compromised during the 2014 Havex Watering Hole attack, where via operators’ implicit trust in the trojanized versions of legitimate applications, on legitimate but compromised websites [2]. ‍
In 2018, the world’s largest manufacturer of semiconductors and processers shut down production for three days after a supplier installed tainted software that spread to over 10,000 machines in the manufacturer’s network [3].
During the 2020 SolarWinds supply chain attack, attackers compromised a version of Orion software that was deployed from SolarWinds’ own servers during a software update to thousands of customers, including tech manufacturing companies such as Intel and Nvidia [4].

Traditional approaches to ICS security have focused on defending against everything from outside the castle walls, or outside of the ICS network. As ICS attacks become more sophisticated, defenders must not solely rely on static perimeter defenses and prevention.

A critical part of active defense is understanding the ICS environment and how it operates, including all possible attack paths to the ICS including network connections, remote access points, the movement of data across zones and conduits and access from mobile devices. For instance, original equipment manufacturers (OEMs) and vendors often install remote access software or third-party equipment in ICS networks to facilitate legitimate maintenance and support activities, which can unintentionally expand the ICS’ attack surface.

This blog describes an example of the convergence between supply chain risk and insider risk, when a vendor left a Raspberry Pi device in a manufacturing customer’s ICS network without the customer’s knowledge.

Case study: Using unsupervised machine learning to detect pre-existing security issues

Raspberry Pi devices are commonly used in SCADA environments as low-cost, remotely accessible data collectors [5][6][7]. They are often paired with Industrial Internet of Things (IIoT) for monitoring and tracking [8]. However, these devices also represent a security risk because their small physical size and time-consuming nature of physical inspection makes them easy to overlook. This poses a security risk, as these devices have previously been used to carry out USB-based attacks or to emulate Ethernet-over-USB connections to exfiltrate sensitive data [8][9].

In this incident, a Darktrace customer was unaware that their supplier had installed a Raspberry Pi device on their ICS network. Crucially, the installation occurred prior to Darktrace’s deployment on the customer’s network.

For other anomaly detection tools, this order of events meant that this third-party device would likely have been treated as part of the customer’s existing infrastructure. However, after Darktrace was deployed, it analyzed the metadata from the encrypted HTTPS and DNS connections that the Raspberry Pi made to ‘call home’ to the supplier and determined that these connections were unusual compared to the rest of the devices in the network, even in the absence of any malicious indicators of compromise (IoCs).

Darktrace triggered the following alerts for this unusual activity that consequently notified the customer to the pre-existing threat of an unmanaged device already present in their network:

Compromise / Sustained SSL or HTTP Increase
Compromise / Agent Beacon (Short Period)
Compromise / Agent Beacon (Medium Period)
Compromise / Agent Beacon (Long Period)
Tags / New Raspberry Pi Device
Device / DNS Requests to Unusual Server
Device / Anomaly Indicators / Spike in Connections to Rare Endpoint Indicator

‍*Figure 1: Darktrace’s External Sites Summary showing the rarity of the external endpoint that the Raspberry Pi device ‘called home’ to and the model alerts triggered.*

Darktrace’s Cyber AI Analyst launched an autonomous investigation into the activity, correlating related events into a broader incident and generating a report outlining the potential threat along with supporting technical details.

Darktrace’s anomaly-based detection meant that the Raspberry Pi device did not need to be observed performing clearly malicious behavior to alert the customer to the security risk, and neither can defenders afford to wait for such escalation.

Why is this significant?

In 2021 a similar attack took place. Aiming to poison a Florida water treatment facility, attackers leveraged a TeamViewer instance that had been dormant on the system for six months, effectively allowing the attacker to ‘live off the land’ [10].

The Raspberry Pi device in this incident also remained outside the purview of the customer’s security team at first. It could have been leveraged by a persistent attacker to pivot within the internal network and communicate externally.

A proactive approach to active defense that seeks to minimize and continuously monitor the attack surface and network is crucial.

The growing interest in manufacturing from attackers and policymakers

Significant motivations for targeting the manufacturing sector and increasing regulatory demands make the convergence of supply chain risk, insider risk, and the prevalence of stealthy living-off-the-land techniques particularly relevant to this sector.

Manufacturing is consistently targeted by cybercriminals [11], and the sector’s ‘just-in-time’ model grants attackers the opportunity for high levels of disruption. Furthermore, under NIS 2, manufacturing and some food and beverage processing entities are now designated as ‘important’ entities. This means stricter incident reporting requirements within 24 hours of detection, and enhanced security requirements such as the implementation of zero trust and network segmentation policies, as well as measures to improve supply chain resilience [12][13][14].

How can Darktrace help?

Ultimately, Darktrace successfully assisted a manufacturing organization in detecting a potentially disruptive 'near-miss' within their OT environment, even in the absence of traditional IoCs. Through passive asset identification techniques and continuous network monitoring, the customer improved their understanding of their network and supply chain risk.

While the swift detection of the rogue device allowed the threat to be identified before it could escalate, the customer could have reduced their time to respond by using Darktrace’s built-in response capabilities, had Darktrace’s Autonomous Response capability been enabled. Darktrace’s Autonomous Response can be configured to target specific connections on a rogue device either automatically upon detection or following manual approval from the security team, to stop it communicating with other devices in the network while allowing other approved devices to continue operating. Furthermore, the exportable report generated by Cyber AI Analyst helps security teams to meet NIS 2’s enhanced reporting requirements.

Sophisticated ICS attacks often leverage insider access to perform in-depth reconnaissance for the development of tailored malware capabilities. This case study and high-profile ICS attacks highlight the importance of mitigating supply chain risk in a similar way to insider risk. As ICS networks adapt to the introduction of IIoT, remote working and the increased convergence between IT and OT, it is important to ensure the approach to secure against these threats is compatible with the dynamic nature of the network.

Credit to Nicole Wong (Principal Cyber Analyst), Matthew Redrup (Senior Analyst and ANZ Team Lead)

[related-resource]

Appendices

MITRE ATT&CK Mapping

Infrastructure / New Raspberry Pi Device - INITIAL ACCESS - T1200 Hardware Additions
Device / DNS Requests to Unusual Server - CREDENTIAL ACCESS, COLLECTION - T1557 Man-in-the-Middle
Compromise / Agent Beacon - COMMAND AND CONTROL - T1071.001 Web Protocols