Krypto-Mining-Malware: Aufdeckung einer Kryptowährungsfarm in einem Lagerhaus
07
Apr 2021
07
Apr 2021
Cyber AI discovered an extensive crypto-mining campaign in cardboard boxes in a disused warehouse. This blog discusses the rise in cryptocurrency farms and what this signals for the international cyber-threat landscape.
Cryptocurrencies are hitting the headlines every week and quickly becoming accepted as a mainstream investment and method of payment. Across the world, cyber-criminals are leveraging data centers called crypto-mining ‘farms’ to profit from this trend, from China to Iceland, Iran, and even a cardboard box in an empty warehouse.
How does cryptocurrency mining work?
Cryptocurrencies are decentralized digital currencies. Unlike traditional currencies, which can be issued at any time by central banks, cryptocurrency is not controlled by any centralized authority. Instead, it relies on a blockchain, which functions as a digital ledger of transactions, organized and maintained by a peer-to-peer network.
Miners create and secure cryptocurrency by solving cryptographic algorithms. Rather than hammers and chisels, crypto-miners use specialized computers with GPUs or ASICs to validate transactions as quickly as possible, earning cryptocurrency in the process.
Crypto-mining farms in 2021: Reaping the early harvest
Crypto-mining takes up an enormous amount of energy. An analysis by the University of Cambridge estimates that generating Bitcoin consumes as much, if not more, energy than entire countries. For instance, Bitcoin uses approximately 137.9 terawatt hours per year, compared to Ukraine, which uses only 128.8 in the same period. Bitcoin is just one of many cryptocurrencies, alongside Monero and Dogecoin, so the total energy consumed by all cryptocurrencies is far higher.
Given that high-powered mining computers require so much processing power, crypto-mining is lucrative in countries with relatively cheap electricity. However, the energy needed can lead to serious consequences – even shutting down entire cities. In Iran, the outdated energy grid has struggled to provide for cryptocurrency farms, resulting in city-wide blackouts.
While some of these crypto-farms are legal, illegal crypto-miners are also straining Iran’s energy supplies. Illegal crypto-mining is popular in Iran partly because Iranian currency is volatile and subject to inflation, whereas cryptocurrency is (for the moment) immune to both inflationary monetary policy and U.S. sanctions. When used for illegal purposes, cryptocurrency farming can lead to network outages and serious financial harm.
Crypto-mining malware in corporate networks
Crypto-mining malware has the ability to hamper and even crash an organization’s digital environment, if unstopped. Cyber AI has discovered and thwarted hundreds of attacks where devices are infected with crypto-mining malware, including:
a server in charge of opening and closing a biometric door;
a spectrometer, a medical IoT device which uses wavelengths of light to analyze materials;
12 servers hidden under the floorboards of an Italian bank.
In one instance last year, Darktrace detected anomalous crypto-mining activity on a corporate system. Upon investigation, the organization in question traced the anomalous activity to one of their warehouses, where they found what appeared to be unassuming cardboard boxes sitting on a shelf. Opening these boxes revealed a cryptocurrency farm in disguise, running off the company’s network power.
Figure 1: The unassuming cardboard boxes
Figure 2: The cryptocurrency farm
Figure 3: The threat actors created a stealthy cryptocurrency mining rig with GPUs, running off the company’s network power
Had it remained undiscovered, the crypto-mining farm would have led to financial losses for the client and disruption to business workings. Mining rigs also generate a lot of heat and could have easily caused a fire in the warehouse.
This case demonstrates the covert methods opportunistic individuals may take to hijack corporate infrastructure with crypto-mining malware, as well as the need for a security tool which covers the entire digital estate and detects any new or unusual events. Darktrace’s machine learning flagged the connections being made from the warehouse boxes as highly anomalous, leading to this unexpected discovery.
In organizations with Darktrace RESPOND active, any anomalous crypto-mining devices would be blocked from communicating with the relevant external endpoints, effectively inhibiting mining activity. RESPOND can also enforce the normal ‘pattern of life’ across the digital environment, preventing malicious behavior while allowing normal business activities to continue. As bad actors continue to proliferate and hackers devise new ways to deploy crypto-mining malware, Darktrace’s full visibility and Autonomous Response in every part of the digital environment is more important than ever.
Thanks to Darktrace analyst Chloe Phillips for her insights.
Darktrace Cyber-Analysten sind erstklassige Experten für Threat Intelligence, Threat Hunting und Incident Response. Sie bieten Tausenden von Darktrace Kunden auf der ganzen Welt rund um die Uhr SOC-Support. Einblicke in das SOC-Team wird ausschließlich von diesen Experten verfasst und bietet Analysen von Cyber-Vorfällen und Bedrohungstrends, die auf praktischen Erfahrungen in diesem Bereich basieren.
AUTOR
ÜBER DEN AUTOR
Justin Fier
SVP, Red Team Operations
Justin is one of the US’s leading cyber intelligence experts, and holds the position of SVP, Red Team Operations at Darktrace. His insights on cyber security and artificial intelligence have been widely reported in leading media outlets, including the Wall Street Journal, CNN, The Washington Post, and VICELAND. With over 10 years’ experience in cyber defense, Justin has supported various elements in the US intelligence community, holding mission-critical security roles with Lockheed Martin, Northrop Grumman Mission Systems and Abraxas. Justin is also a highly-skilled technical specialist, and works with Darktrace’s strategic global customers on threat analysis, defensive cyber operations, protecting IoT, and machine learning.
A Thorn in Attackers’ Sides: How Darktrace Uncovered a CACTUS Ransomware Infection
24
Apr 2024
What is CACTUS Ransomware?
In May 2023, Kroll Cyber Threat Intelligence Analysts identified CACTUS as a new ransomware strain that had been actively targeting large commercial organizations since March 2023 [1]. CACTUS ransomware gets its name from the filename of the ransom note, “cAcTuS.readme.txt”. Encrypted files are appended with the extension “.cts”, followed by a number which varies between attacks, e.g. “.cts1” and “.cts2”.
As the cyber threat landscape adapts to ever-present fast-paced technological change, ransomware affiliates are employing progressively sophisticated techniques to enter networks, evade detection and achieve their nefarious goals.
How does CACTUS Ransomware work?
In the case of CACTUS, threat actors have been seen gaining initial network access by exploiting Virtual Private Network (VPN) services. Once inside the network, they may conduct internal scanning using tools like SoftPerfect Network Scanner, and PowerShell commands to enumerate endpoints, identify user accounts, and ping remote endpoints. Persistence is maintained by the deployment of various remote access methods, including legitimate remote access tools like Splashtop, AnyDesk, and SuperOps RMM in order to evade detection, along with malicious tools like Cobalt Strike and Chisel. Such tools, as well as custom scripts like TotalExec, have been used to disable security software to distribute the ransomware binary. CACTUS ransomware is unique in that it adopts a double-extortion tactic, stealing data from target networks and then encrypting it on compromised systems [2].
At the end of November 2023, cybersecurity firm Arctic Wolf reported instances of CACTUS attacks exploiting vulnerabilities on the Windows version of the business analytics platform Qlik, specifically CVE-2023-41266, CVE-2023-41265, and CVE-2023-48365, to gain initial access to target networks [3]. The vulnerability tracked as CVE-2023-41266 can be exploited to generate anonymous sessions and perform HTTP requests to unauthorized endpoints, whilst CVE-2023-41265 does not require authentication and can be leveraged to elevate privileges and execute HTTP requests on the backend server that hosts the application [2].
Darktrace’s Coverage of CACTUS Ransomware
In November 2023, Darktrace observed malicious actors leveraging the aforementioned method of exploiting Qlik to gain access to the network of a customer in the US, more than a week before the vulnerability was reported by external researchers.
Here, Qlik vulnerabilities were successfully exploited, and a malicious executable (.exe) was detonated on the network, which was followed by network scanning and failed Kerberos login attempts. The attack culminated in the encryption of numerous files with extensions such as “.cts1”, and SMB writes of the ransom note “cAcTuS.readme.txt” to multiple internal devices, all of which was promptly identified by Darktrace DETECT™.
While traditional rules and signature-based detection tools may struggle to identify the malicious use of a legitimate business platform like Qlik, Darktrace’s Self-Learning AI was able to confidently identify anomalous use of the tool in a CACTUS ransomware attack by examining the rarity of the offending device’s surrounding activity and comparing it to the learned behavior of the device and its peers.
Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled in autonomous response mode during their encounter with CACTUS ransomware meaning that attackers were able to successfully escalate their attack to the point of ransomware detonation and file encryption. Had RESPOND been configured to autonomously act on any unusual activity, Darktrace could have prevented the attack from progressing, stopping the download of any harmful files, or the encryption of legitimate ones.
Cactus Ransomware Attack Overview
Holiday periods have increasingly become one of the favoured times for malicious actors to launch their attacks, as they can take advantage of the festive downtime of organizations and their security teams, and the typically more relaxed mindset of employees during this period [4].
Following this trend, in late November 2023, Darktrace began detecting anomalous connections on the network of a customer in the US, which presented multiple indicators of compromise (IoCs) and tactics, techniques and procedures (TTPs) associated with CACTUS ransomware. The threat actors in this case set their attack in motion by exploiting the Qlik vulnerabilities on one of the customer’s critical servers.
Darktrace observed the server device making beaconing connections to the endpoint “zohoservice[.]net” (IP address: 45.61.147.176) over the course of three days. This endpoint is known to host a malicious payload, namely a .zip file containing the command line connection tool PuttyLink [5].
Darktrace’s Cyber AI Analyst was able to autonomously identify over 1,000 beaconing connections taking place on the customer’s network and group them together, in this case joining the dots in an ongoing ransomware attack. AI Analyst recognized that these repeated connections to highly suspicious locations were indicative of malicious command-and-control (C2) activity.
Figure 1: Cyber AI Analyst Incident Log showing the offending device making over 1,000 connections to the suspicious hostname “zohoservice[.]net” over port 8383, within a specific period.
The infected device was then observed downloading the file “putty.zip” over a HTTP connection using a PowerShell user agent. Despite being labelled as a .zip file, Darktrace’s detection capabilities were able to identify this as a masqueraded PuttyLink executable file. This activity resulted in multiple Darktrace DETECT models being triggered. These models are designed to look for suspicious file downloads from endpoints not usually visited by devices on the network, and files whose types are masqueraded, as well as the anomalous use of PowerShell. This behavior resembled previously observed activity with regards to the exploitation of Qlik Sense as an intrusion technique prior to the deployment of CACTUS ransomware [5].
Figure 2: The downloaded file’s URI highlighting that the file type (.exe) does not match the file's extension (.zip). Information about the observed PowerShell user agent is also featured.
Following the download of the masqueraded file, Darktrace observed the initial infected device engaging in unusual network scanning activity over the SMB, RDP and LDAP protocols. During this activity, the credential, “service_qlik” was observed, further indicating that Qlik was exploited by threat actors attempting to evade detection. Connections to other internal devices were made as part of this scanning activity as the attackers attempted to move laterally across the network.
Figure 3: Numerous failed connections from the affected server to multiple other internal devices over port 445, indicating SMB scanning activity.
The compromised server was then seen initiating multiple sessions over the RDP protocol to another device on the customer’s network, namely an internal DNS server. External researchers had previously observed this technique in CACTUS ransomware attacks where an RDP tunnel was established via Plink [5].
A few days later, on November 24, Darktrace identified over 20,000 failed Kerberos authentication attempts for the username “service_qlik” being made to the internal DNS server, clearly representing a brute-force login attack. There is currently a lack of open-source intelligence (OSINT) material definitively listing Kerberos login failures as part of a CACTUS ransomware attack that exploits the Qlik vulnerabilities. This highlights Darktrace’s ability to identify ongoing threats amongst unusual network activity without relying on existing threat intelligence, emphasizing its advantage over traditional security detection tools.
Figure 4: Kerberos login failures being carried out by the initial infected device. The destination device detected was an internal DNS server.
In the month following these failed Kerberos login attempts, between November 26 and December 22, Darktrace observed multiple internal devices encrypting files within the customer’s environment with the extensions “.cts1” and “.cts7”. Devices were also seen writing ransom notes with the file name “cAcTuS.readme.txt” to two additional internal devices, as well as files likely associated with Qlik, such as “QlikSense.pdf”. This activity detected by Darktrace confirmed the presence of a CACTUS ransomware infection that was spreading across the customer’s network.
Figure 5: The model, 'Ransom or Offensive Words Written to SMB', triggered in response to SMB file writes of the ransom note, ‘cAcTuS.readme.txt’, that was observed on the customer’s network.
Figure 6: CACTUS ransomware extensions, “.cts1” and “.cts7”, being appended to files on the customer’s network.
Following this initial encryption activity, two affected devices were observed attempting to remove evidence of this activity by deleting the encrypted files.
Figure 7: Attackers attempting to remove evidence of their activity by deleting files with appendage “.cts1”.
Schlussfolgerung
In the face of this CACTUS ransomware attack, Darktrace’s anomaly-based approach to threat detection enabled it to quickly identify multiple stages of the cyber kill chain occurring in the customer’s environment. These stages ranged from ‘initial access’ by exploiting Qlik vulnerabilities, which Darktrace was able to detect before the method had been reported by external researchers, to ‘actions on objectives’ by encrypting files. Darktrace’s Self-Learning AI was also able to detect a previously unreported stage of the attack: multiple Kerberos brute force login attempts.
If Darktrace’s autonomous response capability, RESPOND, had been active and enabled in autonomous response mode at the time of this attack, it would have been able to take swift mitigative action to shut down such suspicious activity as soon as it was identified by DETECT, effectively containing the ransomware attack at the earliest possible stage.
Learning a network’s ‘normal’ to identify deviations from established patterns of behaviour enables Darktrace’s identify a potential compromise, even one that uses common and often legitimately used administrative tools. This allows Darktrace to stay one step ahead of the increasingly sophisticated TTPs used by ransomware actors.
Credit to Tiana Kelly, Cyber Analyst & Analyst Team Lead, Anna Gilbertson, Cyber Analyst
The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024
22
Apr 2024
About the AI Cybersecurity Report
We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.
Are organizations feeling the impact of AI-powered cyber threats?
Nearly three-quarters (74%) state AI-powered threats are now a significant issue. Almost nine in ten (89%) agree that AI-powered threats will remain a major challenge into the foreseeable future, not just for the next one to two years.
However, only a slight majority (56%) thought AI-powered threats were a separate issue from traditional/non AI-powered threats. This could be the case because there are few, if any, reliable methods to determine whether an attack is AI-powered.
Identifying exactly when and where AI is being applied may not ever be possible. However, it is possible for AI to affect every stage of the attack lifecycle. As such, defenders will likely need to focus on preparing for a world where threats are unique and are coming faster than ever before.
Are security stakeholders concerned about AI’s impact on cyber threats and risks?
The results from our survey showed that security practitioners are concerned that AI will impact organizations in a variety of ways. There was equal concern associated across the board – from volume and sophistication of malware to internal risks like leakage of proprietary information from employees using generative AI tools.
What this tells us is that defenders need to prepare for a greater volume of sophisticated attacks and balance this with a focus on cyber hygiene to manage internal risks.
One example of a growing internal risks is shadow AI. It takes little effort for employees to adopt publicly-available text-based generative AI systems to increase their productivity. This opens the door to “shadow AI”, which is the use of popular AI tools without organizational approval or oversight. Resulting security risks such as inadvertent exposure of sensitive information or intellectual property are an ever-growing concern.
Are organizations taking strides to reduce risks associated with adoption of AI in their application and computing environment?
71.2% of survey participants say their organization has taken steps specifically to reduce the risk of using AI within its application and computing environment.
16.3% of survey participants claim their organization has not taken these steps.
These findings are good news. Even as enterprises compete to get as much value from AI as they can, as quickly as possible, they’re tempering their eager embrace of new tools with sensible caution.
Still, responses varied across roles. Security analysts, operators, administrators, and incident responders are less likely to have said their organizations had taken AI risk mitigation steps than respondents in other roles. In fact, 79% of executives said steps had been taken, and only 54% of respondents in hands-on roles agreed. It seems that leaders believe their organizations are taking the needed steps, but practitioners are seeing a gap.
Do security professionals feel confident in their preparedness for the next generation of threats?
A majority of respondents (six out of every ten) believe their organizations are inadequately prepared to face the next generation of AI-powered threats.
The survey findings reveal contrasting perceptions of organizational preparedness for cybersecurity threats across different regions and job roles. Security administrators, due to their hands-on experience, express the highest level of skepticism, with 72% feeling their organizations are inadequately prepared. Notably, respondents in mid-sized organizations feel the least prepared, while those in the largest companies feel the most prepared.
Regionally, participants in Asia-Pacific are most likely to believe their organizations are unprepared, while those in Latin America feel the most prepared. This aligns with the observation that Asia-Pacific has been the most impacted region by cybersecurity threats in recent years, according to the IBM X-Force Threat Intelligence Index.
The optimism among Latin American respondents could be attributed to lower threat volumes experienced in the region, but it's cautioned that this could change suddenly (1).
What are biggest barriers to defending against AI-powered threats?
The top-ranked inhibitors center on knowledge and personnel. However, issues are alluded to almost equally across the board including concerns around budget, tool integration, lack of attention to AI-powered threats, and poor cyber hygiene.
The cybersecurity industry is facing a significant shortage of skilled professionals, with a global deficit of approximately 4 million experts (2). As organizations struggle to manage their security tools and alerts, the challenge intensifies with the increasing adoption of AI by attackers. This shift has altered the demands on security teams, requiring practitioners to possess broad and deep knowledge across rapidly evolving solution stacks.
Educating end users about AI-driven defenses becomes paramount as organizations grapple with the shortage of professionals proficient in managing AI-powered security tools. Operationalizing machine learning models for effectiveness and accuracy emerges as a crucial skill set in high demand. However, our survey highlights a concerning lack of understanding among cybersecurity professionals regarding AI-driven threats and the use of AI-driven countermeasures indicating a gap in keeping pace with evolving attacker tactics.
The integration of security solutions remains a notable problem, hindering effective defense strategies. While budget constraints are not a primary inhibitor, organizations must prioritize addressing these challenges to bolster their cybersecurity posture. It's imperative for stakeholders to recognize the importance of investing in skilled professionals and integrated security solutions to mitigate emerging threats effectively.
1. IBM, X-Force Threat Intelligence Index 2024, Available at: https://www.ibm.com/downloads/cas/L0GKXDWJ
2. ISC2, Cybersecurity Workforce Study 2023, Available at: https://media.isc2.org/-/media/Project/ISC2/Main/Media/ documents/research/ISC2_Cybersecurity_Workforce_Study_2023.pdf?rev=28b46de71ce24e6ab7705f6e3da8637e
![Cyber AI Analyst Incident Log showing the offending device making over 1,000 connections to the suspicious hostname “zohoservice[.]net” over port 8383, within a specific period.](https://assets-global.website-files.com/626ff4d25aca2edf4325ff97/662971c1cf09890fd46729a1_Screenshot%202024-04-24%20at%201.55.10%20PM.png)
