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Catching CoinLoader: Decrypting the Malware Hijacking Networks for Cryptomining Operations

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08
Feb 2024
08
Feb 2024
This blog explores a series of CoinLoader compromises observed by Darktrace in late 2023. CoinLoader is a loader malware known to carry out cryptocurrency mining on infected devices. Darktrace’s autonomous detection and response capabilities allowed it to identify and shut down compromises in the first instance.

About Loader Malware

Loader malware was a frequent topic of conversation and investigation within the Darktrace Threat Research team throughout 2023, with a wide range of existing and novel variants affecting a significant number of Darktrace customers, as detailed in Darktrace’s inaugural End of Year Threat Report. The multi-phase nature of such compromises poses a significant threat to organizations due to the need to defend against multiple threats at the same time.

CoinLoader, a variant of loader malware first observed in the wild in 2018 [1], is an example of one of the more prominent variant of loaders observed by Darktrace in 2023, with over 65 customers affected by the malware. Darktrace’s Threat Research team conducted a deep dive investigation into the patterns of behavior exhibited by devices infected with CoinLoader in the latter part of 2023, with compromises observed in Europe, the Middle East and Africa (EMEA), Asia-Pacific (APAC) and the Americas.

The autonomous threat detection capabilities of Darktrace DETECT™ allowed for the effective identification of these CoinLoader infections whilst Darktrace RESPOND™, if active, was able to quickly curtail attacker’s efforts and prevent more disruptive, and potentially costly, secondary compromises from occurring.

What is CoinLoader?

Much like other strains of loader, CoinLoader typically serves as a first stage malware that allows threat actors to gain initial access to a network and establish a foothold in the environment before delivering subsequent malicious payloads, including adware, botnets, trojans or pay-per-install campaigns.

CoinLoader is generally propagated through trojanized popular software or game installation archive files, usually in the rar or zip formats. These files tend can be easily obtained via top results displayed in search engines when searching for such keywords as "crack" or "keygen" in conjunction with the name of the software the user wishes to pirate [1,2,3,4]. By disguising the payload as a legitimate programme, CoinLoader is more likely to be unknowingly downloaded by endpoint users, whilst also bypassing traditional security measures that trust the download.

It also has several additional counter-detection methods including using junk code, variable obfuscation, and encryption for shellcode and URL schemes. It relies on dynamic-link library (DLL) search order hijacking to load malicious DLLs to legitimate executable files. The malware is also capable of performing a variety of checks for anti-virus processes and disabling endpoint protection solutions.

In addition to these counter-detection tactics, CoinLoader is also able to prevent the execution of its malicious DLL files in sandboxed environments without the presence of specific DNS cache records, making it extremely difficult for security teams and researchers to analyze.

In 2020 it was reported that CoinLoader compromises were regularly seen alongside cryptomining activity and even used the alias “CoinMiner” in some cases [2]. Darktrace’s investigations into CoinLoader in 2023 largely confirmed this theory, with around 15% of observed CoinLoader connections being related to cryptomining activity.

Cryptomining malware consumes large amounts of a hijacked (or cryptojacked) device's resources to perform complex mathematical calculations and generate income for the attacker all while quietly working in the background. Cryptojacking can lead to high electricity costs, device slow down, loss of functionality, and in the worst case scenario can be a potential fire hazard.

Darktrace Coverage of CoinLoader

In September 2023, Darktrace observed several cases of CoinLoader that served to exemplify the command-and-control (C2) communication and subsequent cryptocurrency mining activities typically observed during CoinLoader compromises. While the initial infection method in these cases was outside of Darktrace’s purview, it likely occurred via socially engineered phishing emails or, as discussed earlier, trojanized software downloads.

Command-and-Control Activity

CoinLoader compromises observed across the Darktrace customer base were typically identified by encrypted C2 connections over port 433 to rare external endpoints using self-signed certificates containing "OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US" in their issue fields.

All observed CoinLoader C2 servers were associated with the ASN of MivoCloud, a Virtual Private Server (VPS) hosting service (AS39798 MivoCloud SRL). It had been reported that Russian-state sponsored threat actors had previously abused MivoCloud’s infrastructure in order to bypass geo-blocking measures during phishing attacks against western nations [5].

Darktrace observed that the majority of CoinLoader infrastructure utilized IP addresses in the 185.225.0.0/19 range and were associated with servers hosted in Romania, with just one instance of an IP address based in Moldova. The domain names of these servers typically followed the naming pattern ‘*[a-d]{1}[.]info’, with 'ams-updatea[.]info’, ‘ams-updateb[.]info’, ‘ams-updatec[.]info’, and ‘ams-updated[.]info’ routinely identified on affected networks.

Researchers found that CoinLoader typically uses DNS tunnelling in order to covertly exchange information with attacker-controlled infrastructure, including the domains ‘candatamsnsdn[.]info’, ‘mapdatamsnsdn[.]info’, ‘rqmetrixsdn[.]info’ [4].

While Darktrace did not observe these particular domains, it did observer similar DNS lookups to a similar suspicous domain, namely ‘ucmetrixsdn[.]info’, in addition to the aforementioned HTTPS C2 connections.

Cryptomining Activity and Possible Additional Tooling

After establishing communication channels with CoinLoader servers, affected devices were observed carrying out a range of cryptocurrency mining activities. Darktrace detected devices connecting to multiple MivoCloud associated IP addresses using the MinerGate protocol alongside the credential “x”, a MinerGate credential observed by Darktrace in previous cryptojacking compromises, including the Sysrv-hello botnet.

Figure 1: Darktrace DETECT breach log showing an alerted mining activity model breach on an infected device.
Figure 2: Darktrace's Cyber AI Analyst providing details about unusual repeated connections to multiple endpoints related to CoinLoader cryptomining.

In a number of customer environments, Darktrace observed affected devices connected to endpoints associated with other malware such as the Andromeda botnet and the ViperSoftX information stealer. It was, however, not possible to confirm whether CoinLoader had dropped these additional malware variants onto infected devices.

On customer networks where Darktrace RESPOND was enabled in autonomous response mode, Darktrace was able to take swift targeted steps to shut down suspicious connections and contain CoinLoader compromises. In one example, following DETECT’s initial identification of an affected device connecting to multiple MivoCloud endpoints, RESPOND autonomously blocked the device from carrying out such connections, effectively shutting down C2 communication and preventing threat actors carrying out any cryptomining activity, or downloading subsequent malicious payloads. The autonomous response capability of RESPOND provides customer security teams with precious time to remove infected devices from their network and action their remediation strategies.

Figure 3: Darktrace RESPOND autonomously blocking CoinLoader connections on an affected device.

Additionally, customers subscribed to Darktrace’s Proactive Threat Notification (PTN) service would be alerted about potential CoinLoader activity observed on their network, prompting Darktrace’s Security Operations Center (SOC) to triage and investigate the activity, allowing customers to prioritize incidents that require immediate attention.

Conclusion

By masquerading as free or ‘cracked’ versions of legitimate popular software, loader malware like CoinLoader is able to indiscriminately target a large number of endpoint users without arousing suspicion. What’s more, once a network has been compromised by the loader, it is then left open to a secondary compromise in the form of potentially costly information stealers, ransomware or, in this case, cryptocurrency miners.

While urging employees to think twice before installing seemingly legitimate software unknown or untrusted locations is an essential first step in protecting an organization against threats like CoinLoader, its stealthy tactics mean this may not be enough.

In order to fully safeguard against such increasingly widespread yet evasive threats, organizations must adopt security solutions that are able to identify anomalies and subtle deviations in device behavior that could indicate an emerging compromise. The Darktrace suite of products, including DETECT and RESPOND, are well-placed to identify and contain these threats in the first instance and ensure they cannot escalate to more damaging network compromises.

Credit to: Signe Zaharka, Senior Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendix

Darktrace DETECT Model Detections

  • Anomalous Connection/Multiple Connections to New External TCP Port
  • Anomalous Connection/Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection/Rare External SSL Self-Signed
  • Anomalous Connection/Repeated Rare External SSL Self-Signed
  • Anomalous Connection/Suspicious Self-Signed SSL
  • Anomalous Connection/Young or Invalid Certificate SSL Connections to Rare
  • Anomalous Server Activity/Rare External from Server
  • Compromise/Agent Beacon (Long Period)
  • Compromise/Beacon for 4 Days
  • Compromise/Beacon to Young Endpoint
  • Compromise/Beaconing Activity To External Rare
  • Compromise/High Priority Crypto Currency Mining
  • Compromise/High Volume of Connections with Beacon Score
  • Compromise/Large Number of Suspicious Failed Connections
  • Compromise/New or Repeated to Unusual SSL Port
  • Compromise/Rare Domain Pointing to Internal IP
  • Compromise/Repeating Connections Over 4 Days
  • Compromise/Slow Beaconing Activity To External Rare
  • Compromise/SSL Beaconing to Rare Destination
  • Compromise/Suspicious File and C2
  • Compromise/Suspicious TLS Beaconing To Rare External
  • Device/ Anomalous Github Download
  • Device/ Suspicious Domain
  • Device/Internet Facing Device with High Priority Alert
  • Device/New Failed External Connections

Indicators of Compromise (IoCs)

IoC - Hostname C2 Server

ams-updatea[.]info

ams-updateb[.]info

ams-updatec[.]info

ams-updated[.]info

candatamsna[.]info

candatamsnb[.]info

candatamsnc[.]info

candatamsnd[.]info

mapdatamsna[.]info

mapdatamsnb[.]info

mapdatamsnc[.]info

mapdatamsnd[.]info

res-smarta[.]info

res-smartb[.]info

res-smartc[.]info

res-smartd[.]info

rqmetrixa[.]info

rqmetrixb[.]info

rqmetrixc[.]info

rqmetrixd[.]info

ucmetrixa[.]info

ucmetrixb[.]info

ucmetrixc[.]info

ucmetrixd[.]info

any-updatea[.]icu

IoC - IP Address - C2 Server

185.225[.]16.192

185.225[.]16.61

185.225[.]16.62

185.225[.]16.63

185.225[.]16.88

185.225[.]17.108

185.225[.]17.109

185.225[.]17.12

185.225[.]17.13

185.225[.]17.135

185.225[.]17.14

185.225[.]17.145

185.225[.]17.157

185.225[.]17.159

185.225[.]18.141

185.225[.]18.142

185.225[.]18.143

185.225[.]19.218

185.225[.]19.51

194.180[.]157.179

194.180[.]157.185

194.180[.]158.55

194.180[.]158.56

194.180[.]158.62

194.180[.]158.63

5.252.178[.]74

94.158.246[.]124

IoC - IP Address - Cryptocurrency mining related endpoint

185.225.17[.]114

185.225.17[.]118

185.225.17[.]130

185.225.17[.]131

185.225.17[.]132

185.225.17[.]142

IoC - SSL/TLS certificate issuer information - C2 server certificate example

emailAddress=admin@example[.]ltd,CN=example[.]ltd,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

emailAddress=admin@'res-smartd[.]info,CN=res-smartd[.]info,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

CN=ucmetrixd[.]info,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

MITRE ATT&CK Mapping

INITIAL ACCESS

Exploit Public-Facing Application - T1190

Spearphishing Link - T1566.002

Drive-by Compromise - T1189

COMMAND AND CONTROL

Non-Application Layer Protocol - T1095

Non-Standard Port - T1571

External Proxy - T1090.002

Encrypted Channel - T1573

Web Protocols - T1071.001

Application Layer Protocol - T1071

DNS - T1071.004

Fallback Channels - T1008

Multi-Stage Channels - T1104

PERSISTENCE

Browser Extensions

T1176

RESOURCE DEVELOPMENT

Web Services - T1583.006

Malware - T1588.001

COLLECTION

Man in the Browser - T1185

IMPACT

Resource Hijacking - T1496

References

1. https://www.avira.com/en/blog/coinloader-a-sophisticated-malware-loader-campaign

2. https://asec.ahnlab.com/en/17909/

3. https://www.cybereason.co.jp/blog/cyberattack/5687/

4. https://research.checkpoint.com/2023/tunnel-warfare-exposing-dns-tunneling-campaigns-using-generative-models-coinloader-case-study/

5. https://securityboulevard.com/2023/02/three-cases-of-cyber-attacks-on-the-security-service-of-ukraine-and-nato-allies-likely-by-russian-state-sponsored-gamaredon/

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.
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ABOUT ThE AUTHOR
Signe Zaharka
Senior Cyber Security Analyst
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Inside the SOC

The Price of Admission: Countering Stolen Credentials with Darktrace

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03
Jun 2024

Using leaked credentials to gain unauthorized access

Dark web marketplaces selling sensitive data have increased accessibility for malicious actors, similar to Ransomware-as-a-Service (RaaS), lowering the barrier to entry usually associated with malicious activity. By utilizing leaked credentials, malicious actors can easily gain unauthorized access to accounts and systems which they can leverage to carry out malicious activities like data exfiltration or malware deployment.

Usage of leaked credentials by malicious actors is a persistent concern for both organizations and security providers. Google Cloud’s ‘H1 2024 Threat Horizons Report’ details that initial access seen in 2.9% of cloud compromises observed on Google Cloud resulted from leaked credential usage [1], with the ‘IBM X-Force Threat Intelligence Index 2024’ reporting 71% year-on-year increase in cyber-attacks which utilize stolen or compromised credentials [2].

Darktrace coverage of leaked credentials

In early 2024, one Darktrace customer was compromised by a malicious actor after their internal credentials had been leaked on the dark web. Subsequent attack phases were detected by Darktrace/Network and the customer was alerted to the suspicious activity via the Proactive Threat Notification (PTN) service, following an investigation by Darktrace’s Security Operation Center (SOC).

Darktrace detected a device on the network of a customer in the US carrying out a string of anomalous activity indicative of network compromise. The device was observed using a new service account to authenticate to a Virtual Private Network (VPN) server, before proceeding to perform a range of suspicious activity including internal reconnaissance and lateral movement.

Malicious actors seemingly gained access to a previously unused service account for which they were able to set up multi-factor authentication (MFA) to access the VPN. As this MFA setup was made possible by the configuration of the customer’s managed service provider (MSP), the initial access phase of the attack fell outside of Darktrace’s purview.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled on the network at the time of the attack. Had RESPOND been active, it would have been able to autonomously act against the malicious activity by disabling users, strategically blocking suspicious connections and limiting devices to their expected patterns of activity.

Attack timeline of leaked credentials spotted by darktrace

Network Scanning Activity

On February 22, 2024, Darktrace detected the affected device performing activity indicative of network scanning, namely initiating connections on multiple ports, including ports 80, 161 389 and 445, to other internal devices. While many of these internal connection attempts were unsuccessful, some successful connections were observed.

Devices on a network can gather information about other internal devices by performing network scanning activity. Defensive scanning can be used to support network security, allowing internal security teams to discover vulnerabilities and potential entry points that require their attention, however attackers are also able to take advantage of such information, such as open ports and services available on internal devices, with offensive scanning.

Brute Force Login Attempts

Darktrace proceeded to identify the malicious actor attempting to access a previously unused service account for which they were able to successfully establish MFA to access the organization’s VPN. As the customer’s third-party MSP had been configured to allow all users to login to the organization’s VPN using MFA, this login was successful. Moreover, the service account had never previously been used and MFA and never been established, allowing the attacker to leverage it for their own nefarious means.

Darktrace/Network identified the attacker attempting to authenticate over the Kerberos protocol using a total of 30 different usernames, of which two were observed successfully authenticating. There was a total of 6 successful Kerberos logins identified from two different credentials.  Darktrace also observed over 100 successful NTLM attempts from the same device for multiple usernames including “Administrator” and “mail”. These credentials were later confirmed by the customer to have been stolen and leaked on the dark web.

Advanced Search query results showing the usernames that successfully authenticated via NTLM.
Figure 1: Advanced Search query results showing the usernames that successfully authenticated via NTLM.

Even though MFA requirements had been satisfied when the threat actor accessed the organization’s VPN, Darktrace recognized that this activity represented a deviation from its previously learned behavior.

Malicious actors frequently attempt to gain unauthorized access to accounts and internal systems by performing login attempts using multiple possible usernames and passwords. This type of brute-force activity is typically accomplished using computational power via the use of software or scripts to attempt different username/password combinations until one is successful.

By purchasing stolen credentials from dark web marketplaces, attackers are able to significantly increase the success rate of brute-force attacks and, if they do gain access, they can easily act on their objectives, be that exfiltrating sensitive data or moving through their target networks to further the compromise.

Share Enumeration

Around 30 minutes after the initial network scanning activity, the compromised device was observed performing SMB enumeration using one of the aforementioned accounts. Darktrace understood that this activity was suspicious as the device had never previously been used to perform SMB activity and had not been tagged as a security device.

Darktrace/Network identifying the suspicious SMB enumeration performed by the compromised device.
Figure 2: Darktrace/Network identifying the suspicious SMB enumeration performed by the compromised device.

Such enumeration can be used by malicious actors to gain insights into the structures and configurations of a target device, view permissions associated with shared resources, and also view general identifying information about the system.

Darktrace further identified that the device connected to the named pipe “srvsvc”. By enumerating over srvsvc, a threat actor is able to request a list of all available SMB shares on a destination device, enabling further data gathering as part of network reconnaissance. Srvsvc also provides access to remote procedure call (RPC) for various services on a destination device.

At this stage, a Darktrace/Network Enhanced Monitoring model was triggered for lateral movement activity taking place on the customer’s network. As this particular customer was subscribed to the PTN service, the Enhanced Monitoring model alert was promptly triaged and investigated by the Darktrace SOC. The customer was alerted to the emerging activity and given full details of the incident and the SOC team’s investigation.

Attack and Reconnaissance Tool Usage

A few minutes later, Darktrace observed the device making a connection with a user agent associated with the Nmap network scanning tool, “Mozilla/5.0 (compatible; Nmap Scripting Engine; https://nmap.org/book/nse[.]html)”. While these tools are often used legitimately by an organization’s security team, they can also be used maliciously by attackers to exploit vulnerabilities that attackers may have unearthed during earlier reconnaissance activity.

As such services are often seen as normal network traffic, attackers can often use them to bypass traditional security measures. Darktrace’s Self-Learning AI, however, was able to recognize that the affected device was not a security device and therefore not expected to carry out such activity, even if it was using a legitimate Nmap service.

Darktrace/Network identifying the compromised device using the Nmap scanning tool.
Figure 3: Darktrace/Network identifying the compromised device using the Nmap scanning tool.

Further Lateral Movement

Following this suspicious Nmap usage, Darktrace observed a range of additional anomalous SMB activity from the aforementioned compromised account. The affected device attempted to establish almost 900 SMB sessions, as well as performing 65 unusual file reads from 29 different internal devices and over 300 file deletes for the file “delete.me” from over 100 devices using multiple paths, including ADMIN$, C$, print$.

Darktrace also observed the device making several DCE-RPC connections associated with Active Directory Domain enumeration, including DRSCrackNames and DRSGetNCChanges; a total of more than 1000 successful DCE-RPC connection were observed to a domain controller.

As this customer did not have Darktrace/Network's autonomous response deployed on their network, the above detailed lateral movement and network reconnaissance activity was allowed to progress unfettered, until Darktrace’s SOC alerted the customer’s security team to take urgent action. The customer also received follow-up support through Darktrace’s Ask the Expert (ATE) service, allowing them to contact the analyst team directly for further details and support on the incident.

Thanks to this early detection, the customer was able to quickly identify and disable affected user accounts, effectively halting the attack and preventing further escalation.

Conclusions

Given the increasing trend of ransomware attackers exfiltrating sensitive data for double extortion and the rise of information stealers, stolen credentials are commonplace across dark web marketplaces. Malicious actors can exploit these leaked credentials to drastically lower the barrier to entry associated with brute-forcing access to their target networks.

While implementing well-configured MFA and enforcing regular password changes can help protect organizations, these measures alone may not be enough to fully negate the advantage attackers gain with stolen credentials.

In this instance, an attacker used leaked credentials to compromise an unused service account, allowing them to establish MFA and access the customer’s VPN. While this tactic may have allowed the attacker to evade human security teams and traditional security tools, Darktrace’s AI detected the unusual use of the account, indicating a potential compromise despite the organization’s MFA requirements being met. This underscores the importance of adopting an intelligent decision maker, like Darktrace, that is able to identify and respond to anomalies beyond standard protective measures.

Credit to Charlotte Thompson, Cyber Security Analyst, Ryan Traill, Threat Content Lead

Appendices

Darktrace DETECT Model Coverage

-       Device / Suspicious SMB Scanning Activity (Model Alert)

-       Device / ICMP Address Scan (Model Alert)

-       Device / Network Scan (Model Alert)

-       Device / Suspicious LDAP Search Operation (Model Alert)

-       User / Kerberos Username Brute Force (Model Alert)

-       Device / Large Number of Model Breaches (Model Alert)

-       Anomalous Connection / SMB Enumeration (Model Alert)

-       Device / Multiple Lateral Movement Model Breaches (Enhanced Monitoring Model Alert)

-       Device / Possible SMB/NTLM Reconnaissance (Model Alert)

-       Anomalous Connection / Possible Share Enumeration Activity (Model Alert)

-       Device / Attack and Recon Tools (Model Alert)

MITRE ATT&CK Mapping

Tactic – Technique - Code

INITIAL ACCESS - Hardware Additions     -T1200

DISCOVERY - Network Service Scanning -T1046

DISCOVERY - Remote System Discovery - T1018

DISCOVERY - Domain Trust Discovery      - T1482

DISCOVERY - File and Directory Discovery - T1083

DISCOVERY - Network Share Discovery - T1135

RECONNAISSANCE - Scanning IP Blocks - T1595.001

RECONNAISSANCE - Vulnerability Scanning - T1595.002

RECONNAISSANCE - Client Configurations - T1592.004

RECONNAISSANCE - IP Addresses - T1590.005

CREDENTIAL ACCESS - Brute Force - T1110

LATERAL MOVEMENT - Exploitation of Remote Services -T1210

References

  1. 2024 Google Cloud Threat Horizons Report
    https://services.google.com/fh/files/misc/threat_horizons_report_h12024.pdf
  2. IBM X-Force Threat Intelligence Index 2024
    https://www.ibm.com/reports/threat-intelligence
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Charlotte Thompson
Cyber Analyst

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Exploring the Benefits and Risks of Third-Party Data Solutions

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03
Jun 2024

Why do companies allow third parties to handle their data?

Companies seek out third parties to handle their data for operational efficiency.

The scale and cost of maintaining in-house infrastructure can be outsourced to third parties who specialize in data management or in certain business functions.

Third parties who handle an organization’s data can range from large public cloud providers such as Azure or AWS, to boutique companies who handle specific business functions such as telemarketing, payment systems, or webpage hosting.

The operational efficiencies gained through third-party data management can be summarized by three key benefits:

  • Global accessibility: Third-party data storage enables data access across the globe, allowing businesses to access data from anywhere.
  • Enhanced collaboration: Third-party data storage allows for file sharing, real-time editing, and integration with other applications and services enhancing a business’s collaboration efforts.
  • Reliability and uptime: Reputable third-party storage providers offer high reliability and uptime guarantees, ensuring that data is available whenever needed. They typically have robust disaster recovery and backup systems in place to prevent data loss.

Given these benefits, it is no surprise that businesses are using these services to expand their operations and scale efforts with the need of a growing business. This strategic move not only optimizes resource allocation but also enhances operational agility, enabling businesses to adapt swiftly to evolving data demands and maintain a competitive edge in a dynamic market.

Security risks of entrusted data to third-party vendors

Entrusting data to third parties can expose businesses to supply chain risks and increase the risk of data breaches and unauthorized access. A business has less control over its data and becomes dependent on the third party's policies, practices, and uptime. Many third-party vendors are the target of hackers who specialize in monetizing sensitive data and exploiting gray areas around who is responsible for securing the data.

Thus, businesses are vulnerable when they entrust sensitive data to third-party platforms, which often lack transparency about data usage and security. The platforms, chosen mainly for cost, efficiency, and user experience, are frequent targets for cyber criminals, hacktivists, and opportunistic lone hackers looking for sensitive data accidentally exposed due to misconfigurations or poor data management policies.

Consumers are putting pressure on businesses to improve cybersecurity when handling their personal data. Businesses who suffer a data breach face a high level of scrutiny from customers, investors, the media, and governments, even when the data breach is the result of a third party’s being hacked. For example, Uber made headlines in 2022 for a data breach which was the result of a compromised vendor who had access to data regarding Uber’s employees.

Similarly, the UK’s Ministry of Defence was the victim of a data breach earlier this year when hackers targeted a third party payroll system used by the government department.

Why do cyber-criminals target third parties?

Cyber-criminals can potentially gain access to multiple networks when targeting a third-party storage provider. A successful attack could give attackers access to the networks and systems of all its clients, amplifying the impact of a single breach.

For example, when Illuminate Education was the target of a cyber-attack, the data of 23 US School Districts was stolen via its student-tracking software. It included student data from the country's two largest school systems - New York City Public Schools and Los Angeles Unified School District.

Common third-party security risks

When collaborating with third parties, organizations should be aware of the most common types of security risks posed to their cybersecurity.

  • Software supply chain attacks: Software supply chain attacks occur when cyber criminals infiltrate and compromise software products or updates at any point in the development or distribution process. This allows attackers to insert malicious code into legitimate software, which then gets distributed to users through trusted channels.
  • Human error: Human error in cybersecurity refers to mistakes made by individuals that lead to security breaches or vulnerabilities. These errors can result from lack of awareness, insufficient training, negligence, or simple mistakes.
  • Privileged access misuse: Privileged access misuse involves the inappropriate or unauthorized use of elevated access rights by individuals within an organization. This can include intentionally malicious actions or unintentional misuse of administrative privileges.

What to look for in a security solution when using third parties to store or manage data

Understanding the security posture of a third party is important when partnering with it and entrusting it with your organization’s data. Understanding how basic cyber hygiene policies are implemented is a good place to start, such as data retention policies, use of encryption for data in storage, and how identity and access are managed.

In some circumstances, it is important to understand who is responsible for the data’s security. For example, when using public cloud infrastructure, it is generally the responsibility of the data owner to manage how the data is accessed and stored.

In that situation, an organization needs to ensure it has solutions in place which gives it full visibility of that third-party environment, and which can proactively identify misconfigurations and detect and respond to suspicious activity in real time.

Benefits of using AI tools to aid in managing sensitive data

According to research performed by IBM, organizations with extensive use of security AI and automation identified and contained a data breach 108 days faster in 2023 than organizations that did not use AI for cybersecurity. (1) This figure is only likely to improve as companies mature in their adoption of AI for cyber security and can be a key indicator in the security posture of a third-party vendor.

Example of third-party security incidents

Sumo data breach

Sumo, an Australian energy and internet provider, suffered a data breach which they became aware of on May 13th, 2024. Further investigation into the cyber incident has found that “the personal details of approximately 40,000 customers were compromised, including approximately 3,000 Australian passport numbers.” (2)

While none of Sumo’s systems were allegedly accessed or affected and the third-party application also worked as designed (3), the incident was blamed on an unnamed third party. The breach may have been the result of a misconfiguration or human error.

This incident underscores the importance of not only selecting third-party providers with robust security measures but also continuously monitoring and assessing their security practices.

How Darktrace helps monitor third-party data usage

Darktrace/Cloud uses Self-Learning AI to provide complete cyber resilience for multi-cloud environments.

Benefits of Darktrace/Cloud:

Architectural awareness: Gives users an understanding of their cloud footprint, including real-time visibility into cloud assets, architectures, users and permissions. Combines asset enumeration, modeled architectures, and flow log analysis. Cost insights give a better understanding of resource allocation, helping teams contextualize resources.

Cloud-native detection and response: AI understands ‘normal’ for your unique business and stops cyber-threats with autonomous response. Near-real-time response goes beyond simple email alerts or opening a ticket; and includes cloud-native actions like detaching EC2 instances and applying security groups to contain risky assets.

Cloud protection and compliance: Identify compliance issues and potential misconfigurations with attack path modeling and prioritized remediation steps. Darktrace’s attack surface management (ASM) adds a critical external view of your organization, highlighting vulnerabilities most impactful to your specific situation and revealing shadow IT.

Learn more about securing cloud environments by reading: The CISO’s Guide to Cloud Security here.

References

1.    https://www.ibm.com/reports/data-breach

2.    https://www.passports.gov.au/news/sumo-data-breach

3.    https://www.smh.com.au/technology/sumo-slammed-by-data-breach-as-energy-and-internet-customers-have-details-leaked-20240515-p5jdwp.html

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Oakley Cox
Analyst Technical Director, APAC
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