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Threat Finds
APT35 ‘Charming Kitten' discovered in a pre-infected environment
APT35, sometimes referred to as Charming Kitten, Imperial Kitten, or Tortoiseshell, is a notorious cyber-espionage group which has been active for nearly 10 years. Famous for stealing scripts from HBO’s Game of Thrones in 2017 and suspected of interfering in the U.S. presidential election last year, it has launched extensive campaigns against organizations and officials across North America and the Middle East. Public attribution has associated APT35 with an Iran-based nation state threat actor.
Darktrace regularly detects attacks by many known threat actors including Evil Corp and APT41, alongside large amounts of malicious but uncategorized activity from sophisticated attack groups. As Cyber AI doesn’t rely on pre-defined rules, signatures, or threat intelligence to detect cyber-attacks, it often detects new and previously unknown threats.
This blog post examines a real-world instance of APT35 activity in an organization in the EMEA region. Darktrace observed this activity last June, but due to ongoing investigations, details are only now being released with the wider community. It represents an interesting case for the value of self-learning AI in two key ways:
- Identifying ‘low and slow’ attacks: How do you spot an attacker that is lying low and conducts very little detectable activity?
- Detecting pre-existing infections without signatures: What if a threat actor is already inside the system when Cyber AI is activated?
Advanced Persistent Threats (APTs) lying low
APT35 had already infected a single corporate device, likely via a spear phishing email, when Cyber AI was deployed in the company’s digital estate for the first time.
The infected device exhibited no other signs of malicious activity beyond continued command and control (C2) beaconing, awaiting instructions from the attackers for several days. This is what we call ‘lying low’ – where the hacker stays present within a system, but remains under the radar, avoiding detection either intentionally, or because they’re focusing on another victim while being content with backdoor access into the organization.
Either way, this is a nightmare scenario for a security team and any security vendor: an APT which has established a foothold and is lying in wait to continue their attack – undetected.
Finding the infected device
When Darktrace’s AI was first activated, it spent five business days learning the unique ‘patterns of life’ for the organization. After this initial, short learning period, Darktrace immediately flagged the infected device and the C2 activity.
Although the breach device had been beaconing since before Darktrace was implemented, Cyber AI automatically clusters devices into ‘peer groups’ based on similar behavioral patterns, enabling Darktrace to identify the continued C2 traffic coming from the device as highly unusual in comparison to the wider, automatically identified peer group. None of its behaviorally close neighbors were doing anything remotely similar, and Darktrace was therefore able to determine that the activity was malicious, and that it represented C2 beaconing.
Darktrace detected the APT35 C2 activity without the use of any signatures or threat intelligence on multiple levels. Responding to the alerts, the internal security team quickly isolated the device and verified with the Darktrace system that no further reconnaissance, lateral movement, or data exfiltration had taken place.
APT35 ‘Charming Kitten’ analysis
Once the C2 was detected, Cyber AI Analyst immediately began analyzing the infected device. The Cyber AI Analyst only highlights the most severe incidents in any given environment and automates many of the typical level one and level two SOC tasks. This includes reviewing all alerts, investigating the scope and nature of each event, and reducing time to triage by 92%.
Figure 1: Similar Cyber AI Analyst report observing C2 communications
Numerous factors made the C2 activity stand out strongly to Darktrace. Combining all those small anomalies, Darktrace was able to autonomously prioritize this behavior and classify it as the most significant security incident in the week.
Figure 2: Example list of C2 detections for an APT35 attack
Some of the command and control destinations were known to threat intelligence and open-source intelligence (OSINT) – for instance, the domain cortanaservice[.]com is a known C2 domain for APT35.
However, the presence of a known malicious domain does not guarantee detection. In fact, the organization had a very mature security stack, yet they failed to discover the existing APT35 infection until Darktrace was activated in their environment.
Assessing the impact of the intrusion
Once an intrusion has been identified, it is important to understand the extent of it – such as whether lateral movement is occurring and what connectivity the infected device has in general. Asset management is never perfect, so it can be very hard for organizations to determine what damage a compromised device is capable of inflicting.
Darktrace presents this information in real time, and from a bird’s-eye perspective, making the assessment very simple. It immediately highlights which subnet the device is located in and any further context.
Figure 3: Darktrace’s Threat Visualizer displaying the connectivity of a device
Based on this information, the organization confirmed that it was a corporate device that had been infected by APT35. As Darktrace shows any credentials associated with the device, a quick assessment could be made of potentially compromised accounts.
Figure 4: Similar and associated credentials of a device
Luckily, only a single local user account was associated with the device.
The exact level of privileges and connectivity which the infected device had, as well as the extent to which the intrusion might have spread from the initially infected device, was still uncertain. By looking at the device’s event log, this became rapidly clear within minutes.
Filtering first for internal connections only (excluding any connections going to the Internet) gave a good idea of the level of connectivity of the device. A cursory glance showed that the device did indeed have some level of internal connectivity. It made DNS requests to the internal domain controller and was making successful NetBIOS connections over ports 135 and 139 internally.
By filtering further in the event log, it quickly became clear that in this time the device had not used any administrative channels, such as RDP, SSH, Telnet, or SMB. This is a strong indicator that no lateral movement over common channels had taken place.
It is more difficult to assess whether the device was performing any other suspicious activity, like stealthy reconnaissance or staging data from other internal devices. Darktrace provided another capability to assess this quickly – filtering the device’s network connections to show only unusual or new connections.
Figure 5: Event device log filtered to show unusual connections only
Darktrace assesses each individual connection for every entity observed in context, using its unsupervised machine learning to evaluate how unusual a given connection is. This could be a single new failed internal connection attempt, indicating stealthy reconnaissance, or a connection over SMB at an unusual time to a new internal destination, implying lateral movement or data staging.
By filtering for only unusual or new connections, Darktrace’s AI produces further leads that can be pursued extremely quickly, thanks to the context and added visibility.
No further suspicious internal connections were observed, strengthening the hypothesis that APT35 was lying low at that time.
Unprecedented but not unpreventable
Darktrace’s 24/7 monitoring service, Proactive Threat Notifications, would have alerted on and escalated the incident. Darktrace Antigena would have responded autonomously and enforced normal activity for the device, preventing the C2 traffic without interrupting regular business workflows.
It is impossible to predefine where the next attack will come from. APT35 is just one of the many sophisticated threat actors on the scene, and with such a diverse and volatile threat landscape, unsupervised machine learning is crucial in spotting and defending against anomalies, no matter what form they take.
This case study helps illustrate how Darktrace detects pre-existing infections and ‘low and slow’ attacks, and further shows how Darktrace can be used to quickly understand the scope and extent of an intrusion.
Learn how Cyber AI Analyst detected APT41 two weeks before public attribution
Shortened list of C2 detections over four days on the infected device:
- Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
- Compromise / Beaconing Meta Model
- Compromise / Beaconing Activity To External Rare
- Compromise / SSL Beaconing To Rare Destination
- Compromise / Slow Beaconing To External Rare
- Compromise / High Volume of Connections with Beacon Score
- Compromise / Unusual Connections to Rare Lets Encrypt
- Compromise / Beacon for 4 Days
- Compromise / Agent Beacon
Observed C2 destinations:
- Cortanaservice[.]com, port 443, beacon intervals at 100 seconds
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Inside the SOC
How Abuse of ‘PerfectData Software’ May Create a Perfect Storm: An Emerging Trend in Account Takeovers
Amidst the ever-changing threat landscape, new tactics, techniques, and procedures (TTPs) seem to emerge daily, creating extreme challenges for security teams. The broad range of attack methods utilized by attackers seems to present an insurmountable problem: how do you defend against a playbook that does not yet exist?
Faced with the growing number of novel and uncommon attack methods, it is essential for organizations to adopt a security solution able to detect threats based on their anomalies, rather than relying on threat intelligence alone.
In March 2023, Darktrace observed an emerging trend in the use of an application known as ‘PerfectData Software’ for probable malicious purposes in several Microsoft 365 account takeovers.
Using its anomaly-based detection, Darktrace DETECT™ was able to identify the activity chain surrounding the use of this application, potentially uncovering a novel piece of threat actor tradecraft in the process.
Microsoft 365 Intrusions
In recent years, Microsoft’s Software-as-a-Service (SaaS) suite, Microsoft 365, along with its built-in identity and access management (IAM) service, Azure Active Directory (Azure AD), have been heavily targeted by threat actors due to their near-ubiquitous usage across industries. Four out of every five Fortune 500 companies, for example, use Microsoft 365 services [1].
Malicious actors typically gain entry to organizations’ Microsoft 365 environments by abusing either stolen account credentials or stolen session cookies [2]. Once inside, actors can access sensitive data within mailboxes or SharePoint repositories, and send out emails or Teams messages. This activity can often result in serious financial harm, especially in cases where the malicious actor’s end-goal is to elicit fraudulent transactions.
Darktrace regularly observes malicious actors behaving in predictable ways once they gain access to customer Microsoft 365 environment. One typical example is the creation of new inbox rules and sending deceitful emails intended to convince recipients to carry out subsequent actions, such as following a malicious link or providing sensitive information. It is also common for actors to register new applications in Azure AD so that they can be used to conduct follow-up activities, like mass-mailing or data theft. The registration of applications in Azure AD therefore seems to be a relatively predictable threat actor behavior [3][4]. Darktrace DETECT understands that unusual application registrations in Azure AD may constitute a deviation in expected behavior, and therefore a possible indicator of account compromise.
These registrations of applications in Azure AD are evidenced by creations of, as well as assignments of permissions to, Service Principals in Azure AD. Darktrace has detected a growing trend in actors creating and assigning permissions to a Service Principal named ‘PerfectData Software’. Further investigation of this Azure AD activity revealed it to be part of an ongoing account takeover.
‘PerfectData Software’ Activity
Darktrace observed variations of the following pattern of activity relating to an application named ‘PerfectData Software’ within its customer base:
- Actor signs in to a Microsoft 365 account from an endpoint associated with a Virtual Private Server (VPS) or Virtual Private Network (VPN) service
- Actor registers an application called 'PerfectData Software' with Azure AD, and then grants permissions to the application
- Actor accesses mailbox data and creates inbox rule
In two separate incidents, malicious actors were observed conducting their activities from endpoints associated with VPN services (HideMyAss (HMA) VPN and Surfshark VPN, respectively) and from endpoints within the Autonomous System AS396073 MAJESTIC-HOSTING-01.
In March 2023, Darktrace observed a malicious actor signing in to a Microsoft 365 account from a Kuwait-based IP address within the Autonomous System, AS198605 AVAST Software s.r.o. This IP address is associated with the VPN service, HMA VPN. Over the next couple of days, an actor (likely the same malicious actor) signed in to the account several more times from two different Nigeria-based endpoints, as well as a VPS-related endpoint and a HMA VPN endpoint.
During their login sessions, the actor performed a variety of actions. First, they created and assigned permissions to a Service Principal named ‘PerfectData Software’. This Service Principal creation represents the registration of an application called ‘PerfectData Software’ in Azure AD. Although the reason for registering this application is unclear, within a few days the actor registered and granted permission to another application, ‘Newsletter Software Supermailer’, and created a new inbox rule names ‘s’ on the mailbox of the hijacked account. This inbox rule moved emails meeting certain conditions to a folder named ‘RSS Subscription. The ‘Newsletter Software Supermailer’ application was likely registered by the actor to facilitate mass-mailing activity.
Immediately after these actions, Darktrace detected the actor sending out thousands of malicious emails from the account. The emails included an attachment named ‘Credit Transfer Copy.html’, which contained a suspicious link. Further investigation revealed that the customer’s network had received several fake invoice emails prior to this initial intrusion activity. Additionally, there was an unusually high volume of failed logins to the compromised account around the time of the initial access.
In a separate case also observed by Darktrace in March 2023, a malicious actor was observed signing in to a Microsoft 365 account from an endpoint within the Autonomous System, AS397086 LAYER-HOST-HOUSTON. The endpoint appears to be related to the VPN service, Surfshark VPN. This login was followed by several failed and successful logins from a VPS-related within the Autonomous System, AS396073 MAJESTIC-HOSTING-01. The actor was then seen registering and assigning permissions to an application called ‘PerfectData Software’. As with the previous example, the motives for this registration are unclear. The actor proceeded to log in several more times from a Surfshark VPN endpoint, however, they were not observed carrying out any further suspicious activity.
It was not clear in either of these examples, nor in fact any of cases observed by Darktrace, why actors had registered and assigned permissions to an application called ‘PerfectData Software’, and there do not appear to be any open-source intelligence (OSINT) resources or online literature related to the malicious usage of an application by that name. That said, there are several websites which appear to provide email migration and data recovery/backup tools under the moniker ‘PerfectData Software’.
It is unclear whether the use of ‘PerfectData Software’ by malicious actors observed on the networks of Darktrace customers was one of these tools. However, given the nature of the tools, it is possible that the actors intended to use them to facilitate the exfiltration of email data from compromises mailboxes.
If the legitimate software ‘PerfectData’ is the application in question in these incidents, it is likely being purchased and misused by attackers for malicious purposes. It is also possible the application referenced in the incidents is a spoof of the legitimate ‘PerfectData’ software designed to masquerade a malicious application as legitimate.
Darktrace Coverage
Cases of ‘PerfectData Software’ activity chains detected by Darktrace typically began with an actor signing into an internal user’s Microsoft 365 account from a VPN or VPS-related endpoint. These login events, along with the suspicious email and/or brute-force activity which preceded them, caused the following DETECT models to breach:
- SaaS / Access / Unusual External Source for SaaS Credential Use
- SaaS / Access / Suspicious Login Attempt
- SaaS / Compromise / Login From Rare Following Suspicious Login Attempt(s)
- SaaS / Email Nexus / Unusual Location for SaaS and Email Activity
Subsequent activities, including inbox rule creations, registration of applications in Azure AD, and mass-mailing activity, resulted in breaches of the following DETECT models.
- SaaS / Admin / OAuth Permission Grant
- SaaS / Compromise / Unusual Logic Following OAuth Grant
- SaaS / Admin / New Application Service Principal
- IaaS / Admin / Azure Application Administration Activities
- SaaS / Compliance / New Email Rule
- SaaS / Compromise / Unusual Login and New Email Rule
- SaaS / Email Nexus / Suspicious Internal Exchange Activity
- SaaS / Email Nexus / Possible Outbound Email Spam
- SaaS / Compromise / Unusual Login and Outbound Email Spam
- SaaS / Compromise / Suspicious Login and Suspicious Outbound Email(s)
In cases where Darktrace RESPOND™ was enabled in autonomous response mode, ‘PerfectData Software’ activity chains resulted in breaches of the following RESPOND models:
• Antigena / SaaS / Antigena Suspicious SaaS Activity Block
• Antigena / SaaS / Antigena Significant Compliance Activity Block
In response to these model breaches, Darktrace RESPOND took immediate action, performing aggressive, inhibitive actions, such as forcing the actor to log out of the SaaS platform, and disabling the user entirely. When applied autonomously, these RESPOND actions would seriously impede an attacker’s progress and minimize network disruption.
In addition, Darktrace Cyber AI Analyst was able to autonomously investigate registrations of the ‘PerfectData Software’ application and summarized its findings into digestible reports.
Conclusion
Due to the widespread adoption of Microsoft 365 services in the workplace and continued emphasis on a remote workforce, account hijackings now pose a more serious threat to organizations around the world than ever before. The cases discussed here illustrate the tendency of malicious actors to conduct their activities from endpoints associated with VPN services, while also registering new applications, like PerfectData Software, with malicious intent.
While it was unclear exactly why the malicious actors were using ‘PerfectData Software’ as part of their account hijacking, it is clear that either the legitimate or spoofed version of the application is becoming an very likely emergent piece of threat actor tradecraft.
Darktrace DETECT’s anomaly-based approach to threat detection allowed it to recognize that the use of ‘PerfectData Software’ represented a deviation in the SaaS user’s expected behavior. While Darktrace RESPOND, when enabled in autonomous response mode, was able to quickly take preventative action against threat actors, blocking the potential use of the application for data exfiltration or other nefarious purposes.
Appendices
MITRE ATT&CK Mapping
Reconnaissance:
• T1598 – Phishing for Information
Credential Access:
• T1110 – Brute Force
Initial Access:
• T1078.004 – Valid Accounts: Cloud Accounts
Command and Control:
• T1105 – Ingress Tool Transfer
Persistence:
• T1098.003 – Account Manipulation: Additional Cloud Roles
Collection:
• T1114 – Email Collection
Defense Evasion:
• T1564.008 – Hide Artifacts: Email Hiding Rules
Lateral Movement:
• T1534 – Internal Spearphishing
Unusual Source IPs
• 5.62.60[.]202 (AS198605 AVAST Software s.r.o.)
• 160.152.10[.]215 (AS37637 Smile-Nigeria-AS)
• 197.244.250[.]155 (AS37705 TOPNET)
• 169.159.92[.]36 (AS37122 SMILE)
• 45.62.170[.]237 (AS396073 MAJESTIC-HOSTING-01)
• 92.38.180[.]49 (AS202422 G-Core Labs S.A)
• 129.56.36[.]26 (AS327952 AS-NATCOM)
• 92.38.180[.]47 (AS202422 G-Core Labs S.A.)
• 107.179.20[.]214 (AS397086 LAYER-HOST-HOUSTON)
• 45.62.170[.]31 (AS396073 MAJESTIC-HOSTING-01)
References
[1] https://www.investing.com/academy/statistics/microsoft-facts/
[2] https://intel471.com/blog/countering-the-problem-of-credential-theft
[3] https://darktrace.com/blog/business-email-compromise-to-mass-phishing-campaign-attack-analysis
[4] https://darktrace.com/blog/breakdown-of-a-multi-account-compromise-within-office-365
Blog
Cloud
Darktrace Integrates Self-Learning AI with Amazon Security Lake to Support Security Investigations
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Darktrace has deepened its relationship with AWS by integrating its detection and response capabilities with Amazon Security Lake.
This development will allow mutual customers to seamlessly combine Darktrace AI’s bespoke understanding of their organization with the Threat Intelligence offered by other security tools, and investigate all of their alerts in one central location.
This integration will improve the value security teams get from both products, streamlining analyst workflows and improving their ability to detect and respond to the full spectrum of known and unknown cyber-threats.
How Darktrace and Amazon Security Lake augment security teams
Amazon Security Lake is a newly-released service that automatically centralizes an organization’s security data from cloud, on-premises, and custom sources into a customer owned purpose-built data lake. Both Darktrace and Amazon Security Lake support the Open Cybersecurity Schema Framework (OCSF), an open standard to simplify, combine, and analyze security logs.
Customers can store security logs, events, alerts, and other relevant data generated by various AWS services and security tools. By consolidating security data in a central lake, organizations can gain a holistic view of their security posture, perform advanced analytics, detect anomalies and open investigations to improve their security practices.
With Darktrace DETECT and RESPOND AI engines covering all assets across IT, OT, network, endpoint, IoT, email and cloud, organizations can augment the value of their security data lakes by feeding Darktrace’s rich and context-aware datapoints to Amazon Security Lake.
Amazon Security Lake empowers security teams to improve the protection of your digital estate:
- Quick and painless data normalization
- Fast-tracks ability to investigate, triage and respond to security events
- Broader visibility aids more effective decision-making
- Surfaces and prioritizes anomalies for further investigation
- Single interface for seamless data management
How will Darktrace customers benefit?
Across the Cyber AI Loop, all Darktrace solutions have been architected with AWS best practices in mind. With this integration, Darktrace is bringing together its understanding of ‘self’ for every organization with the centralized data visibility of the Amazon Security Lake. Darktrace’s unique approach to cyber security, powered by groundbreaking AI research, delivers a superior dataset based on a deep and interconnected understanding of the enterprise.
Where other cyber security solutions are trained to identify threats based on historical attack data and techniques, Darktrace DETECT gains a bespoke understanding of every digital environment, continuously analyzing users, assets, devices and the complex relationships between them. Our AI analyzes thousands of metrics to reveal subtle deviations that may signal an evolving issue – even unknown techniques and novel malware. It distinguishes between malicious and benign behavior, identifying harmful activity that typically goes unnoticed. This rich dataset is fed into RESPOND, which takes precise action to neutralize threats against any and every asset, no matter where data resides.
Both DETECT and RESPOND are supported by Darktrace Self-Learning AI, which provides full, real-time visibility into an organization’s systems and data. This always-on threat analysis already makes humans better at cyber security, improving decisions and outcomes based on total visibility of the digital ecosystem, supporting human performance with AI coverage and empowering security teams to proactively protect critical assets.
Converting Darktrace alerts to the Amazon Security Lake Open Cybersecurity Schema Framework (OCSF) supplies the Security Operations Center (SOC) and incident response team with contextualized data, empowering them to accelerate their investigation, triage and response to potential cyber threats.
Darktrace is available for purchase on the AWS Marketplace.
Learn more about how Darktrace provides full-coverage, AI-powered cloud security for AWS, or see how our customers use Darktrace in their AWS cloud environments.
