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[Part 1] Analysis of a Raccoon Stealer v1 Infection

Nov 2022

Darktrace’s SOC team observed a fast-paced compromise involving Raccoon Stealer v1. See which steps the Raccoon Stealer v1 took to extract company data!

Introduction

Towards the end of March 2022, the operators of Raccoon Stealer announced the closure of the Raccoon Stealer project [1]. In May 2022, Raccoon Stealer v2 was unleashed onto the world, with huge numbers of cases being detected across Darktrace’s client base. In this series of blog posts, we will follow the development of Raccoon Stealer between March and September 2022. We will first shed light on how Raccoon Stealer functioned before its demise, by providing details of a Raccoon Stealer v1 infection which Darktrace’s SOC saw within a client network on the 18^th March 2022. In the follow-up post, we will provide details about the surge in Raccoon Stealer v2 cases that Darktrace’s SOC has observed since May 2022.

What is Raccoon Stealer?

The misuse of stolen account credentials is a primary method used by threat actors to gain initial access to target environments [2]. Threat actors have several means available to them for obtaining account credentials. They may, for example, distribute phishing emails which trick their recipients into divulging account credentials. Alternatively, however, they may install information-stealing malware (i.e, info-stealers) onto users’ devices. The results of credential theft can be devastating. Threat actors may use the credentials to gain access to an organization’s SaaS environment, or they may use them to drain users’ online bank accounts or cryptocurrency wallets.

Raccoon Stealer is a Malware-as-a-Service (MaaS) info-stealer first publicized in April 2019 on Russian-speaking hacking forums.

Figure 1: One of the first known mentions of Raccoon Stealer on a Russian-speaking hacking forum named ‘Hack Forums’ on the 13th April 2019

The team of individuals behind Raccoon Stealer provide a variety of services to their customers (known as ‘affiliates’), including access to the info-stealer, an easy-to-use automated backend panel, hosting infrastructure, and 24/7 customer support [3].

Once Raccoon Stealer affiliates gain access to the info-stealer, it is up to them to decide how to distribute it. Since 2019, affiliates have been observed distributing the info-stealer via a variety of methods, such as exploit kits, phishing emails, and fake cracked software websites [3]/[4]. Once affiliates succeed in installing Raccoon Stealer onto target systems, the info-stealer will typically seek to obtain sensitive information saved in browsers and cryptocurrency wallets. The info-stealer will then exfiltrate the stolen data to a Command and Control (C2) server. The affiliate can then use the stolen data to conduct harmful follow-up activities.

Towards the end of March 2022, the team behind Raccoon Stealer publicly announced that they would be suspending their operations after one of their core developers was killed during the Russia-Ukraine conflict [5].

Figure 2: Raccoon Stealer resignation post on March 25th 2022

Recent details shared by the US Department of Justice [6]/[7] indicate that it was in fact the arrest, rather than the death, of a key Raccoon Stealer operator which led the Raccoon Stealer team to suspend their operations [8].

The closure of the Raccoon Stealer project, which ultimately resulted from the FBI-backed dismantling of Raccoon Stealer’s infrastructure in March 2022, did not last long, with the completion of Raccoon Stealer v2 being announced on the Raccoon Stealer Telegram channel on the 17^th May 2022 [9].

Figure 3: Telegram post about new version of Raccoon Stealer

In the second part of this blog series, we will provide details of the recent surge in Raccoon Stealer v2 activity. In this post, however, we will provide insight into how the old version of Raccoon Stealer functioned just before its demise, by providing details of a Raccoon Stealer v1 infection which occurred on the 18^th March 2022.

Attack Details

On the 18^th March, at around 13:00 (UTC), a user’s device within a customer’s network was seen contacting several websites providing fake cracked software.

Figure 4: The above figure — obtained from the Darktrace Event Log for the infected device — highlights its connections to cracked software websites such as ‘licensekeysfree[.]com’ and ‘hdlicense[.]com’ before contacting ‘lion-files[.]xyz’ and ‘www.mediafire[.]com’

The user’s attempt to download cracked software from one of these websites resulted in their device making an HTTP GET request with a URI string containing ‘autodesk-revit-crack-v2022-serial-number-2022’ to an external host named ‘lion-filez[.]xyz’

Figure 5: Screenshot from hdlicense[.]com around the time of the infection shows a “Download” button linking to the ‘lion-filez[.]xyz’ endpoint

The device’s HTTP GET request to lion-filez[.]xyz was immediately followed by an HTTPS connection to the file hosting service, www.mediafire[.]com. Given that threat actors are known to abuse platforms such as MediaFire and Discord CDN to host their malicious payloads, it is likely that the user’s device downloaded the Raccoon Stealer v1 sample over its HTTPS connection to www.mediafire[.]com.

After installing the info-stealer sample, the user’s device was seen making an HTTP GET request with the URI string ‘/g_shock_casio_easy’ to 194.180.191[.]185. The endpoint responded to the request with data related to a Telegram channel named ‘G-Shock’.

Figure 6: Telegram channel ‘@g_shock_casio_easy’

The returned data included the Telegram channel’s description, which in this case, was a base64 encoded and RC4 encrypted string of characters [10]/[11]. The Raccoon Stealer sample decoded and decrypted this string of characters to obtain its C2 IP address, 188.166.49[.]196. This technique used by Raccoon Stealer v1 closely mirrors the espionage method known as ‘dead drop’ — a method in which an individual leaves a physical object such as papers, cash, or weapons in an agreed hiding spot so that the intended recipient can retrieve the object later on without having to come in to contact with the source. In this case, the operators of Raccoon Stealer ‘left’ the malware’s C2 IP address within the description of a Telegram channel. Usage of this method allowed the operators of Raccoon Stealer to easily change the malware’s C2 infrastructure.

After obtaining the C2 IP address from the ‘G-Shock’ Telegram channel, the Raccoon Stealer sample made an HTTP POST request with the URI string ‘/’ to the C2 IP address, 188.166.49[.]196. This POST request contained a Windows GUID, a username, and a configuration ID. These details were RC4 encrypted and base64 encoded [12]. The C2 server responded to this HTTP POST request with JSON-formatted configuration information [13], including an identifier string, URL paths for additional files, along with several other fields. This configuration information was also concealed using RC4 encryption and base64 encoding.

Figure 7- Fields within the JSON-formatted configuration data [13]

In this case, the server’s response included the identifier string ‘hv4inX8BFBZhxYvKFq3x’, along with the following URL paths:

/l/f/hv4inX8BFBZhxYvKFq3x/77d765d8831b4a7d8b5e56950ceb96b7c7b0ed70
/l/f/hv4inX8BFBZhxYvKFq3x/0cb4ab70083cf5985b2bac837ca4eacb22e9b711
/l/f/hv4inX8BFBZhxYvKFq3x/5e2a950c07979c670b1553b59b3a25c9c2bb899b
/l/f/hv4inX8BFBZhxYvKFq3x/2524214eeea6452eaad6ea1135ed69e98bf72979

After retrieving configuration data, the user’s device was seen making HTTP GET requests with the above URI strings to the C2 server. The C2 server responded to these requests with legitimate library files such as sqlite3.dll. Raccoon Stealer uses these libraries to extract data from targeted applications.

Once the Raccoon Stealer sample had collected relevant data, it made an HTTP POST request with the URI string ‘/’ to the C2 server. This posted data likely included a ZIP file (named with the identifier string) containing stolen credentials [13].

The observed infection chain, which lasted around 20 minutes, consisted of the following steps:

1. User’s device installs Raccoon Stealer v1 samples from the user attempting to download cracked software

2. User’s device obtains the info-stealer’s C2 IP address from the description text of a Telegram channel

3. User’s device makes an HTTP POST request with the URI string ‘/’ to the C2 server. The request contains a Windows GUID, a username, and a configuration ID. The response to the request contains configuration details, including an identifier string and URL paths for additional files

4. User’s device downloads library files from the C2 server

5. User’s device makes an HTTP POST request with the URI string ‘/’ to the C2 server. The request contains stolen data

Darktrace Coverage

Although RESPOND/Network was not enabled on the customer’s deployment, DETECT picked up on several of the info-stealer’s activities. In particular, the device’s downloads of library files from the C2 server caused the following DETECT/Network models to breach:

Anomalous File / Masqueraded File Transfer
Anomalous File / EXE from Rare External Location
Anomalous File / Zip or Gzip from Rare External Location
Anomalous File / EXE from Rare External Location
Anomalous File / Multiple EXE from Rare External Locations

Figure 8: Event Log for the infected device shows 'Anomalous File / Masqueraded File Transfer' model breach after the device's download of a library file from the C2 server

Since the customer was subscribed to the Darktrace Proactive Threat Notification (PTN) service, they were proactively notified of the info-stealer’s activities. The quick response by Darktrace’s 24/7 SOC team helped the customer to contain the infection and to prevent further damage from being caused. Having been alerted to the info-stealer activity by the SOC team, the customer would also have been able to change the passwords for the accounts whose credentials were exfiltrated.

If RESPOND/Network had been enabled on the customer’s deployment, then it would have blocked the device’s connections to the C2 server, which would have likely prevented any stolen data from being exfiltrated.

Conclusion

Towards the end of March 2022, the team behind Raccoon Stealer announced that they would be suspending their operations. Recent developments suggest that the arrest of a core Raccoon Stealer developer was responsible for this suspension. Just before the Raccoon Stealer team were forced to shut down, Darktrace’s SOC team observed a Raccoon Stealer infection within a client’s network. In this post, we have provided details of the network-based behaviors displayed by the observed Raccoon Stealer sample. Since these v1 samples are no longer active, the details provided here are only intended to provide historical insight into the development of Raccoon Stealer’s operations and the activities carried out by Raccoon Stealer v1 just before its demise. In the next post of this series, we will discuss and provide details of Raccoon Stealer v2 — the new and highly prolific version of Raccoon Stealer.

Thanks to Stefan Rowe and the Threat Research Team for their contributions to this blog.

References

[1] https://twitter.com/3xp0rtblog/status/1507312171914461188

[2] https://www.gartner.com/doc/reprints?id=1-29OTFFPI&ct=220411&st=sb

[3] https://www.cybereason.com/blog/research/hunting-raccoon-stealer-the-new-masked-bandit-on-the-block

[4] https://www.cyberark.com/resources/threat-research-blog/raccoon-the-story-of-a-typical-infostealer

[5] https://www.bleepingcomputer.com/news/security/raccoon-stealer-malware-suspends-operations-due-to-war-in-ukraine/

[6] https://www.justice.gov/usao-wdtx/pr/newly-unsealed-indictment-charges-ukrainian-national-international-cybercrime-operation

[7] https://www.youtube.com/watch?v=Fsz6acw-ZJY

[8] https://riskybiznews.substack.com/p/raccoon-stealer-dev-didnt-die-in

[9] https://medium.com/s2wblog/raccoon-stealer-is-back-with-a-new-version-5f436e04b20d

[10] https://blog.cyble.com/2021/10/21/raccoon-stealer-under-the-lens-a-deep-dive-analysis/

[11] https://decoded.avast.io/vladimirmartyanov/raccoon-stealer-trash-panda-abuses-telegram/

[12] https://blogs.blackberry.com/en/2021/09/threat-thursday-raccoon-infostealer

[13] https://cyberint.com/blog/research/raccoon-stealer/

Appendices

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.

AUTHOR

ABOUT ThE AUTHOR

Mark Turner

SOC Shift Supervisor

Sam Lister

SOC Analyst

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Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

May 2024

Email – the vector of choice for threat actors

In times of unprecedented globalization and internationalization, the enormous number of emails sent and received by organizations every day has opened the door for threat actors looking to gain unauthorized access to target networks.

Now, increasingly global organizations not only need to safeguard their email environments against phishing campaigns targeting their employees in their own language, but they also need to be able to detect malicious emails sent in foreign languages too [1].

Why are non-English language phishing emails more popular?

Many traditional email security vendors rely on pre-trained English language models which, while function adequately against malicious emails composed in English, would struggle in the face of emails composed in other languages. It should, therefore, come as no surprise that this limitation is becoming increasingly taken advantage of by attackers.

Darktrace/Email™, on the other hand, focuses on behavioral analysis and its Self-Learning AI understands what is considered ‘normal’ for every user within an organization’s email environment, bypassing any limitations that would come from relying on language-trained models [1].

In March 2024, Darktrace observed anomalous emails on a customer’s network that were sent from email addresses belonging to an international fast-food chain. Despite this seeming legitimacy, Darktrace promptly identified them as phishing emails that contained malicious payloads, preventing a potentially disruptive network compromise.

Attack Overview and Darktrace Coverage

On March 3, 2024, Darktrace observed one of the customer’s employees receiving an email which would turn out to be the first of more than 50 malicious emails sent by attackers over the course of three days.

The Sender

Darktrace/Email immediately understood that the sender never had any previous correspondence with the organization or its employees, and therefore treated the emails with caution from the onset. Not only was Darktrace able to detect this new sender, but it also identified that the emails had been sent from a domain located in China and contained an attachment with a Chinese file name.

Figure 1: The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.

Darktrace further detected that the phishing emails had been sent in a synchronized fashion between March 3 and March 5. Eight unique senders were observed sending a total of 55 emails to 55 separate recipients within the customer’s email environment. The format of the addresses used to send these suspicious emails was “12345@fastflavor-shack[.]cn”^*. The domain “fastflavor-shack[.]cn” is the legitimate domain of the Chinese division of an international fast-food company, and the numerical username contained five numbers, with the final three digits changing which likely represented different stores.

^*(To maintain anonymity, the pseudonym “Fast Flavor Shack” and its fictitious domain, “fastflavor-shack[.]cn”, have been used in this blog to represent the actual fast-food company and the domains identified by Darktrace throughout this incident.)

The use of legitimate domains for malicious activities become commonplace in recent years, with attackers attempting to leverage the trust endpoint users have for reputable organizations or services, in order to achieve their nefarious goals. One similar example was observed when Darktrace detected an attacker attempting to carry out a phishing attack using the cloud storage service Dropbox.

As these emails were sent from a legitimate domain associated with a trusted organization and seemed to be coming from the correct connection source, they were verified by Sender Policy Framework (SPF) and were able to evade the customer’s native email security measures. Darktrace/Email; however, recognized that these emails were actually sent from a user located in Singapore, not China.

Figure 2: Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.

The Emails

Darktrace/Email autonomously analyzed the suspicious emails and identified that they were likely phishing emails containing a malicious multistage payload.

Figure 3: Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.

There has been a significant increase in multistage payload attacks in recent years, whereby a malicious email attempts to elicit recipients to follow a series of steps, such as clicking a link or scanning a QR code, before delivering a malicious payload or attempting to harvest credentials [2].

In this case, the malicious actor had embedded a suspicious link into a QR code inside a Microsoft Word document which was then attached to the email in order to direct targets to a malicious domain. While this attempt to utilize a malicious QR code may have bypassed traditional email security tools that do not scan for QR codes, Darktrace was able to identify the presence of the QR code and scan its destination, revealing it to be a suspicious domain that had never previously been seen on the network, “sssafjeuihiolsw[.]bond”.

Figure 4: Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.

At the time of the attack, there was no open-source intelligence (OSINT) on the domain in question as it had only been registered earlier the same day. This is significant as newly registered domains are typically much more likely to bypass gateways until traditional security tools have enough intelligence to determine that these domains are malicious, by which point a malicious actor may likely have already gained access to internal systems [4]. Despite this, Darktrace’s Self-Learning AI enabled it to recognize the activity surrounding these unusual emails as suspicious and indicative of a malicious phishing campaign, without needing to rely on existing threat intelligence.

The most commonly used sender name line for the observed phishing emails was “财务部”, meaning “finance department”, and Darktrace observed subject lines including “The document has been delivered”, “Income Tax Return Notice” and “The file has been released”, all written in Chinese. The emails also contained an attachment named “通知文件.docx” (“Notification document”), further indicating that they had been crafted to pass for emails related to financial transaction documents.

Figure 5: Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.

Conclusion

Although this phishing attack was ultimately thwarted by Darktrace/Email, it serves to demonstrate the potential risks of relying on solely language-trained models to detect suspicious email activity. Darktrace’s behavioral and contextual learning-based detection ensures that any deviations in expected email activity, be that a new sender, unusual locations or unexpected attachments or link, are promptly identified and actioned to disrupt the attacks at the earliest opportunity.

In this example, attackers attempted to use non-English language phishing emails containing a multistage payload hidden behind a QR code. As traditional email security measures typically rely on pre-trained language models or the signature-based detection of blacklisted senders or known malicious endpoints, this multistage approach would likely bypass native protection.

Darktrace/Email, meanwhile, is able to autonomously scan attachments and detect QR codes within them, whilst also identifying the embedded links. This ensured that the customer’s email environment was protected against this phishing threat, preventing potential financial and reputation damage.

Credit to: Rajendra Rushanth, Cyber Analyst, Steven Haworth, Head of Threat Modelling, Email

Appendices

List of Indicators of Compromise (IoCs)

IoC – Type – Description

sssafjeuihiolsw[.]bond – Domain Name – Suspicious Link Domain

通知文件.docx – File - Payload

References

[1] https://darktrace.com/blog/stopping-phishing-attacks-in-enter-language

[2] https://darktrace.com/blog/attacks-are-getting-personal

[3] https://darktrace.com/blog/phishing-with-qr-codes-how-darktrace-detected-and-blocked-the-bait

[4] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

About the author

Rajendra Rushanth

Cyber Analyst

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The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

May 2024

About the AI Cybersecurity Report

Darktrace 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.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on cybersecurity solutions.

To access the full report, click here.

The effects of AI on cybersecurity solutions

Overwhelming alert volumes, high false positive rates, and endlessly innovative threat actors keep security teams scrambling. Defenders have been forced to take a reactive approach, struggling to keep pace with an ever-evolving threat landscape. It is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.

The impact of AI on the threat landscape will soon make yesterday’s approaches untenable. Cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the need. But not all AI is created equal, and not all these solutions live up to the widespread hype.

Do security professionals believe AI will impact their security operations?

Yes! 95% of cybersecurity professionals agree that AI-powered solutions will level up their organization’s defenses.

Not only is there strong agreement about the ability of AI-powered cybersecurity solutions to improve the speed and efficiency of prevention, detection, response, and recovery, but that agreement is nearly universal, with more than 95% alignment.

This AI-powered future is about much more than generative AI. While generative AI can help accelerate the data retrieval process within threat detection, create quick incident summaries, automate low-level tasks in security operations, and simulate phishing emails and other attack tactics, most of these use cases were ranked lower in their impact to security operations by survey participants.

There are many other types of AI, which can be applied to many other use cases:

Supervised machine learning: Applied more often than any other type of AI in cybersecurity. Trained on attack patterns and historical threat intelligence to recognize known attacks.

Natural language processing (NLP): Applies computational techniques to process and understand human language. It can be used in threat intelligence, incident investigation, and summarization.

Large language models (LLMs): Used in generative AI tools, this type of AI applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. The integrity of the output depends upon the quality of the data on which the AI was trained.

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies. With the correct models, this AI can use anomaly-based detections to identify all kinds of cyber-attacks, including entirely unknown and novel ones.

What are the areas of cybersecurity AI will impact the most?

Improving threat detection is the #1 area within cybersecurity where AI is expected to have an impact.

The most frequent response to this question, improving threat detection capabilities in general, was top ranked by slightly more than half (57%) of respondents. This suggests security professionals hope that AI will rapidly analyze enormous numbers of validated threats within huge volumes of fast-flowing events and signals. And that it will ultimately prove a boon to front-line security analysts. They are not wrong.

Identifying exploitable vulnerabilities (mentioned by 50% of respondents) is also important. Strengthening vulnerability management by applying AI to continuously monitor the exposed attack surface for risks and high-impact vulnerabilities can give defenders an edge. If it prevents threats from ever reaching the network, AI will have a major downstream impact on incident prevalence and breach risk.

Where will defensive AI have the greatest impact on cybersecurity?

Cloud security (61%), data security (50%), and network security (46%) are the domains where defensive AI is expected to have the greatest impact.

Respondents selected broader domains over specific technologies. In particular, they chose the areas experiencing a renaissance. Cloud is the future for most organizations,
and the effects of cloud adoption on data and networks are intertwined. All three domains are increasingly central to business operations, impacting everything everywhere.

Responses were remarkably consistent across demographics, geographies, and organization sizes, suggesting that nearly all survey participants are thinking about this similarly—that AI will likely have far-reaching applications across the broadest fields, as well as fewer, more specific applications within narrower categories.

Going forward, it will be paramount for organizations to augment their cloud and SaaS security with AI-powered anomaly detection, as threat actors sharpen their focus on these targets.

How will security teams stop AI-powered threats?

Most security stakeholders (71%) are confident that AI-powered security solutions are better able to block AI-powered threats than traditional tools.

There is strong agreement that AI-powered solutions will be better at stopping AI-powered threats (71% of respondents are confident in this), and there’s also agreement (66%) that AI-powered solutions will be able to do so automatically. This implies significant faith in the ability of AI to detect threats both precisely and accurately, and also orchestrate the correct response actions.

There is also a high degree of confidence in the ability of security teams to implement and operate AI-powered solutions, with only 30% of respondents expressing doubt. This bodes well for the acceptance of AI-powered solutions, with stakeholders saying they’re prepared for the shift.

On the one hand, it is positive that cybersecurity stakeholders are beginning to understand the terms of this contest—that is, that only AI can be used to fight AI. On the other hand, there are persistent misunderstandings about what AI is, what it can do, and why choosing the right type of AI is so important. Only when those popular misconceptions have become far less widespread can our industry advance its effectiveness.

To access the full report, click here.

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Check out this article by Darktrace: [Part 1] Analysis of a Raccoon Stealer v1 Infection

Blog
‍

Inside the SOC

[Part 1] Analysis of a Raccoon Stealer v1 Infection

Introduction

What is Raccoon Stealer?

Attack Details

Darktrace Coverage

Conclusion

References

Appendices

Like this and want more?

More in this series

Blog
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Inside the SOC

Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

Email – the vector of choice for threat actors

Why are non-English language phishing emails more popular?

Attack Overview and Darktrace Coverage

The Sender

The Emails

Conclusion

Appendices

List of Indicators of Compromise (IoCs)

References

Blog
‍

The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

About the AI Cybersecurity Report

The effects of AI on cybersecurity solutions

Do security professionals believe AI will impact their security operations?

What are the areas of cybersecurity AI will impact the most?

Where will defensive AI have the greatest impact on cybersecurity?

How will security teams stop AI-powered threats?

Elevate your cyber defenses with Darktrace AI

Additional Products

Blog‍

Inside the SOC

[Part 1] Analysis of a Raccoon Stealer v1 Infection

Introduction

What is Raccoon Stealer?

Attack Details

Darktrace Coverage

Conclusion

References

Appendices

Like this and want more?

Like this and want more?

More in this series

Blog‍

Inside the SOC

Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

Email – the vector of choice for threat actors

Why are non-English language phishing emails more popular?

Attack Overview and Darktrace Coverage

The Sender

The Emails

Conclusion

Appendices

List of Indicators of Compromise (IoCs)

References

Blog‍

The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

About the AI Cybersecurity Report

The effects of AI on cybersecurity solutions

Do security professionals believe AI will impact their security operations?

What are the areas of cybersecurity AI will impact the most?

Where will defensive AI have the greatest impact on cybersecurity?

How will security teams stop AI-powered threats?

Elevate your cyber defenses with Darktrace AI

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