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Cyber Security Threat Trends of 2023: Analysis of the Last Six Months

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06
Feb 2024
06
Feb 2024
Darktrace's comprehensive report on the threats faced by businesses examines the trends our Threat Research team saw across our customer fleet in the second half of 2023.

Darktrace Threat Report

Darktrace’s distinctive approach to threat analysis yields us a unique perspective on the threat landscape. In our End of Year Threat Report, we built on the work of our First 6: Half-Year Threat Report, sharing the insights we've garnered throughout the latter half of 2023.  

We have observed not only the continuing development and evolution of identified threats in the malware and ransomware spaces, but also changes brought about by the innovation of cyber security tools.  

Amid these challenges, the breadth, scope, and complexity of threats to organizations has grown, underscoring the importance of employing behavioral analysis, anomaly detection, and AI for cyber security.  

Threat Research Across the Customer Fleet

Malware-as-a-Service (MaaS) and Ransomware-as-a-Service (RaaS) together represent the majority of malicious tools across the cyber threat landscape and were the most consistently identified threats affecting Darktrace customers in the second half of 2023. These malicious tools have a variety of capabilities, with many including tailorable or bespoke elements alterable from campaign to campaign.  

Figure 1: The diagram represents Darktrace detections containing indicators of compromise (IoCs) that have been associated with particular MaaS and RaaS threats. The size of the bubble displayed relates to the frequency of detections observed across the Darktrace fleet.

The Darktrace Threat Research team found that within MaaS and RaaS offerings detected across the customer fleet, loader malware was the most observed threat category, accounting for 77% of all investigated threats.  

MaaS initial access offerings were often observed harvesting data, which could then be sold, and loading or enabling subsequent infections by second and third-stage payloads, resulting in more damaging malware attacks and even ransomware.

Similar to how the MaaS and RaaS tools were often customized in an attempt to land an attack, Darktrace observed the cross-functional adaptation of many other malware strains, such as remote access trojans (RATs) and information-stealing malware, along with existing tools like Cobalt Strike.  

The ability to remix known strains of malware can increase the difficulty of detection by combining kill chain elements and utilizing overlapping compromised infrastructure. Malware developers achieve this by using open-source repositories, leaked code, and multi-faceted tooling.

SOC Team Insights on Major Trends

The Darktrace Security Operations Center (SOC), which helps customers investigate threats, observed two significant trends in the second half of 2023.

1. Enhanced Defense Evasion Methods

Darktrace's SOC saw an increase in usage of a variety of defense evasion methods, such as the session cookie abuse to evade multi-factor authentication (MFA), the targeting of ESXi servers for ransomware encryption to evade host-based security measures, and the use of tunnelling services such as Cloudflare Tunnel to hide command-and-control (C2) infrastructure.  

Malicious actors' increased usage of these defense evasion methods is a probable result of prominence of endpoint solutions within the security industry.

2. Ransomware

Ransomware continued to be the most common compromise. Darktrace's SOC observed ransomware actors compromising Internet-facing servers, such as Exchange, Citrix Netscaler, Ivanti Sentry, Remote Desktop Services (RDS) hosts, VPN appliances, and Confluence, in order to gain entry to target networks. Once inside, ransomware actors abused Remote Monitoring and Management (RMM) tools such as Splashtop, Atera, AnyDesk, and Action1, to gain access to target systems.  

A variety of ransomware strains were observed, with LockBit, ALPHV (i.e, BlackCat), Play, and Akira being the most common.

Top Critical Vulnerabilities

New critical vulnerabilities (CVEs), like Log4J and ProxyLogon, regularly enter the public domain within a short time of discovery, meaning the average time to exploitation is shorter than ever. As such, organizations must be able to promptly identify whether they are susceptible to new vulnerabilities and understand mitigation techniques.  

In the second half of 2023, there were five major vulnerabilities observed by Darktrace across its customer fleet, as determined by the number of affected assets.

1. CVE-2022-42889 is a critical vulnerability in the Apache Commons Text Library which has been compared to Log4Shell, albeit not as widespread. Apache Commons Text performs variable interpolation, allowing properties to be dynamically evaluated and expanded. Affected versions are vulnerable to remote code execution or unintentional exposure to remote servers if untrusted configuration values are used.

2. CVE-2023-25690 is a critical vulnerability which enables HTTP request smuggling attacks on Apache HTTP Server. If exploited, it could be used by an attacker to bypass access constraints in proxy servers, route undesired URLs to existing origin servers and perform cache poisoning.

3. Two critical vulnerabilities were observed in Git that would enable attackers to execute arbitrary code after successfully exploiting heap-based buffer overflow weaknesses. CVE-2022-41903 would allow an attacker to trigger a heap-based memory corruption during clone or pull operations, resulting in remote code execution, while CVE-2022-23521 could enable code execution during an archive operation, which is commonly performed by Git forges.

4. CVE-2023-2982 is an authentication bypass vulnerability disclosed in miniOrange's Social Login and Register plugin for WordPress that could enable a malicious actor to log in as any user, provided that they know the corresponding email address.

5. CVE-2023-46747 is a critical vulnerability rooted in the configuration of BIG-IP that could result in unauthenticated remote code execution. This vulnerability allows malicious actors to gain unauthorized access to networks through the management port and/or self-IP addresses to execute arbitrary system commands.

Stay Ahead of Threats with AI-Powered Cyber Security

After tracking threat trends across its customer fleet in the second half of 2023, Darktrace found that MaaS like loader malware, ransomware and especially RaaS, and enhanced defense evasion methods were top threats.  

As threats continue to evolve, it’s more important than ever to have cyber security tools that can detect and respond in real time, even when dealing with remixed and novel attacks.  

Darktrace’s approach to cyber security allows it to do just that. The Darktrace platform uses AI that learns from each organization’s specific data to understand ‘normal’ in order to recognize activity that is abnormal and indicative of a cyber-attack.  

As a result, Darktrace can detect and respond to attacks, including customized strains of malware and ransomware, even if they have been altered from previously known instances. Since it is powered by AI, Darktrace can take action within seconds.

Darktrace can also help organizations address new CVEs. Darktrace/Newsroom, a capability included with Darktrace’s attack surface management (ASM) tool, continuously monitors open-source intelligence (OSINT) sources for new CVEs and assesses each organization’s exposure through its in-depth knowledge of the unique external attack surface. It then presents a detailed summary of the vulnerability, highlighting the affected software and how many assets run this software on the customer’s network.

With AI that is trained on your organization’s data, Darktrace protects against the trending threats of today and the emerging threats of tomorrow.  

Learn more about the latest threat trends in the full report

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

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15
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.

The phishing emails detected by Darktrace sent from a domain in China and containing 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.

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.
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.

Darktrace/Email identifying the presence of a malicious phishing link and a 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”.

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

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

結論

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

付録  

侵害指標(IoC)一覧  

IoC – Type – Description

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

通知文件.docx – File - Payload  

参考文献

[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

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

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13
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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