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Understanding Email Security & the Psychology of Trust

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18
Jul 2023
18
Jul 2023
We explore how psychological research into the nature of trust relates to our relationship with technology - and what that means for AI solutions.

When security teams discuss the possibility of phishing attacks targeting their organization, often the first reaction is to assume it is inevitable because of the users. Users are typically referenced in cyber security conversations as organizations’ greatest weaknesses, cited as the causes of many grave cyber-attacks because they click links, open attachments, or allow multi-factor authentication bypass without verifying the purpose.

While for many, the weakness of the user may feel like a fact rather than a theory, there is significant evidence to suggest that users are psychologically incapable of protecting themselves from exploitation by phishing attacks, with or without regular cyber awareness trainings. The psychology of trust and the nature of human reliance on technology make the preparation of users for the exploitation of that trust in technology very difficult – if not impossible.

This Darktrace long read will highlight principles of psychological and sociological research regarding the nature of trust, elements of the trust that relate to technology, and how the human brain is wired to rely on implicit trust. These principles all point to the outcome that humans cannot be relied upon to identify phishing. Email security driven by machine augmentation, such as AI anomaly detection, is the clearest solution to tackle that challenge.

What is the psychology of trust?

Psychological and sociological theories on trust largely centre around the importance of dependence and a two-party system: the trustor and the trustee. Most research has studied the impacts of trust decisions on interpersonal relationships, and the characteristics which make those relationships more or less likely to succeed. In behavioural terms, the elements most frequently referenced in trust decisions are emotional characteristics such as benevolence, integrity, competence, and predictability.1

Most of the behavioural evaluations of trust decisions survey why someone chooses to trust another person, how they made that decision, and how quickly they arrived at their choice. However, these micro-choices about trust require the context that trust is essential to human survival. Trust decisions are rooted in many of the same survival instincts which require the brain to categorize information and determine possible dangers. More broadly, successful trust relationships are essential in maintaining the fabric of human society, critical to every element of human life.

Trust can be compared to dark matter (Rotenberg, 2018), which is the extensive but often difficult to observe material that binds planets and earthly matter. In the same way, trust is an integral but often a silent component of human life, connecting people and enabling social functioning.2

Defining implicit and routine trust

As briefly mentioned earlier, dependence is an essential element of the trusting relationship. Being able to build a routine of trust, based on the maintenance rather than establishment of trust, becomes implicit within everyday life. For example, speaking to a friend about personal issues and life developments is often a subconscious reaction to the events occurring, rather than an explicit choice to trust said friend each time one has new experiences.

Active and passive levels of cognition are important to recognize in decision-making, such as trust choices. Decision-making is often an active cognitive process requiring a lot of resource from the brain. However, many decisions occur passively, especially if they are not new choices e.g. habits or routines. The brain’s focus turns to immediate tasks while relegating habitual choices to subconscious thought processes, passive cognition. Passive cognition leaves the brain open to impacts from inattentional blindness, wherein the individual may be abstractly aware of the choice but it is not the focus of their thought processes or actively acknowledged as a decision. These levels of cognition are mostly referenced as “attention” within the brain’s cognition and processing.3

This idea is essentially a concept of implicit trust, meaning trust which is occurring as background thought processes rather than active decision-making. This implicit trust extends to multiple areas of human life, including interpersonal relationships, but also habitual choice and lifestyle. When combined with the dependence on people and services, this implicit trust creates a haze of cognition where trust is implied and assumed, rather than actively chosen across a myriad of scenarios.

Trust and technology

As researchers at the University of Cambridge highlight in their research into trust and technology, ‘In a fundamental sense, all technology depends on trust.’  The same implicit trust systems which allow us to navigate social interactions by subconsciously choosing to trust, are also true of interactions with technology. The implied trust in technology and services is perhaps most easily explained by a metaphor.

Most people have a favourite brand of soda. People will routinely purchase that soda and drink it without testing it for chemicals or bacteria and without reading reviews to ensure the companies that produce it have not changed their quality standards. This is a helpful, representative example of routine trust, wherein the trust choice is implicit through habitual action and does not mean the person is actively thinking about the ramifications of continuing to use a product and trust it.

The principle of dependence is especially important in trust and technology discussions, because the modern human is entirely reliant on technology and so has no way to avoid trusting it.5   Specifically important in workplace scenarios, employees are given a mandatory set of technologies, from programs to devices and services, which they must interact with on a daily basis. Over time, the same implicit trust that would form between two people forms between the user and the technology. The key difference between interpersonal trust and technological trust is that deception is often much more difficult to identify.

The implicit trust in workplace technology

To provide a bit of workplace-specific context, organizations rely on technology providers for the operation (and often the security) of their devices. The organizations also rely on the employees (users) to use those technologies within the accepted policies and operational guidelines. The employees rely on the organization to determine which products and services are safe or unsafe.

Within this context, implicit trust is occurring at every layer of the organization and its technological holdings, but often the trust choice is only made annually by a small security team rather than continually evaluated. Systems and programs remain in place for years and are used because “that’s the way it’s always been done. Within that context, the exploitation of that trust by threat actors impersonating or compromising those technologies or services is extremely difficult to identify as a human.

For example, many organizations utilize email communications to promote software updates for employees. Typically, it would consist of email prompting employees to update versions from the vendors directly or from public marketplaces, such as App Store on Mac or Microsoft Store for Windows. If that kind of email were to be impersonated, spoofing an update and including a malicious link or attachment, there would be no reason for the employee to question that email, given the explicit trust enforced through habitual use of that service and program.

Inattentional blindness: How the brain ignores change

Users are psychologically predisposed to trust routinely used technologies and services, with most of those trust choices continuing subconsciously. Changes to these technologies would often be subject to inattentional blindness, a psychological phenomenon wherein the brain either overwrites sensory information with what the brain expects to see rather than what is actually perceived.

A great example of inattentional blindness6 is the following experiment, which asks individuals to count the number of times a ball is passed between multiple people. While that is occurring, something else is going on in the background, which, statistically, those tested will not see. The shocking part of this experiment comes after, when the researcher reveals that the event occurring in the background not seen by participants was a person in a gorilla suit moving back and forth between the group. This highlights how significant details can be overlooked by the brain and “overwritten” with other sensory information. When applied to technology, inattentional blindness and implicit trust makes spotting changes in behaviour, or indicators that a trusted technology or service has been compromised, nearly impossible for most humans to detect.

With all this in mind, how can you prepare users to correctly anticipate or identify a violation of that trust when their brains subconsciously make trust decisions and unintentionally ignore cues to suggest a change in behaviour? The short answer is, it’s difficult, if not impossible.

How threats exploit our implicit trust in technology

Most cyber threats are built around the idea of exploiting the implicit trust humans place in technology. Whether it’s techniques like “living off the land”, wherein programs normally associated with expected activities are leveraged to execute an attack, or through more overt psychological manipulation like phishing campaigns or scams, many cyber threats are predicated on the exploitation of human trust, rather than simply avoiding technological safeguards and building backdoors into programs.

In the case of phishing, it is easy to identify the attempts to leverage the trust of users in technology and services. The most common example of this would be spoofing, which is one of the most common tactics observed by Darktrace/Email. Spoofing is mimicking a trusted user or service, and can be accomplished through a variety of mechanisms, be it the creation of a fake domain meant to mirror a trusted link type, or the creation of an email account which appears to be a Human Resources, Internal Technology or Security service.

In the case of a falsified internal service, often dubbed a “Fake Support Spoof”, the user is exploited by following instructions from an accepted organizational authority figure and service provider, whose actions should normally be adhered to. These cases are often difficult to spot when studying the sender’s address or text of the email alone, but are made even more difficult to detect if an account from one of those services is compromised and the sender’s address is legitimate and expected for correspondence. Especially given the context of implicit trust, detecting deception in these cases would be extremely difficult.

How email security solutions can solve the problem of implicit trust

How can an organization prepare for this exploitation? How can it mitigate threats which are designed to exploit implicit trust? The answer is by using email security solutions that leverage behavioural analysis via anomaly detection, rather than traditional email gateways.

Expecting humans to identify the exploitation of their own trust is a high-risk low-reward endeavour, especially when it takes different forms, affects different users or portions of the organization differently, and doesn’t always have obvious red flags to identify it as suspicious. Cue email security using anomaly detection as the key answer to this evolving problem.

Anomaly detection enabled by machine learning and artificial intelligence (AI) removes the inattentional blindness that plagues human users and security teams and enables the identification of departures from the norm, even those designed to mimic expected activity. Using anomaly detection mitigates multiple human cognitive biases which might prevent teams from identifying evolving threats, and also guarantees that all malicious behaviour will be detected. Of course, anomaly detection means that security teams may be alerted to benign anomalous activity, but still guarantees that no threat, no matter how novel or cleverly packaged, won’t be identified and raised to the human security team.

Utilizing machine learning, especially unsupervised machine learning, mimics the benefits of human decision making and enables the identification of patterns and categorization of information without the framing and biases which allow trust to be leveraged and exploited.

For example, say a cleverly written email is sent from an address which appears to be a Microsoft affiliate, suggesting to the user that they need to patch their software due to the discovery of a new vulnerability. The sender’s address appears legitimate and there are news stories circulating on major media providers that a new Microsoft vulnerability is causing organizations a lot of problems. The link, if clicked, forwards the user to a login page to verify their Microsoft credentials before downloading the new version of the software. After logging in, the program is available for download, and only requires a few minutes to install. Whether this email was created by a service like ChatGPT (generative AI) or written by a person, if acted upon it would give the threat actor(s) access to the user’s credential and password as well as activate malware on the device and possibly broader network if the software is downloaded.

If we are relying on users to identify this as unusual, there are a lot of evidence points that enforce their implicit trust in Microsoft services that make them want to comply with the email rather than question it. Comparatively, anomaly detection-driven email security would flag the unusualness of the source, as it would likely not be coming from a Microsoft-owned IP address and the sender would be unusual for the organization, which does not normally receive mail from the sender. The language might indicate solicitation, an attempt to entice the user to act, and the link could be flagged as it contains a hidden redirect or tailored information which the user cannot see, whether it is hidden beneath text like “Click Here” or due to link shortening. All of this information is present and discoverable in the phishing email, but often invisible to human users due to the trust decisions made months or even years ago for known products and services.

AI-driven Email Security: The Way Forward

Email security solutions employing anomaly detection are critical weapons for security teams in the fight to stay ahead of evolving threats and varied kill chains, which are growing more complex year on year. The intertwining nature of technology, coupled with massive social reliance on technology, guarantees that implicit trust will be exploited more and more, giving threat actors a variety of avenues to penetrate an organization. The changing nature of phishing and social engineering made possible by generative AI is just a drop in the ocean of the possible threats organizations face, and most will involve a trusted product or service being leveraged as an access point or attack vector. Anomaly detection and AI-driven email security are the most practical solution for security teams aiming to prevent, detect, and mitigate user and technology targeting using the exploitation of trust.

References

1https://www.kellogg.northwestern.edu/trust-project/videos/waytz-ep-1.aspx

2Rotenberg, K.J. (2018). The Psychology of Trust. Routledge.

3https://www.cognifit.com/gb/attention

4https://www.trusttech.cam.ac.uk/perspectives/technology-humanity-society-democracy/what-trust-technology-conceptual-bases-common

5Tyler, T.R. and Kramer, R.M. (2001). Trust in organizations : frontiers of theory and research. Thousand Oaks U.A.: Sage Publ, pp.39–49.

6https://link.springer.com/article/10.1007/s00426-006-0072-4

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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Thought Leadership

The State of AI in Cybersecurity: Understanding AI Technologies

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24
Jul 2024

About the State of 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”. This blog will focus on security professionals’ understanding of AI technologies in cybersecurity tools.

To access download the full report, click here.

How familiar are security professionals with supervised machine learning

Just 31% of security professionals report that they are “very familiar” with supervised machine learning.

Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).

Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.

Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.  

If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.  

Different types of AI

Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.  

Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.  

Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.  

Natural language processing (NLP): Applies computational techniques to process and understand human language.  

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.  

What impact will generative AI have on the cybersecurity field?

More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.

Chart showing the types of AI expected to impact security the most
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report

Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.

AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.

Do security professionals fully understand the different types of AI in security products?

Only 26% of security professionals report a full understanding of the different types of AI in use within security products.

Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.

Key findings include:

  • Executives and managers report higher understanding than practitioners.
  • Larger organizations have better understanding due to greater specialization.

As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.

To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.  

Do security professionals believe generative AI alone is enough to stop zero-day threats?

No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats

This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.

Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.

Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.

To access the full report, click here.

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Inside the SOC

Jupyter Ascending: Darktrace’s Investigation of the Adaptive Jupyter Information Stealer

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18
Jul 2024

What is Malware as a Service (MaaS)?

Malware as a Service (MaaS) is a model where cybercriminals develop and sell or lease malware to other attackers.

This approach allows individuals or groups with limited technical skills to launch sophisticated cyberattacks by purchasing or renting malware tools and services. MaaS is often provided through online marketplaces on the dark web, where sellers offer various types of malware, including ransomware, spyware, and trojans, along with support services such as updates and customer support.

The Growing MaaS Marketplace

The Malware-as-a-Service (MaaS) marketplace is rapidly expanding, with new strains of malware being regularly introduced and attracting waves of new and previous attackers. The low barrier for entry, combined with the subscription-like accessibility and lucrative business model, has made MaaS a prevalent tool for cybercriminals. As a result, MaaS has become a significant concern for organizations and their security teams, necessitating heightened vigilance and advanced defense strategies.

Examples of Malware as a Service

  • Ransomware as a Service (RaaS): Providers offer ransomware kits that allow users to launch ransomware attacks and share the ransom payments with the service provider.
  • Phishing as a Service: Services that provide phishing kits, including templates and email lists, to facilitate phishing campaigns.
  • Botnet as a Service: Renting out botnets to perform distributed denial-of-service (DDoS) attacks or other malicious activities.
  • Information Stealer: Information stealers are a type of malware specifically designed to collect sensitive data from infected systems, such as login credentials, credit card numbers, personal identification information, and other valuable data.

How does information stealer malware work?

Information stealers are an often-discussed type MaaS tool used to harvest personal and proprietary information such as administrative credentials, banking information, and cryptocurrency wallet details. This information is then exfiltrated from target networks via command-and-control (C2) communication, allowing threat actors to monetize the data. Information stealers have also increasingly been used as an initial access vector for high impact breaches including ransomware attacks, employing both double and triple extortion tactics.

After investigating several prominent information stealers in recent years, the Darktrace Threat Research team launched an investigation into indicators of compromise (IoCs) associated with another variant in late 2023, namely the Jupyter information stealer.

What is Jupyter information stealer and how does it work?

The Jupyter information stealer (also known as Yellow Cockatoo, SolarMarker, and Polazert) was first observed in the wild in late 2020. Multiple variants have since become part of the wider threat landscape, however, towards the end of 2023 a new variant was observed. This latest variant achieved greater stealth and updated its delivery method, targeting browser extensions such as Edge, Firefox, and Chrome via search engine optimization (SEO) poisoning and malvertising. This then redirects users to download malicious files that typically impersonate legitimate software, and finally initiates the infection and the attack chain for Jupyter [3][4]. In recently noted cases, users download malicious executables for Jupyter via installer packages created using InnoSetup – an open-source compiler used to create installation packages in the Windows OS.

The latest release of Jupyter reportedly takes advantage of signed digital certificates to add credibility to downloaded executables, further supplementing its already existing tactics, techniques and procedures (TTPs) for detection evasion and sophistication [4]. Jupyter does this while still maintaining features observed in other iterations, such as dropping files into the %TEMP% folder of a system and using PowerShell to decrypt and load content into memory [4]. Another reported feature includes backdoor functionality such as:

  • C2 infrastructure
  • Ability to download and execute malware
  • Execution of PowerShell scripts and commands
  • Injecting shellcode into legitimate windows applications

Darktrace Coverage of Jupyter information stealer

In September 2023, Darktrace’s Threat Research team first investigated Jupyter and discovered multiple IoCs and TTPs associated with the info-stealer across the customer base. Across most investigated networks during this time, Darktrace observed the following activity:

  • HTTP POST requests over destination port 80 to rare external IP addresses (some of these connections were also made via port 8089 and 8090 with no prior hostname lookup).
  • HTTP POST requests specifically to the root directory of a rare external endpoint.
  • Data streams being sent to unusual external endpoints
  • Anomalous PowerShell execution was observed on numerous affected networks.

Taking a further look at the activity patterns detected, Darktrace identified a series of HTTP POST requests within one customer’s environment on December 7, 2023. The HTTP POST requests were made to the root directory of an external IP address, namely 146.70.71[.]135, which had never previously been observed on the network. This IP address was later reported to be malicious and associated with Jupyter (SolarMarker) by open-source intelligence (OSINT) [5].

Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.
Figure 1: Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.

This activity triggered the Darktrace / NETWORK model, ‘Anomalous Connection / Posting HTTP to IP Without Hostname’. This model alerts for devices that have been seen posting data out of the network to rare external endpoints without a hostname. Further investigation into the offending device revealed a significant increase in external data transfers around the time Darktrace alerted the activity.

This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.
Figure 2: This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.

Packet capture (PCAP) analysis of this activity also demonstrates possible external data transfer, with the device observed making a POST request to the root directory of the malicious endpoint, 146.70.71[.]135.

PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.
Figure 3: PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.

In other cases investigated by the Darktrace Threat Research team, connections to the rare external endpoint 67.43.235[.]218 were detected on port 8089 and 8090. This endpoint was also linked to Jupyter information stealer by OSINT sources [6].

Darktrace recognized that such suspicious connections represented unusual activity and raised several model alerts on multiple customer environments, including ‘Compromise / Large Number of Suspicious Successful Connections’ and ‘Anomalous Connection / Multiple Connections to New External TCP Port’.

In one instance, a device that was observed performing many suspicious connections to 67.43.235[.]218 was later observed making suspicious HTTP POST connections to other malicious IP addresses. This included 2.58.14[.]246, 91.206.178[.]109, and 78.135.73[.]176, all of which had been linked to Jupyter information stealer by OSINT sources [7] [8] [9].

Darktrace further observed activity likely indicative of data streams being exfiltrated to Jupyter information stealer C2 endpoints.

Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.
Figure 4: Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.

In several cases, Darktrace was able to leverage customer integrations with other security vendors to add additional context to its own model alerts. For example, numerous customers who had integrated Darktrace with Microsoft Defender received security integration alerts that enriched Darktrace’s model alerts with additional intelligence, linking suspicious activity to Jupyter information stealer actors.

The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).
Figure 5: The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).

Conclusion

The MaaS ecosystems continue to dominate the current threat landscape and the increasing sophistication of MaaS variants, featuring advanced defense evasion techniques, poses significant risks once deployed on target networks.

Leveraging anomaly-based detections is crucial for staying ahead of evolving MaaS threats like Jupyter information stealer. By adopting AI-driven security tools like Darktrace / NETWORK, organizations can more quickly identify and effectively detect and respond to potential threats as soon as they emerge. This is especially crucial given the rise of stealthy information stealing malware strains like Jupyter which cannot only harvest and steal sensitive data, but also serve as a gateway to potentially disruptive ransomware attacks.

Credit to Nahisha Nobregas (Senior Cyber Analyst), Vivek Rajan (Cyber Analyst)

References

1.     https://www.paloaltonetworks.com/cyberpedia/what-is-multi-extortion-ransomware

2.     https://flashpoint.io/blog/evolution-stealer-malware/

3.     https://blogs.vmware.com/security/2023/11/jupyter-rising-an-update-on-jupyter-infostealer.html

4.     https://www.morphisec.com/hubfs/eBooks_and_Whitepapers/Jupyter%20Infostealer%20WEB.pdf

5.     https://www.virustotal.com/gui/ip-address/146.70.71.135

6.     https://www.virustotal.com/gui/ip-address/67.43.235.218/community

7.     https://www.virustotal.com/gui/ip-address/2.58.14.246/community

8.     https://www.virustotal.com/gui/ip-address/91.206.178.109/community

9.     https://www.virustotal.com/gui/ip-address/78.135.73.176/community

Appendices

Darktrace Model Detections

  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Compromise / HTTP Beaconing to Rare Destination
  • Unusual Activity / Unusual External Data to New Endpoints
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Large Number of Suspicious Successful Connections
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Excessive Posts to Root
  • Compromise / Sustained SSL or HTTP Increase
  • Security Integration / High Severity Integration Detection
  • Security Integration / Low Severity Integration Detection
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Unusual Activity / Unusual External Data Transfer

AI Analyst Incidents:

  • Unusual Repeated Connections
  • Possible HTTP Command and Control to Multiple Endpoints
  • Possible HTTP Command and Control

List of IoCs

Indicators – Type – Description

146.70.71[.]135

IP Address

Jupyter info-stealer C2 Endpoint

91.206.178[.]109

IP Address

Jupyter info-stealer C2 Endpoint

146.70.92[.]153

IP Address

Jupyter info-stealer C2 Endpoint

2.58.14[.]246

IP Address

Jupyter info-stealer C2 Endpoint

78.135.73[.]176

IP Address

Jupyter info-stealer C2 Endpoint

217.138.215[.]105

IP Address

Jupyter info-stealer C2 Endpoint

185.243.115[.]88

IP Address

Jupyter info-stealer C2 Endpoint

146.70.80[.]66

IP Address

Jupyter info-stealer C2 Endpoint

23.29.115[.]186

IP Address

Jupyter info-stealer C2 Endpoint

67.43.235[.]218

IP Address

Jupyter info-stealer C2 Endpoint

217.138.215[.]85

IP Address

Jupyter info-stealer C2 Endpoint

193.29.104[.]25

IP Address

Jupyter info-stealer C2 Endpoint

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About the author
Nahisha Nobregas
SOC Analyst
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