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.
Written by
Max Heinemeyer
Global Field CISO
Written by
Adam Stevens
Director of Product, Cloud Security
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09
Aug 2023
TLDR: There are too many technical vulnerabilities and there is too little organizational context for IT teams to patch effectively. Attack path modelling provides the organizational context, allowing security teams to prioritize vulnerabilities. The result is a system where CVEs can be parsed in, organizational context added, and attack paths considered, ultimately providing a prioritized list of vulnerabilities that need to be patched.
Figure 1: The Darktrace user interface presents risk-prioritized vulnerabilities
This blog post explains how Darktrace addresses the challenge of vulnerability prioritization. Most of the industry focusses on understanding the technical impact of vulnerabilities globally (‘How could this CVE generally be exploited? Is it difficult to exploit? Are there pre-requisites to exploitation? …’), without taking local context of a vulnerability into account. We’ll discuss here how we create that local context through attack path modelling and map it to technical vulnerability information. The result is a stunningly powerful way to prioritize vulnerabilities.
We will explore:
1) The challenge and traditional approach to vulnerability prioritization 2) Creating local context through machine learning and attack path modelling 3) Examining the result – contextualized, vulnerability prioritization
The Challenge
Anyone dealing with Threat and Vulnerability Management (TVM) knows this situation:
You have a vulnerability scanning report with dozens or hundreds of pages. There is a long list of ‘critical’ vulnerabilities. How do you start prioritizing these vulnerabilities, assuming your goal is reducing the most risk?
Sometimes the challenge is even more specific – you might have 100 servers with the same critical vulnerability present (e.g. MoveIT). But which one should you patch first, as all of those have the same technical vulnerability priority (‘critical’)? Which one will achieve the biggest risk reduction (critical asset e.g.)? Which one will be almost meaningless to patch (asset with no business impact e.g.) and thus just a time-sink for the patch and IT team?
There have been recent improvements upon flat CVE-scoring for vulnerability prioritization by adding threat-intelligence about exploitability of vulnerabilities into the mix. This is great, examples of that additional information are Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities Catalogue (KEV).
With CVE and CVSS scores we have the theoretical technical impact of vulnerabilities, and with EPSS and KEV we have information about the likelihood of exploitation of vulnerabilities. That’s a step forward, but still doesn’t give us any local context. Now we know even more about the global and generic technical risk of a vulnerability, but we still lack the local impact on the organization.
Let’s add that missing link via machine learning and attack path modelling.
Adding Attack Path Modelling for Local Context
To prioritize technical vulnerabilities, we need to know as much as we can about the asset on which the vulnerability is present in the context of the local organization. Is it a crown jewel? Is it a choke point? Does it sit on a critical attack path? Is it a dead end, never used and has no business relevance? Does it have organizational priority? Is the asset used by VIP users, as part of a core business or IT process? Does it share identities with elevated credentials? Is the human user on the device susceptible to social engineering?
Those are just a few typical questions when trying to establish local context of an asset. Knowing more about the threat landscape, exploitability, or technical information of a CVE won’t help answer any of the above questions. Gathering, evaluating, maintaining, and using this local context for vulnerability prioritization is the hard part. This local context often resides informally in the head of the TVM or IT team member, having been assembled by having been at the organization for a long time, ‘knowing’ systems, applications and identities in question and talking to asset and application owners if time permits. This does unfortunately not scale, is time-consuming and heavily dependent on individuals.
Understanding all attack paths for an organization provides this local context programmatically.
We discover those attack paths, and these are bespoke for each organization through Darktrace PREVENT™, using the following method (simplified):
1) Build an adaptive model of the local business. Collect, combine, and analyze (using machine learning and non-machine learning techniques) data from various data domains:
a. Network, Cloud, IT, and OT data (network-based attack paths, communication patterns, peer-groups, choke-points, …). Natively collected by Darktrace technology.
b. Email data (social engineering attack paths, phishing susceptibility, external exposure, security awareness level, …). Natively collected by Darktrace technology.
c. Identity data (account privileges, account groups, access levels, shared permissions, …). Collected via various integrations, e.g. Active Directory.
d. Attack surface data (internet-facing exposure, high-impact vulnerabilities, …). Natively collected by Darktrace technology.
e. SaaS information (further identity context). Natively collected by Darktrace
f. Vulnerability information (CVEs, CVSS, EPSS, KEV, …). Collected via integrations, e.g. Vulnerability Scanners or Endpoint products.
Figure 3: Darktrace PREVENT revealing each stage of an attack path
2) Understand what ‘crown jewels’ are and how to get to them. Calculate entity importance (user, technical asset), exposure levels, potential damage levels (blast radius) weakness levels, and other scores to identify most important entities and their relationships to each other (‘crown jewels’).
Various forms of machine learning and non-machine learning techniques are used to achieve this. Further details on some of the exact methods can be found here. The result is a holistic, adaptive and dynamic model of the organization that shows most important entities and how to get to them across various data domains.
The combination of local context and technical context, around the severity and likelihood of exploitation, creates the Darktrace Vulnerability Score. This enables effective risk-based prioritisation of CVE patching.
Figure 4: List of devices with the highest damage potential in the organization - local context
3) Map the attack path model of the organization to common cyber domain knowledge. We can then combine things like MITRE ATT&CK techniques with those identified connectivity patterns and attack paths – making it easy to understand which techniques, tools and procedures (TTPs) can be used to move through the organization, and how difficult it is to exploit each TTP.
Figure 5: An example attack path with associated MITRE techniques and difficulty scores for each TTP
We can now easily start prioritizing CVE patching based on actual, organizational risk and local context.
Bringing It All Together
Finally, we overlay the attack paths calculated by Darktrace with the CVEs collected from a vulnerability scanner or EDR. This can either happen as a native integration in Darktrace PREVENT, if we are already ingesting CVE data from another solution, or via CSV upload.
Figure 6: Darktrace's global CVE prioritization in action.
But you can also go further than just looking at the CVE that delivers the biggest risk reduction globally in your organization if it is patched. You can also look only at certain group of vulnerabilities, or a sub-set of devices to understand where to patch first in this reduced scope:
Figure 7: An example of the information Darktrace reveals around a CVE
This also provides the TVM team clear justification for the patch and infrastructure teams on why these vulnerabilities should be prioritized and what the positive impact will be on risk reduction.
Attack path modelling can be utilized for various other use cases, such as threat modelling and improving SOC efficiency. We’ll explore those in more depth at a later stage.
Want to explore more on using machine learning for vulnerability prioritization? Want to test it on your own data, for free? Arrange a demo today.
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.
Untangling the web: Darktrace’s investigation of Scattered Spider’s evolving tactics
What is Scattered Spider?
Scattered Spider is a native English-speaking group, also referred to, or closely associated with, aliases such as UNC3944, Octo Tempest and Storm-0875. They are primarily financially motivated with a clear emphasis on leveraging social engineering, SIM swapping attacks, exploiting legitimate tooling as well as using Living-Off-the-Land (LOTL) techniques [1][2].
In recent years, Scattered Spider has been observed employing a shift in tactics, leveraging Ransomware-as-a-Service (RaaS) platforms in their attacks. This adoption reflects a shift toward more scalable attacks with a lower barrier to entry, allowing the group to carry out sophisticated ransomware attacks without the need to develop it themselves.
While RaaS offerings have been available for purchase on the Dark Web for several years, they have continued to grow in popularity, providing threat actors a way to cause significant impact to critical infrastructure and organizations without requiring highly technical capabilities [12].
This blog focuses on the group’s recent changes in tactics, techniques, and procedures (TTPs) reported by open-source intelligence (OSINT) and how TTPs in a recent Scattered Spider attack observed by Darktrace compare.
How has Scattered Spider been reported to operate?
First observed in 2022, Scattered Spider is known to target various industries globally including telecommunications, technology, financial services, and commercial facilities.
Overview of key TTPs
Scattered Spider has been known to utilize the following methods which cover multiple stages of the Cyber Kill Chain including initial access, lateral movement, evasion, persistence, and action on objective:
Social engineering [1]:
Impersonating staff via phone calls, SMS and Telegram messages; obtaining employee credentials (MITRE techniques T1598,T1656), multi-factor authentication (MFA) codes such as one-time passwords, or convincing employees to run commercial remote access tools enabling initial access (MITRE techniques T1204,T1219,T1566)
Phishing using specially crafted domains containing the victim name e.g. victimname-sso[.]com
MFA fatigue: sending repeated requests for MFA approval with the intention that the victim will eventually accept (MITRE technique T1621)
SIM swapping [1][3]:
Includes hijacking phone numbers to intercept 2FA codes
This involves the actor migrating the victim's mobile number to a new SIM card without legitimate authorization
Reconnaissance, lateral movement & command-and-control (C2) communication via use of legitimate tools:
Examples include Mimikatz, Ngrok, TeamViewer, and Pulseway [1]. A more recently reported example is Teleport [3].
Financial theft through their access to victim networks: Extortion via ransomware, data theft (MITRE technique T1657) [1]
Bring Your Own Vulnerable Driver (BYOVD) techniques [4]:
Exploiting vulnerable drivers to evade detection from Endpoint Detection and Response (EDR) security products (MITRE technique T1068) frequently used against Windows devices.
LOTL techniques
LOTL techniques are also closely associated with Scattered Spider actors once they have gained initial access; historically this has allowed them to evade detection until impact starts to be felt. It also means that specific TTPs may vary from case-to-case, making it harder for security teams to prepare and harden defences against the group.
Prominent Scattered Spider attacks over the years
While attribution is sometimes unconfirmed, Scattered Spider have been linked with a number of highly publicized attacks since 2022.
Smishing attacks on Twilio: In August 2022 the group conducted multiple social engineering-based attacks. One example was an SMS phishing (smishing) attack against the cloud communication platform Twilio, which led to the compromise of employee accounts, allowing actors to access internal systems and ultimately target Twilio customers [5][6].
Phishing and social engineering against MailChimp: Another case involved a phishing and social engineering attack against MailChimp. After gaining access to internal systems through compromised employee accounts the group conducted further attacks specifically targeting MailChimp users within cryptocurrency and finance industries [5][7].
Social engineering against Riot Games: In January 2023, the group was linked with an attack on video game developer Riot Games where social engineering was once again used to access internal systems. This time, the attackers exfiltrated game source code before sending a ransom note [8][9].
Attack on Caesars & MGM: In September 2023, Scattered Spider was linked with attacked on Caesars Entertainment and MGM Resorts International, two of the largest casino and gambling companies in the United States. It was reported that the group gathered nearly six terabytes of stolen data from the hotels and casinos, including sensitive information of guests, and made use of the RaaS strain BlackCat [10].
Ransomware against Marks & Spencer: More recently, in April 2025, the group has also been linked to the alleged ransomware incident against the UK-based retailer Marks & Spencer (M&S) making use of the DragonForce RaaS [11].
How a recent attack observed by Darktrace compares
In May 2025, Darktrace observed a Scattered Spider attack affecting one of its customers. While initial access in this attack fell outside of Darktrace’s visibility, information from the affected customer suggests similar social engineering techniques involving abuse of the customer’s helpdesk and voice phishing (vishing) were used for reconnaissance.
Initial access
It is believed the threat actor took advantage of the customer’s third-party Software-as-a-Service (SaaS) applications, such as Salesforce during the attack.
Such applications are a prime target for data exfiltration due to the sensitive data they hold; customer, personnel, and business data can all prove useful in enabling further access into target networks.
Techniques used by Scattered Spider following initial access to a victim network tend to vary more widely and so details are sparser within OSINT. However, Darktrace is able to provide some additional insight into what techniques were used in this specific case, based on observed activity and subsequent investigation by its Threat Research team.
Lateral movement
Following initial access to the customer’s network, the threat actor was able to pivot into the customer’s Virtual Desktop Infrastructure (VDI) environment.
Darktrace observed the threat actor spinning up new virtual machines and activating cloud inventory management tools to enable discovery of targets for lateral movement.
In some cases, these virtual machines were not monitored or managed by the customer’s security tools, allowing the threat actor to make use of additional tooling such as AnyDesk which may otherwise have been blocked.
Tooling in further stages of the attack sometimes overlapped with previous OSINT reporting on Scattered Spider, with anomalous use of Ngrok and Teleport observed by Darktrace, likely representing C2 communication. Additional tooling was also seen being used on the virtual machines, such as Pastebin.
Figure 1: Cyber AI Analyst’s detection of C2 beaconing to a teleport endpoint with hostname CUSTOMERNAME.teleport[.]sh, likely in an attempt to conceal the traffic.
Leveraging LOTL techniques
Alongside use of third-party tools that may have been unexpected on the network, various LOTL techniques were observed during the incident; this primarily involved the abuse of standard network protocols such as:
SAMR requests to alter Active Directory account details
Lateral movement over RDP and SSH
Data collection over LDAP and SSH
Coordinated exfiltration activity linked through AI-driven analysis
Multiple methods of exfiltration were observed following internal data collection. This included SSH transfers to IPs associated with Vultr, alongside significant uploads to an Amazon S3 bucket.
While connections to this endpoint were not deemed unusual for the network at this stage due to the volume of traffic seen, Darktrace’s Cyber AI Analyst was still able to identify the suspiciousness of this behavior and launched an investigation into the activity.
Cyber AI Analyst successfully correlated seemingly unrelated internal download and external upload activity across multiple devices into a single, broader incident for the customer’s security team to review.
Figure 2: Cyber AI Analyst Incident summary showing a clear outline of the observed activity, including affected devices and the anomalous behaviors detected.
Figure 3: Cyber AI Analyst’s detection of internal data downloads and subsequent external uploads to an Amazon S3 bucket.
Exfiltration and response
Unfortunately, as Darktrace was not configured in Autonomous Response mode at the time, the attack was able to proceed without interruption, ultimately escalating to the point of data exfiltration.
Despite this, Darktrace was still able to recommend several Autonomous Response actions, aimed at containing the attack by blocking the internal data-gathering activity and the subsequent data exfiltration connections.
These actions required manual approval by the customer’s security team and as shown in Figure 3, at least one of the recommended actions was subsequently approved.
Had Darktrace been enabled in Autonomous Response mode, these measures would have been applied immediately, effectively halting the data exfiltration attempts.
Figure 4: Further recommendations for Autonomous Response actions in Darktrace‘s Incident Interface, with surgical response targeting both the internal data collection and subsequent exfiltration.
Scattered Spider’s use of RaaS
In this recent Scattered Spider incident observed by Darktrace, exfiltration appears to have been the primary impact. While no signs of ransomware deployment were observed here, it is possible that this was the threat actors’ original intent, consistent with other recent Scattered Spider attacks involving RaaS platforms like DragonForce.
DragonForce emerged towards the end of 2023, operating by offering their platform and capabilities on a wide scale. They also launched a program which offered their affiliates 80% of the eventual ransom, along with tools for further automation and attack management [13].
The rise of RaaS and attacker customization is fragmenting TTPs and indicators, making it harder for security teams to anticipate and defend against each unique intrusion.
While DragonForce appears to be the latest RaaS used by Scattered Spider, it is not the first, showcasing the ongoing evolution of tactics used the group.
In addition, the BlackCat RaaS strain was reportedly used by Scattered Spider for their attacks against Caesars Entertainment and MGM Resorts International [10].
In 2024 the group was also seen making use of additional RaaS strains; RansomHub and Qilin [15].
What security teams and CISOs can do to defend against Scattered Spider
The ongoing changes in tactics used by Scattered Spider, reliance on LOTL techniques, and continued adoption of evolving RaaS providers like DragonForce make it harder for organizations and their security teams to prepare their defenses against such attacks.
CISOs and security teams should implement best practices such as MFA, Single Sign-On (SSO), notifications for suspicious logins, forward logging, ethical phishing tests.
Also, given Scattered Spider’s heavy focus on social engineering, and at times using their native English fluency to their advantage, it is critical to IT and help desk teams are reminded they are possible targets.
Beyond social engineering, the threat actor is also adept at taking advantage of third-party SaaS applications in use by victims to harvest common SaaS data, such as PII and configuration data, that enable the threat actor to take on multiple identities across different domains.
With Darktrace’s Self-Learning AI, anomaly-based detection, and Autonomous Response inhibitors, businesses can halt malicious activities in real-time, whether attackers are using known TTPs or entirely new ones. Offerings such as Darktrace /Attack Surface Management enable security teams to proactively identify signs of malicious activity before it can cause an impact, while more generally Darktrace’s ActiveAI Security Platform can provide a comprehensive view of an organization’s digital estate across multiple domains.
Credit to Justin Torres (Senior Cyber Analyst), Emma Foulger (Global Threat Research Operations Lead), Zaki Al-Dhamari (Cyber Analyst), Nathaniel Jones (VP, Security & AI Strategy, FCISO), and Ryan Traill (Analyst Content Lead)
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The information provided in this blog post is for general informational purposes only and is provided "as is" without any representations or warranties, express or implied. While Darktrace makes reasonable efforts to ensure the accuracy and timeliness of the content related to cybersecurity threats such as Scattered Spider, we make no warranties or guarantees regarding the completeness, reliability, or suitability of the information for any purpose.
This blog post does not constitute professional cybersecurity advice, and should not be relied upon as such. Readers should seek guidance from qualified cybersecurity professionals or legal counsel before making any decisions or taking any actions based on the content herein.
No warranty of any kind, whether express or implied, including, but not limited to, warranties of performance, merchantability, fitness for a particular purpose, or non-infringement, is given with respect to the contents of this post.
Darktrace expressly disclaims any liability for any loss or damage arising from reliance on the information contained in this blog.
The rapid migration to cloud resources has security teams playing catch up. While they attempt to apply traditional on-prem tools to the cloud, it’s becoming increasingly clear that they are not fit for purpose. Especially in the context of forensics and incident response, the cloud presents unique complexities that demand cloud-specific solutions.
Organizations are increasingly adopting services from multiple cloud platforms (in fact, recent studies suggest 89% of organizations now operate multi-cloud environments), and container-based and serverless setups have become the norm. Security analysts already have enough on their plates; it’s unrealistic to expect them to be cloud experts too.
Why Digital Forensics and Incident Response (DFIR) roles are so hard to fill
Compounding these issues of alert fatigue and cloud complexity, there is a lack of DFIR talent. The cybersecurity skills gap is a well-known problem.
According to the 2024 ISC2 Cybersecurity Workforce Study, there is a global shortage of 4.8 million cybersecurity workers, up 19% from the previous year.
Why is this such an issue?
Highly specialized skill set: DFIR professionals need to have a deep understanding of various operating systems, network protocols, and security architectures, even more so when working in the cloud. They also need to be proficient in using a wide range of forensic tools and techniques. This level of expertise takes a lot of time and effort to develop.
Rapid technological changes: The cloud landscape is constantly changing and evolving with new services, monitoring tools, security mechanisms, and threats emerging regularly. Keeping up with these changes and staying current requires continuous learning and adaptation.
Lack of formal education and training: There are limited educational programs specifically dedicated for DFIR. Further, an industry for cloud DFIR has yet to be defined. While some universities and institutions offer courses or certifications in digital forensics, they may not cover the full spread of knowledge required in real-world incident response scenarios, especially for cloud-based environments.
High-stress nature of the job: DFIR professionals often work under tight deadlines in high-pressure situations, especially when handling security incidents. This can lead to burnout and high turnover rates in the profession.
Bridging the skills gap with usable cloud digital forensics and incident response tools
To help organizations close the DFIR skills gap, it's critical that we modernize our approaches and implement a new way of doing things in DFIR that's fit for the cloud era. Modern cloud forensics and incident response platforms must prioritize usability in order to up-level security teams. A platform that is easy to use has the power to:
Enable more advanced analysts to be more efficient and have the ability to take on more cases
Uplevel more novel analysts to perform more advanced tasks than ever before
Eliminate cloud complexity– such as the complexities introduced by multi-cloud environments and container-based and serverless setups
What to look for in cloud forensics and incident response solutions
Data enrichment: Automated correlation of collected data with threat intelligence feeds, both external and proprietary, delivers immediate insight into suspicious or malicious activities. Data enrichment expedites investigations, enabling analysts to seamlessly pivot from key events and delve deeper into the raw data.
Single timeline view: A unified perspective across various cloud platforms and data sources is crucial. A single timeline view empowers security teams to seamlessly navigate evidence based on timestamps, events, users, and more, enhancing investigative efficiency. Pulling together a timeline has historically been a very time consuming task when using traditional approaches.
Saved search: Preserving queries during investigations allows analysts to re-execute complex searches or share them with colleagues, increasing efficiency and collaboration.
Faceted search: Facet search options provide analysts with quick insights into core data attributes, facilitating efficient dataset refinement.
Cross-cloud investigations: Analyzing evidence acquired from multiple cloud providers in a single platform is crucial for security teams. A unified view and timeline across cross cloud is critical in streamlining investigations.
How Darktrace can help
Darktrace’s cloud offerings have been bolstered with the acquisition of Cado Security Ltd., which enables security teams to gain immediate access to forensic-level data in multi-cloud, container, serverless, SaaS, and on-premises environments.
Not only does Darktrace offer centralized automation solutions for cloud forensics and investigation, but it also delivers a proactive approach Cloud Detection and Response (CDR). Darktrace / CLOUD is built with advanced AI to make cloud security accessible to all security teams and SOCs. By using multiple machine learning techniques, Darktrace brings unprecedented visibility, threat detection, investigation, and incident response to hybrid and multi-cloud environments.
![Cyber AI Analyst’s detection of C2 beaconing to a teleport endpoint with hostname CUSTOMERNAME.teleport[.]sh, likely in an attempt to conceal the traffic.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68798ca3603c1ac18b3ed3d6_Screenshot%202025-07-17%20at%204.51.51%E2%80%AFPM.png)
