Forensic Victory: Catching the Ransomware EDR Couldn't See
This blog details a simulation of a ransomware attack that bypassed EDR, simulated via a ClickFix social engineering technique. The attack used an obfuscated HTML and custom C++ binary to encrypt files and establish a reverse shell. Cado's forensic platform then demonstrated how to trace the attack chain, highlighting the need for robust DFIR beyond EDR.
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
Nate Bill
Threat Researcher
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13
Feb 2025
Introduction: Catching the ransomware EDR couldn't see
Endpoint Detection & Response (EDR) is frequently used by organizations as the first line of defense against cyberattacks. EDR platforms monitor organizations’ endpoints (servers, employee laptops, etc.) and detect and contain malicious activity running where possible. This blog will explore a ransomware attack in a lab environment, using payloads inspired from real attacks.
The incident
For this experiment, Cado Security Labs (now part of Darktrace) set up an up-to-date Windows machine, with a mainstream EDR tool installed, and simulated a ClickFix attack [1] against the user, which relies on socially engineering the user into running malicious commands.
During the first stage of the attack, the fake end user receives a phishing email with a ClickFix attachment:
Figure 1: Test Email
As this is a test, the email was kept fairly short. However, an attacker in a real-world setting would make the email far more convincing to view. In the real world, this type of attack is often seen being used with fake invoices being sent to finance staff.
After opening up the HTML, the end user is presented with the following page:
Figure 2: The ClickFix HTML the user is presented with as part of our simulated attack
This is taken from a real attack where a Microsoft Word online page is mimicked, prompting the user to interact with it. The user needs to interact with the button, as most browsers will block clipboard writes unless the user has interacted with an element. Clicking the button copies a command to the user’s clipboard, and updates the instructions to tell them to press Win + R, Ctrl + V, and then Enter. If the user does this, it will open the run dialog, paste in the command, and execute it. This approach capitalizes on the typical user's lack of comprehension or uncritical adherence to directives, a tactic that has demonstrated efficacy in real-world cyberattacks.
It is worth noting that the EDR tool flagged this stage during initial testing. However, adding a layer of obfuscation to the HTML allowed for bypass detection. The page was able to be encoded, decoded and then written to the document using reflection to access methods that would normally be flagged.
Once the command is executed, PowerShell is invoked to download and run an .exe file from an attacker-controlled server.
The payload is a custom C++ binary that was developed for the purpose of this test. The binary spawns a reverse shell, as well as encrypting all of the files in the Documents folder for ransom. This binary was iteratively tested against the EDR tool, and the functionality was tweaked each time to bypass elements that were getting detected. Bypassing the EDR tool did not require any fancy techniques. Simply using a different Windows API to accomplish a goal that was previously flagged by the EDR tool, or altering the behavior, timing, and ordering of activities performed was sufficient to evade detection. This may seem surprising that sophisticated techniques aren’t strictly required to be undetected.
The aftermath of the attack can be seen in the images below, with a ransom note being written, and our important documents no longer being readable.
Figure 3: The Ransom Note
Figure 4: The aftermath of trying to open one of the PDFs
With no alerts to investigate from the EDR tool - how could a blue team uncover this attack chain after the fact for incident response?
Investigating the artifacts with cado
Using Cado (acquired by Darktrace), we can import the affected VM directly with just a few clicks.
Figure 5: Import the affect VM
The ransom note is a good starting point for the investigation. The timeline search feature quickly finds entries that show what process made the readme.txt file.
Figure 6: Timeline search feature
It shows that the ransom note was created by the process fix.exe, which can be used to pivot off and build a better understanding of what else the malware did, and how it got onto the system.
Reviewing events relating to the fix.exe payload shows that an event established a connection to a server, in this case, an attacker-controlled C2 server. It also spawned a command prompt instance, which provides a remote shell to the attacker.
Figure 7: Event Information
Figure 8: Event Information showing ransomware
Looking at the event information, it’s easy to spot the ransom attacks against the files. For example, the ransom attack modified the internal_draft_important.pdf document, which was seen before it can no longer be opened.
Figure 9: Event information showing the modified document
And finally reaching the start of the log trail relating to the payload, it shows it initially being executed by PowerShell.
Figure 10: Event information showing PowerShell
However, this does not definitively show what caused the malware to run in the first place, and so the next step is running the pivot feature to find related events.
Pivoting off the event allows for quickly figuring out this was precipitated by a visit to obfuscated.html, which was downloaded from an email in Outlook online:
Figure 11: Related events showing that the attack was precipiated by a visit to a obfuscated.html
The Cado Platform [2] also allows for directly jumping to the file in the file browser to conduct further analysis:
Figure 12: File seen in file browser
An EDR platform usually only provides an alert, process snapshot, and event details for a singular moment in time, missing the vital context needed to successfully understand the attack. Cado provides the vital context needed to successfully understand the full scope of the attack, not just its entry point.
Key takeaways
This research covered how Cado can provide the ability to forensically analyze systems and fully understand how attacks have occurred and unfolded. Defense-in-depth is a core component of cybersecurity, and being entirely reliant on an EDR platform as your only line of defense and insight into attacks can leave you without full context.
This was an example only, and a finely tuned EDR platform would likely detect an attack similar to this. However, many organizations may overlook the forensics side of Digital Forensics and Incident Response [3], and remediate incidents solely using their EDR platform. This can result in organizations missing out on the complete picture of an attack, potentially leaving them open to re-infection. A DFIR platform is vital to respond quickly to incidents across Cloud, SaaS, and on-prem.
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.
Cloud has changed everything, but investigations haven’t kept up. With breaches hitting cloud data and attackers moving faster than ever, legacy forensics are too slow, too manual, and too fragmented. It’s time for a cloud-first approach: automated, unified, and built for today’s speed of attack.
Unpacking the Salesloft Incident: Insights from Darktrace Observations
Darktrace identified anomalous activity within its customer base linked to a supply chain attack exploiting Salesloft integrations in August 2025. Learn about the campaign and how Darktrace detects and responds to such threats using AI-driven threat detection and Autonomous Response capabilities.
How Organizations are Addressing Cloud Investigation and Response
The importance of cloud investigation and incident response are compounded by an expanded attack surface in the cloud, lack of advanced tooling to upskill teams, and increasing regulatory pressure from compliance regulations. This blog dives into these challenges and explores potential solutions for security teams attempting to secure their cloud environment
Cloud adoption has revolutionized how businesses operate, offering speed, scalability, and flexibility. But for security teams, this transformation has introduced a new set of challenges, especially when it comes to incident response (IR) and forensic investigations.
Cloud-related breaches are skyrocketing – 82% of breaches now involve cloud-stored data (IBM Cost of a Data Breach, 2023). Yet incidents often go unnoticed for days: according to a 2025 report by Cybersecurity Insiders, of the 65% of organizations experienced a cloud-related incident in the past year, only 9% detected it within the first hour, and 62% took more than 24 hours to remediate it (Cybersecurity Insiders, Cloud Security Report 2025).
Despite the shift to cloud, many investigation practices remain rooted in legacy on-prem approaches. According to a recent report, 65% of organizations spend approximately 3-5 days longer when investigating an incident in the cloud vs. on premises.
Cloud investigations must evolve, or risk falling behind attackers who are already exploiting the cloud’s speed and complexity.
4 Reasons Cloud Investigations Are Broken
The cloud’s dynamic nature – with its ephemeral workloads and distributed architecture – has outpaced traditional incident response methods. What worked in static, on-prem environments simply doesn’t translate.
Here’s why:
Ephemeral workloads Containers and serverless functions can spin up and vanish in minutes. Attackers know this as well – they’re exploiting short-lived assets for “hit-and-run” attacks, leaving almost no forensic footprint. If you’re relying on scheduled scans or manual evidence collection, you’re already too late.
Fragmented tooling Each cloud provider has its own logs, APIs, and investigation workflows. In addition, not all logs are enabled by default, cloud providers typically limit the scope of their logs (both in terms of what data they collect and how long they retain it), and some logs are only available through undocumented APIs. This creates siloed views of attacker activity, making it difficult to piece together a coherent timeline. Now layer in SaaS apps, Kubernetes clusters, and shadow IT — suddenly you’re stitching together 20+ tools just to find out what happened. Analysts call it the ‘swivel-chair Olympics,’ and it’s burning hours they don’t have.
SOC overload Analysts spend the bulk of their time manually gathering evidence and correlating logs rather than responding to threats. This slows down investigations and increases burnout. SOC teams are drowning in noise; they receive thousands of alerts a day, the majority of which never get touched. False positives eat hundreds of hours a month, and consequently burnout is rife.
Cost of delay The longer an investigation takes, the higher its cost. Breaches contained in under 200 days save an average of over $1M compared to those that linger (IBM Cost of a Data Breach 2025).
These challenges create a dangerous gap for threat actors to exploit. By the time evidence is collected, attackers may have already accessed or exfiltrated data, or entrenched themselves deeper into your environment.
What’s Needed: A New Approach to Cloud Investigations
It’s time to ditch the manual, reactive grind and embrace investigations that are automated, proactive, and built for the world you actually defend. Here’s what the next generation of cloud forensics must deliver:
Automated evidence acquisition Capture forensic-level data the moment a threat is detected and before assets disappear.
Unified multi-cloud visibility Stitch together logs, timelines, and context across AWS, Azure, GCP, and hybrid environments into a single unified view of the investigation.
Accelerated investigation workflows Reduce time-to-insight from hours or days to minutes with automated analysis of forensic data, enabling faster containment and recovery.
Empowered SOC teams Fully contextualised data and collaboration workflows between teams in the SOC ensure seamless handover, freeing up analysts from manual collection tasks so they can focus on what matters: analysis and response.
Attackers are already leveraging the cloud’s agility. Defenders must do the same — adopting solutions that match the speed and scale of modern infrastructure.
Cloud Changed Everything. It’s Time to Change Investigations.
The cloud fundamentally reshaped how businesses operate. It’s time for security teams to rethink how they investigate threats.
Forensics can no longer be slow, manual, and reactive. It must be instant, automated, and cloud-first — designed to meet the demands of ephemeral infrastructure and multi-cloud complexity.
The future of incident response isn’t just faster. It’s smarter, more scalable, and built for the environments we defend today, not those of ten years ago.
Darktrace's latest investigation uncovered a novel campaign that blends traditional malware with modern devops technology.
At the center of this campaign is a Python-based command-and-control (C2) framework hosted on GitHub CodeSpaces. This campaign also utilizes a Python based spreader with a multi-stage Docker deployment as the initial access vector.
The campaign further makes use of a Go-based Remote Access Trojan (RAT) that implements a RESTful registration and polling mechanism, enabling command execution and communication with its operators.
ShadowV2 attack techniques
What sets this campaign apart is the sophistication of its attack toolkit.
The threat actors employ advanced methods such as HTTP/2 rapid reset, a Cloudflare under attack mode (UAM) bypass, and large-scale HTTP floods, demonstrating a capability to combine distributed denial-of-service (DDoS) techniques with targeted exploitation.
With the inclusion of an OpenAPI specification, implemented with FastAPI and Pydantic and a fully developed login panel and operator interface, the infrastructure seems to resemble a “DDoS-as-a-service” platform rather than a traditional botnet, showing the extent to which modern malware increasingly mirrors legitimate cloud-native applications in both design and usability.
Analysis of a SadowV2 attack
Initial access
The initial compromise originates from a Python script hosted on GitHub CodeSpaces. This can be inferred from the observed headers:
User-Agent: docker-sdk-python/7.1.0
X-Meta-Source-Client: github/codespaces
The user agent shows that the attacker is using the Python Docker SDK, a library for Python programs that allows them to interact with Docker to create containers. The X-Meta-Source-Client appears to have been injected by GitHub into the request to allow for attribution, although there is no documentation online about this header.
The IP the connections originate from is 23.97.62[.]139, which is a Microsoft IP based in Singapore. This aligns with expectations as GitHub is owned by Microsoft.
This campaign targets exposed Docker daemons, specifically those running on AWS EC2. Darktrace runs a number of honeypots across multiple cloud providers and has only observed attacks against honeypots running on AWS EC2. By default, Docker is not accessible to the Internet, however, can be configured to allow external access. This can be useful for managing complex deployments where remote access to the Docker API is needed.
Typically, most campaigns targeting Docker will either take an existing image from Docker Hub and deploy their tools within it, or upload their own pre-prepared image to deploy. This campaign works slightly differently; it first spawns a generic “setup” container and installs a number of tools within it. This container is then imaged and deployed as a live container with the malware arguments passed in via environmental variables.
Figure 1: Attacker creates a blank container from an Ubuntu image.
Figure 2: Attacker sets up their tools for the attack.
Figure 3: Attacker deploys a new container using the image from the setup container.
It is unclear why the attackers chose this approach - one possibility is that the actor is attempting to avoid inadvertently leaving forensic artifacts by performing the build on the victim machine, rather than building it themselves and uploading it.
Malware analysis
The Docker container acts as a wrapper around a single binary, dropped in /app/deployment. This is an ELF binary written in Go, a popular choice for modern malware. Helpfully, the binary is unstripped, making analysis significantly easier.
The current version of the malware has not been reported by OSINT providers such as VirusTotal. Using the domain name from the MASTER_ADDR variable and other IoCs, we were able to locate two older versions of the malware that were submitted to VirusTotal on the June 25 and July 30 respectively [1] [2]. Neither of these had any detections and were only submitted once each using the web portal from the US and Canada respectively. Darktrace first observed the attack against its honeypot on June 24, so it could be a victim of this campaign submitting the malware to VirusTotal. Due to the proximity of the start of the attacks, it could also be the attacker testing for detections, however it is not possible to know for certain.
The malware begins by phoning home, using the MASTER_ADDR and VPS_NAME identifiers passed in from the Docker run environmental variables. In addition, the malware derives a unique VPS_ID, which is the VPS_NAME concatenated with the current unix timestamp. The VPS_ID is used for all communications with the C2 server as the identifier for the specific implant. If the malware is restarted, or the victim is re-infected, the C2 server will inform the implant of its original VPS_ID to ensure continuity.
Figure 4: Snippet that performs the registration by sending a POST request to the C2 API with a JSON structure.
From there, the malware then spawns two main loops that will remain active for the lifetime of the implant. Every second, it sends a heartbeat to the C2 by sending the VPS_ID to hxxps://shadow.aurozacloud[.]xyz/api/vps/heartbeat via POST request. Every 5 seconds, it retrieves hxxps://shadow.aurozacloud[.]xyz/api/vps/poll/<VPS ID> via a GET request to poll for new commands.
Figure 5: The poll mechanism.
At this stage, Darktrace security researchers wrote a custom client that ran on the server infected by the attacker that mimicked their implant. The goal was to intercept commands from the C2. Based on this, it was observed initiating an attack against chache08[.]werkecdn[.]me using a 120 thread HTTP2 rapid reset attack. This site appears to be hosted on an Amsterdam VPS provided by FDCServers, a server hosting company. It was not possible to identify what normally runs on this site, as it returns a 403 Forbidden error when visited.
Darktrace’s code analysis found that the returned commands contain the following fields:
Method (e.g. GET, POST)
A unique ID for the attack
A URL endpoint used to report attack statistics
The target URL & port
The duration of the attack
The number of threads to use
An optional proxy to send HTTP requests through
The malware then spins up several threads, each running a configurable number of HTTP clients using Valyala’s fasthttp library, an open source Go library for making high-performance HTTP requests. After this is complete, it uses these clients to perform an HTTP flood attack against the target.
Figure 6: A snippet showing the fasthttp client creation loop, as well as a function to report the worker count back to the C2.
In addition, it also features several flags to enable different bypass mechanisms to augment the malware:
WordPress bypass (does not appear to be implemented - the flag is not used anywhere)
Random query strings appended to the URL
Spoofed forwarding headers with random IP addresses
Cloudflare under-attack-mode (UAM) bypass
HTTP2 rapid reset
The most interesting of these is the Cloudflare UAM bypass mechanism. When this is enabled, the malware will attempt to use a bundled ChromeDP binary to solve the Cloudflare JavaScript challenge that is presented to new visitors. If this succeeds, the clearance cookie obtained is then included in subsequent requests. This is unlikely to work in most cases as headless Chrome browsers are often flagged, and a regular CAPTCHA is instead served.
Figure 7: The UAM bypass success snippet.
Additionally, the malware has a flag to enable an HTTP2 rapid reset attack mode instead of a regular HTTP flood. In HTTP2, a client can create thousands of requests within a single connection using multiplexing, allowing sites to load faster. The number of request streams per connection is capped however, so in a rapid reset attack many requests are made and then immediately cancelled to allow more requests to be created. This allows a single client to execute vastly more requests per second and use more server resources than it otherwise would, allowing for more effective denial-of-service (DoS) attacks.
Figure 8: The HTTP2 rapid reset snippet from the main attack function.
API/C2 analysis
As mentioned throughout the malware analysis section, the malware communicates with a C2 server using HTTP. The server is behind Cloudflare, which obscures its hosting location and prevents analysis. However, based on analysis of the spreader, it's likely running on GitHub CodeSpaces.
When sending a malformed request to the API, an error generated by the Pydantic library is returned:
This shows they are using Python for the API, which is the same language that the spreader is written in.
One of the larger frameworks that ships with Pydantic is FastAPI, which also ships with Swagger. The malware author left this publicly exposed, and Darktrace’s researchers were able to obtain a copy of their API documentation. The author appears to have noticed this however, as subsequent attempts to access it now returns a HTTP 404 Not Found error.
Figure 9: Swagger UI view based on the obtained OpenAPI spec.
This is useful to have as it shows all the API endpoints, including the exact fields they take and return, along with comments on each endpoint written by the attacker themselves.
It is very likely a DDoS for hire platform (or at the very least, designed for multi-tenant use) based on the extensive user API, which features authentication, distinctions between privilege level (admin vs user), and limitations on what types of attack a user can execute. The screenshot below shows the admin-only user create endpoint, with the default limits.
Figure 10: The admin-only user create endpoint.
The endpoint used to launch attacks can also be seen, which lines up with the options previously seen in the malware itself. Interestingly, this endpoint requires a list of zombie systems to launch the attack from. This is unusual as most DDoS for hire services will decide this internally or just launch the attack from every infected host (zombie). No endpoints that returned a list of zombies were found, however, it’s possible one exists as the return types are not documented for all the API endpoints.
Figure 11: The attack start endpoint.
There is also an endpoint to manage a blacklist of hosts that cannot be attacked. This could be to stop users from launching attacks against sites operated by the malware author, however it’s also possible the author could be attempting to sell protection to victims, which has been seen previously with other DDoS for hire services.
Figure 12: Blacklist endpoints.
Attempting to visit shadow[.]aurozacloud[.]xyz results in a seizure notice. It is most likely fake the same backend is still in use and all of the API endpoints continue to work. Appending /login to the end of the path instead brings up the login screen for the DDoS platform. It describes itself as an “advanced attack platform”, which highlights that it is almost certainly a DDoS for hire service. The UI is high quality, written in Tailwind, and even features animations.
Figure 13: The fake seizure notice.
Figure 14: The login UI at /login.
Conclusion
By leveraging containerization, an extensive API, and with a full user interface, this campaign shows the continued development of cybercrime-as-a-service. The ability to deliver modular functionality through a Go-based RAT and expose a structured API for operator interaction highlights how sophisticated some threat actors are.
For defenders, the implications are significant. Effective defense requires deep visibility into containerized environments, continuous monitoring of cloud workloads, and behavioral analytics capable of identifying anomalous API usage and container orchestration patterns. The presence of a DDoS-as-a-service panel with full user functionality further emphasizes the need for defenders to think of these campaigns not as isolated tools but as evolving platforms.
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Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.
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