Fusing Vulnerability and Threat Data: Enhancing the Depth of Attack Analysis
This blog highlights how vulnerability data, collected using Cado's new vulnerability discovery feature, can be fused with threat data to help deepen the understanding of an attack, as well as guide remediation efforts.
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
Paul Bottomley
Director of Product Management, Cado
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02
Apr 2025
Cado Security, recently acquired by Darktrace, is excited to announce a significant enhancement to its data collection capabilities, with the addition of a vulnerability discovery feature for Linux-based cloud resources. According to Darktrace’s Annual Threat Report 2024, the most significant campaigns observed in 2024 involved the ongoing exploitation of significant vulnerabilities in internet-facing systems. Cado’s new vulnerability discovery capability further deepens its ability to provide extensive context to security teams, enabling them to make informed decisions about threats, faster than ever.
Deep context to accelerate understanding and remediation
Context is critical when understanding the circumstances surrounding a threat. It can also take many forms – alert data, telemetry, file content, business context (for example asset criticality, core function of the resource), and risk context, such as open vulnerabilities.
When performing an investigation, it is common practice to understand the risk profile of the resource impacted, specifically determining open vulnerabilities and how they may relate to the threat. For example, if an analyst is triaging an alert related to an internet-facing Webserver running Apache, it would greatly benefit the analyst to understand open vulnerabilities in the Apache version that is running, if any of them are exploitable, whether a fix is available, etc. This dataset also serves as an invaluable source when developing a remediation plan, identifying specific vulnerabilities to be prioritised for patching.
Data acquisition in Cado
Cado is the only platform with the ability to perform full forensic captures as well as utilize instant triage collection methods, which is why fusing host-based artifact data with vulnerability data is such an exciting and compelling development.
The vulnerability discovery feature can be run as part of an acquisition – full or triage – as well as independently using a fast ‘Scan only’ mode.
Figure 1: A fast vulnerability scan being performed on the acquired evidence
Once the acquisition has completed, the user will have access to a ‘Vulnerabilities’ table within their investigation, where they are able to view and filter open vulnerabilities (by Severity, CVE ID, Resource, and other properties), as well as pivot to the full Event Timeline. In the Event Timeline, the user will be able to identify whether there is any malicious, suspicious or other interesting activity surrounding the vulnerable package, given the unified timeline presents a complete chronological dataset of all evidence and context collected.
Figure 2: Vulnerabilities discovered on the acquired evidence
Figure 3: Pivot from the Vulnerabilities table to the Event Timeline provides an in-depth view of file and process data associated with the vulnerable package selected. In this example, Apache2.
Future work
In the coming months, we’ll be releasing initial versions of highly anticipated integrations between Cado and Darktrace, including the ability to ingest Darktrace / CLOUD alerts which will automatically trigger a forensic capture (as well as a vulnerability discovery) of the impacted assets.
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 Security Evolution: Why Security Teams are Taking the Lead
While many internal teams contribute to general cloud hygiene, the security team has increasingly taken the lead on cloud security. Learn how AI-powered cloud detection and response tools can help these teams with new responsibilities.
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
From Containment to Remediation: Darktrace / CLOUD & Cado Reducing MTTR
Darktrace / CLOUD combines with Cado’s automated forensics capture to achieve rapid containment and deep investigative capabilities. Learn more about accelerating MTTR here.
CNAPP Alone Isn’t Enough: Focusing on CDR for Real-Time Cross Domain Protection
This blog dives into the strengths and limitations of CNAPP, explaining how a CDR solution can enhance cloud security to identify and mitigate cross-domain threats.
As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.
Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.
This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.
Threat actors see local security agents as targets
Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.
The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.
With the local EDR agent silently disabled or evaded, how will the threat be discovered?
What are the limitations of relying solely on EDR?
Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.
In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:
“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”
This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.
Why is network detection & response (NDR) critical?
An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.
An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.
Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK
On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.
An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.
Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.
Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.
Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.
This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.
Unlocking adaptive protection for evolving cyber risks
Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.
With its ability to uncover previously unknown threats as well as detect known threats using signatures and threat intelligence, Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.
Stay tuned for part II of this series which dives deeper into the differences between NDR types.
Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.
Obfuscation Overdrive: Next-Gen Cryptojacking with Layers
Out of all the services honeypotted by Darktrace, Docker is the most commonly attacked, with new strains of malware emerging daily. This blog will analyze a novel malware campaign with a unique obfuscation technique and a new cryptojacking technique.
What is obfuscation?
Obfuscation is a common technique employed by threat actors to prevent signature-based detection of their code, and to make analysis more difficult. This novel campaign uses an interesting technique of obfuscating its payload.
Docker image analysis
The attack begins with a request to launch a container from Docker Hub, specifically the kazutod/tene:ten image. Using Docker Hub’s layer viewer, an analyst can quickly identify what the container is designed to do. In this case, the container is designed to run the ten.py script which is built into itself.
Figure 1: Docker Hub Image Layers, referencing the script ten.py.
To gain more information on the Python file, Docker’s built in tooling can be used to download the image (docker pull kazutod/tene:ten) and then save it into a format that is easier to work with (docker image save kazutod/tene:ten -o tene.tar). It can then be extracted as a regular tar file for further investigation.
Figure 2: Extraction of the resulting tar file.
The Docker image uses the OCI format, which is a little different to a regular file system. Instead of having a static folder of files, the image consists of layers. Indeed, when running the file command over the sha256 directory, each layer is shown as a tar file, along with a JSON metadata file.
Figure 3: Output of the file command over the sha256 directory.
As the detailed layers are not necessary for analysis, a single command can be used to extract all of them into a single directory, recreating what the container file system would look like:
find blobs/sha256 -type f -exec sh -c 'file "{}" | grep -q "tar archive" && tar -xf "{}" -C root_dir' \;
Figure 4: Result of running the command above.
The find command can then be used to quickly locate where the ten.py script is.
find root_dir -name ten.py
root_dir/app/ten.py
Figure 5: Details of the above ten.py script.
This may look complicated at first glance, however after breaking it down, it is fairly simple. The script defines a lambda function (effectively a variable that contains executable code) and runs zlib decompress on the output of base64 decode, which is run on the reversed input. The script then runs the lambda function with an input of the base64 string, and then passes it to exec, which runs the decoded string as Python code.
To help illustrate this, the code can be cleaned up to this simplified function:
decoded_string = decode(the_big_text_blob) exec(decoded_string) # run the decoded string
This can then be set up as a recipe in Cyberchef, an online tool for data manipulation, to decode it.
Figure 6: Use of Cyberchef to decode the ten.py script.
The decoded payload calls the decode function again and puts the output into exec. Copy and pasting the new payload into the input shows that it does this another time. Instead of copy-pasting the output into the input all day, a quick script can be used to decode this.
The script below uses the decode function from earlier in order to decode the base64 data and then uses some simple string manipulation to get to the next payload. The script will run this over and over until something interesting happens.
# Decode the initial base64
decoded = decode(initial) # Remove the first 11 characters and last 3
# so we just have the next base64 string
clamped = decoded[11:-3]
for i in range(1, 100): # Decode the new payload
decoded = decode(clamped) # Print it with the current step so we
# can see what’s going on
print(f"Step {i}")
print(decoded) # Fetch the next base64 string from the
# output, so the next loop iteration will
# decode it
clamped = decoded[11:-3]
Figure 7: Result of the 63rd iteration of this script.
After 63 iterations, the script returns actual code, accompanied by an error from the decode function as a stopping condition was never defined. It not clear what the attacker’s motive to perform so many layers of obfuscation was, as one round of obfuscation versus several likely would not make any meaningful difference to bypassing signature analysis. It’s possible this is an attempt to stop analysts or other hackers from reverse engineering the code. However, it took a matter of minutes to thwart their efforts.
Cryptojacking 2.0?
Figure 8: Cleaned up version of the de-obfuscated code.
The cleaned up code indicates that the malware attempts to set up a connection to teneo[.]pro, which appears to belong to a Web3 startup company.
Teneo appears to be a legitimate company, with Crunchbase reporting that they have raised USD 3 million as part of their seed round [1]. Their service allows users to join a decentralized network, to “make sure their data benefits you” [2]. Practically, their node functions as a distributed social media scraper. In exchange for doing so, users are rewarded with “Teneo Points”, which are a private crypto token.
The malware script simply connects to the websocket and sends keep-alive pings in order to gain more points from Teneo and does not do any actual scraping. Based on the website, most of the rewards are gated behind the number of heartbeats performed, which is likely why this works [2].
Checking out the attacker’s dockerhub profile, this sort of attack seems to be their modus operandi. The most recent container runs an instance of the nexus network client, which is a project to perform distributed zero-knowledge compute tasks in exchange for cryptocurrency.
Typically, traditional cryptojacking attacks rely on using XMRig to directly mine cryptocurrency, however as XMRig is highly detected, attackers are shifting to alternative methods of generating crypto. Whether this is more profitable remains to be seen. There is not currently an easy way to determine the earnings of the attackers due to the more “closed” nature of the private tokens. Translating a user ID to a wallet address does not appear to be possible, and there is limited public information about the tokens themselves. For example, the Teneo token is listed as “preview only” on CoinGecko, with no price information available.
Conclusion
This blog explores an example of Python obfuscation and how to unravel it. Obfuscation remains a ubiquitous technique employed by the majority of malware to aid in detection/defense evasion and being able to de-obfuscate code is an important skill for analysts to possess.
We have also seen this new avenue of cryptominers being deployed, demonstrating that attackers’ techniques are still evolving - even tried and tested fields. The illegitimate use of legitimate tools to obtain rewards is an increasingly common vector. For example, as has been previously documented, 9hits has been used maliciously to earn rewards for the attack in a similar fashion.
Docker remains a highly targeted service, and system administrators need to take steps to ensure it is secure. In general, Docker should never be exposed to the wider internet unless absolutely necessary, and if it is necessary both authentication and firewalling should be employed to ensure only authorized users are able to access the service. Attacks happen every minute, and even leaving the service open for a short period of time may result in a serious compromise.