February 11, 2025

Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

Discover how Darktrace detected and responded to cyberattacks using Living-off-the-Land (LOTL) tactics to exploit trusted services and tools on customer networks.

Written by

Alexandra Sentenac

Cyber Analyst

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.

Written by

Alexandra Sentenac

Cyber Analyst

Feb 2025

What is living-off-the-land?

Threat actors employ a variety of techniques to compromise target networks, including exploiting unpatched vulnerabilities, abusing misconfigurations, deploying backdoors, and creating custom malware. However, these methods generate a lot of noise and are relatively easy for network and host-based monitoring tools to detect, especially once indicators of compromise (IoCs) and tactics, techniques, and procedures (TTPs) are published by the cybersecurity community.

Living-off-the-Land (LOTL) techniques, however, allow attacks to remain nearly invisible to Endpoint Detection and Response (EDR) tools – leveraging trusted protocols, applications and native systems to carry out malicious activity. While mitigations exist, they are often poorly implemented. The Cybersecurity and Infrastructure Security Agency (CISA) found that some organizations “lacked security baselines, allowing [Living-off-the-Land binaries (LOLBins)] to execute and leaving analysts unable to identify anomalous activity” and “organizations did not appropriately tune their detection tools to reduce alert noise, leading to an unmanageable level of alerts to sift through and action" [1].

Darktrace / NETWORK addresses this challenge across Information Technology (IT), Operational Technology (OT), and cloud environments by continuously analyzing network traffic and identifying deviations from normal behavior with its multi-layered AI – helping organizations detect and respond to LOTL attacks in real time.

Darktrace’s detection of LOTL attacks

This blog will review two separate attacks detected by Darktrace that leveraged LOTL techniques at several stages of the intrusion.

Case A

Reconnaissance

In September 2024, a malicious actor gained access to a customer network via their Virtual Private Network (VPN) from two desktop devices that had no prior connection history. Over two days, the attacker conducted multiple network scans, targeting ports associated with Remote Desktop Protocol (RDP) and NTLM authentication. Darktrace detected this unusual activity, triggering multiple alerts for scanning and enumeration activity.

Unusual NTLM authentication attempts using default accounts like “Guest” and “Administrator” were detected. Two days after the initial intrusion, suspicious DRSGetNCChanges requests were observed on multiple domain controllers (DCs), targeting the Directory Replication Service RPC interface (i.e., drsuapi) – a technique used to extract account hashes from DCs. This process can be automated using tools like Mimikatz's DcSync and DCShadow

Around the same time, attacker-controlled devices were seen presenting an admin credential and another credential potentially granting access to Cisco Firewall systems, suggesting successful privilege escalation. Due to the severity of this activity, Darktrace’s Autonomous Response was triggered to prevent the device from further deviation from its normal behavior. However, because Autonomous Response was configured in Human Confirmation mode, the response actions had to be manually applied by the customer.

Figure 1: Cyber AI Analyst Critical Incident showing the unusual DRSGetNCChanges requests following unusual scanning activity.

Lateral movement

Darktrace also detected anomalous RDP connections to domain controllers, originating from an attacker-controlled device using admin and service credentials. The attacker then successfully pivoted to a likely RDP server, leveraging the RDP protocol – one of the most commonly used for lateral movement in network compromises observed by Darktrace.

Tooling

Following an incoming RDP connection, one of the DCs made a successful GET request to the URI '/download/122.dll' on the 100% rare IP, 146.70.145[.]189. The request returned an executable file, which open-source intelligence (OSINT) suggests is likely a CobaltStrike C2 sever payload [2] [3]. Had Autonomous Response been enabled here, it would have blocked all outgoing traffic from the DC allowing the customer to investigate and remediate.

Additionally, Darktrace detected a suspicious CreateServiceW request to the Service Control (SVCCTL) RPC interface on a server. The request executed commands using ‘cmd.exe’ to perform the following actions

Used ‘tasklist’ to filter processes named ”lsass.exe” (Local Security Authority Subsystem Service) to find its specific process ID.
Used “rundll32.exe” to execute the MiniDump function from the “comsvcs.dll” library, creating a memory dump of the “lsass.exe” process.
Saved the output to a PNG file in a temporary folder,

Notably, “cmd.exe” was referenced as “CMd.EXE” within the script, likely an attempt to evade detection by security tools monitoring for specific keywords and patterns.

Figure 3: Model Alert Log showing the unusual SVCCTL create request.

Over the course of three days, this activity triggered around 125 Darktrace / NETWORK alerts across 11 internal devices. In addition, Cyber AI Analyst launched an autonomous investigation into the activity, analyzing and connecting 16 separate events spanning multiple stages of the cyber kill chain - from initial reconnaissance to payload retrieval and lateral movement.

Darktrace’s comprehensive detection enabled the customer’s security team to remediate the compromise before any further escalation was observed.

Case B

Between late 2023 and early 2024, Darktrace identified a widespread attack that combined insider and external threats, leveraging multiple LOTL tools for reconnaissance and lateral movement within a customer's network.

Reconnaissance

Initially, Darktrace detected the use of a new administrative credential by a device, which then made unusual RDP connections to multiple internal systems, including a 30-minute connection to a DC. Throughout the attack, multiple unusual RDP connections using the new administrative credential “%admin!!!” were observed, indicating that this protocol was leveraged for lateral movement.

The next day, a Microsoft Defender Security Integration alert was triggered on the device due to suspicious Windows Local Security Authority Subsystem Service (LSASS) credential dump behavior. Since the LSASS process memory can store operating system and domain admin credentials, obtaining this sensitive information can greatly facilitate lateral movement within a network using legitimate tools such as PsExec or Windows Management Instrumentation (WMI) [4]. Security integrations with other security vendors like this one can provide insights into host-based processes, which are typically outside of Darktrace’s coverage. Darktrace’s anomaly detection and network activity monitoring help prioritize the investigation of these alerts.

Three days later, the attacker was observed logging into the DC and querying tickets for the Lightweight Directory Access Protocol (LDAP) service using the default credential “Administrator.” This activity, considered new by Darktrace, triggered an Autonomous Response action that blocked further connections on Kerberos port 88 to the DC. LDAP provides a central location to access and manage data about computers, servers, users, groups, and policies within a network. LDAP enumeration can provide valuable Active Directory (AD) object information to an attacker, which can be used to identify critical attack paths or accounts with high privileges.

Lateral movement

Following the incoming RDP connection, the DC began scanning activities, including RDP and Server Block Message (SMB) services, suggesting the attacker was using remote access for additional reconnaissance. Outgoing RDP connection attempts to over 100 internal devices were observed, with around 5% being successful, highlighting the importance of this protocol for the threat actor’s lateral movement.

Around the same time, the DC made WMI, PsExec, and service control connections to two other DCs, indicating further lateral movement using native administrative protocols and tools. These functions can be leveraged by attackers to query system information, run malicious code, and maintain persistent access to compromised devices while avoiding traditional security tool alarms. In this case, requested services included the IWbemServices (used to access WMI services) and IWbemFetchSmartEnum (used to retrieve a network-optimized enumerator interface) interfaces, with ExecQuery operations detected for the former. This method returns an enumerable collection of IWbemClassObject interface objects based on a query.

Additionally, unusual Windows Remote Management (WinRM) connections to another domain controller were observed. WinRM is a Microsoft protocol that allows systems to exchange and access management information over HTTP(S) across a network, such as running executables or modifying the registry and services.

Figure 4: Cyber AI Analyst Incident showing unusual WMI activity between the two DCs.

The DC was also detected writing the file “PSEXESVC.exe” to the “ADMIN$” share of another internal device over the SMB file transfer network protocol. This activity was flagged as highly unusual by Darktrace, as these two devices had not previously engaged in this type of SMB connectivity.

It is rare for an attacker to immediately find the information or systems they are after, making it likely they will need to move around the network before achieving their objectives. Tools such as PsExec enable attackers to do this while largely remaining under the radar. With PsExec, attackers who gain access to a single system can connect to and execute commands remotely on other internal systems, access sensitive information, and spread their attack further into the environment.

Figure 5. Model Alert Event Log showing the new write of the file “PSEXESVC.exe” by one of the compromised devices over an SMB connection initiated at an unusual time.

Darktrace further observed the DC connecting to the SVCCTL endpoint on a remote device and performing the CreateServiceW operation, which was flagged as highly unusual based on previous behavior patterns between the two devices. Additionally, new ChangeServiceConfigW operations were observed from another device.

Aside from IWbemServices requests seen on multiple devices, Darktrace also detected multiple internal devices connecting to the ITaskSchedulerService interface over DCE-RPC and performing new SchRpcRegisterTask operations, which register a task on the destination system. Attackers can exploit the task scheduler to facilitate the initial or recurring execution of malicious code by a trusted system process, often with elevated permissions. The creation of these tasks was considered new or highly unusual and triggered several anomalous ITaskScheduler activity alerts.

Conclusion

As pointed out by CISA, threat actors frequently exploit the lack of implemented controls on their target networks, as demonstrated in the incidents discussed here. In the first case, VPN access was granted to all domain users, providing the attacker with a point of entry. In the second case, there were no restrictions on the use of RDP within the targeted network segment, allowing the attackers to pivot from device to device.

Darktrace assists security teams in monitoring for unusual use of LOTL tools and protocols that can be leveraged by threat actors to achieve a wide range of objectives. Darktrace’s Self-Learning AI sifts through the network traffic noise generated by these trusted tools, which are essential to administrators and developers in their daily tasks, and highlights any anomalous and potentially unexpected use.

Credit to Alexandra Sentenac (Senior Cyber Analyst) and Ryan Traill (Analyst Content Lead)

References

[1] https://www.cisa.gov/sites/default/files/2024-02/Joint-Guidance-Identifying-and-Mitigating-LOTL_V3508c.pdf

[2] https://www.virustotal.com/gui/ip-address/146.70.145.189/community

[3] https://www.virustotal.com/gui/file/cc9a670b549d84084618267fdeea13f196e43ae5df0d88e2e18bf5aa91b97318

[4]https://www.microsoft.com/en-us/security/blog/2022/10/05/detecting-and-preventing-lsass-credential-dumping-attacks

MITRE Mapping

INITIAL ACCESS - External Remote Services

DISCOVERY - Remote System Discovery

DISCOVERY - Network Service Discovery

DISCOVERY - File and Directory Discovery

CREDENTIAL ACCESS – OS Credential Dumping: LSASS Memory

LATERAL MOVEMENT - Remote Services: Remote Desktop Protocol

LATERAL MOVEMENT - Remote Services: SMB/Windows Admin Shares

EXECUTION - System Services: Service Execution

PERSISTENCE - Scheduled Task

COMMAND AND CONTROL - Ingress Tool Transfer

Darktrace Model Detections

Case A

Device / Suspicious Network Scan Activity

Device / Network Scan

Device / ICMP Address Scan

Device / Reverse DNS Sweep

Device / Suspicious SMB Scanning Activity

Device / Possible SMB/NTLM Reconnaissance

Anomalous Connection / Unusual Admin SMB Session

Device / SMB Session Brute Force (Admin)

Device / Possible SMB/NTLM Brute Force

Device / SMB Lateral Movement

Device / Anomalous NTLM Brute Force

Anomalous Connection / SMB Enumeration

Device / SMB Session Brute Force (Non-Admin)

Device / Anomalous SMB Followed By Multiple Model Breaches

Anomalous Connection / Possible Share Enumeration Activity

Device / RDP Scan

Device / Anomalous RDP Followed By Multiple Model Breaches

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Anomalous Connection / High Priority DRSGetNCChanges

Compliance / Default Credential Usage

User / New Admin Credentials on Client

User / New Admin Credentials on Server

Device / Large Number of Model Breaches from Critical Network Device

User / New Admin Credential Ticket Request

Compromise / Unusual SVCCTL Activity

Anomalous Connection / New or Uncommon Service Control

Anomalous File / Script from Rare External Location

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous File / EXE from Rare External Location

Anomalous File / Numeric File Download

Device / Initial Breach Chain Compromise

Device / Multiple Lateral Movement Model Breaches

Device / Large Number of Model Breaches

Compromise / Multiple Kill Chain Indicators

Case B

User / New Admin Credentials on Client

Compliance / Default Credential Usage

Anomalous Connection / SMB Enumeration

Device / Suspicious SMB Scanning Activity

Device / RDP Scan

Device / New or Uncommon WMI Activity

Device / Anomaly Indicators / New or Uncommon WMI Activity Indicator

Device / New or Unusual Remote Command Execution

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Active Remote Desktop Tunnel

Compliance / SMB Drive Write

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Device / Multiple Lateral Movement Model Breaches

Device / Anomalous ITaskScheduler Activity

Anomalous Connection / Unusual Admin RDP Session

Device / Large Number of Model Breaches from Critical Network Device

Compliance / Default Credential Usage

IOC - Type - Description/Probability

146.70.145[.]189 - IP Address - Likely C2 Infrastructure

Written by

Alexandra Sentenac

Cyber Analyst

Inside the SOC

Written by

Alexandra Sentenac

Cyber Analyst

•

April 17, 2025

Jeffrey Macre

Industrial Security Solutions Architect

•

April 16, 2025

Margaret Cunningham, PhD

Director, Security & AI Strategy, Field CISO

Watch the NIS2 Webinar

Blog

April 24, 2025

The Importance of NDR in Resilient XDR

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.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

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.

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

Blog

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

man looking at multiple computer screens

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.

Docker Hub Image Layers, referencing the script ten.py. — 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

Details of the above ten.py script. — 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:

def decode(input):
reversed = input[::-1]

decoded = base64.decode(reversed)
decompressed = zlib.decompress(decoded)
return decompressed

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.

Use of Cyberchef to decode the ten.py script. — 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.

References

1. https://www.crunchbase.com/funding_round/teneo-protocol-seed--a8ff2ad4

2. https://teneo.pro/

About the author

Nate Bill

Threat Researcher

Your data. Our AI.

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Check out this article by Darktrace: Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

What is living-off-the-land?

Darktrace’s detection of LOTL attacks

Case A

Reconnaissance

Lateral movement

Tooling

Case B

Reconnaissance

Lateral movement

Conclusion

References

MITRE Mapping

Darktrace Model Detections

Case A

Case B

IOC - Type - Description/Probability

Why Asset Visibility and Signature-Based Threat Detection Fall Short in ICS Security

Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)

Enjoying the blog?

More in this series

The Importance of NDR in Resilient XDR

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

How NDR and Secure Access Service Edge (SASE) Work Together to Achieve Network Security Outcomes

Blog

April 24, 2025

The Importance of NDR in Resilient XDR

Threat actors see local security agents as targets

What are the limitations of relying solely on EDR?

Why is network detection & response (NDR) critical?

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

Unlocking adaptive protection for evolving cyber risks

Blog

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

What is obfuscation?

Docker image analysis

Cryptojacking 2.0?

Conclusion

References