Blog
/
Network
/
May 23, 2023

Darktrace’s Detection of a Hive Ransomware-as-Service

This blog investigates a new strain of ransomware, Hive, a ransomware-as-a-service. Darktrace was able to provide full visibility over the attacks.
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
Emily Megan Lim
Cyber Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
23
May 2023

Update: On January 26, 2023, the Hive ransomware group was dismantled and servers associated with the sale of the ransomware were taken offline following an investigation by the FBI, German law enforcement and the National Crime Agency (NCA). The activity detailed in this blog took place in 2022, whilst the group was still active.

RaaS in Cyber Security

The threat of ransomware continues to be a constant concern for security teams across the cyber threat landscape. With the growing popularity of Ransomware-as-a-Service (RaaS), it is becoming more and more accessible for even inexperienced would-be attackers. As a result of this low barrier to entry, the volume of ransomware attacks is expected to increase significantly.

What’s more, RaaS is a highly tailorable market in which buyers can choose from varied kits and features to use in their ransomware deployments meaning attacks will rarely behave the same. To effectively detect and safeguard against these differentiations, it is crucial to implement security measures that put the emphasis on detecting anomalies and focusing on deviations in expected behavior, rather than relying on depreciated indicators of compromise (IoC) lists or playbooks that focus on attack chains unable to keep pace with the increasing speed of ransomware evolution.

In early 2022, Darktrace DETECT/Network™ identified several instances of Hive ransomware on the networks of multiple customers. Using its anomaly-based detection, Darktrace was able to successfully detect the attacks and multiple stages of the kill chain, including command and control (C2) activity, lateral movement, data exfiltration, and ultimately data encryption and the writing of ransom notes.

Hive Ransomware 

Hive ransomware is a relatively new strain that was first observed in the wild in June 2021. It is known to target a variety of industries including healthcare, energy providers, and retailers, and has reportedly attacked over 1,500 organizations, collecting more than USD 100m in ransom payments [1].

Hive is distributed via a RaaS model where its developers update and maintain the code, in return for a percentage of the eventual ransom payment, while users (or affiliates) are given the tools to carry out attacks using a highly sophisticated and complex malware they would otherwise be unable to use. Hive uses typical tactics, techniques and procedures (TTPs) associated with ransomware, though they do vary depending on the Hive affiliate carrying out the attack.

In most cases a double extortion attack is carried out, whereby data is first exfiltrated and then encrypted before a ransom demand is made. This gives attackers extra leverage as victims are at risk of having their sensitive data leaked to the public on websites such as the ‘HiveLeaks’ TOR website.

Attack Timeline

Owing to the highly customizable nature of RaaS, the tactics and methods employed by Hive actors are expected to differ on a case-by-case basis. Nonetheless in the majority of Hive ransomware incidents identified on Darktrace customer environments, Darktrace DETECT observed the following general attack stages and features. This is possibly indicative of the attacks originating from the same threat actor(s) or from a widely sold batch with a particular configuration to a variety of actors.

Figure 1: A typical timeline of a Hive attack observed by Darktrace.

Initial Access 

Although Hive actors are known to gain initial access to networks through multiple different vectors, the two primary methods reported by security researchers are the exploitation of Microsoft Exchange vulnerabilities, or the distribution of phishing emails with malicious attachments [2][3].

In the early stages of one Hive ransomware attack observed on the network of a Darktrace customer, for example, Darktrace detected a device connecting to the rare external location 23.81.246[.]84, with a PowerShell user agent via HTTP. During this connection, the device attempted to download an executable file named “file.exe”. It is possible that the file was initially accessed and delivered via a phishing email; however, as Darktrace/Email was not enabled at the time of the attack, this was outside of Darktrace’s purview. Fortunately, the connection failed the proxy authentication was thus blocked as seen in the packet capture (PCAP) in Figure 2. 

Shortly after this attempted download, the same device started to receive a high volume of incoming SSL connections from a rare external endpoint, namely 146.70.87[.]132. Darktrace logged that this endpoint was using an SSL certificate signed by Go Daddy CA, an easily obtainable and accessible SSL certificate, and that the increase in incoming SSL connections from this endpoint was unusual behavior for this device. 

It is likely that this highly anomalous activity detected by Darktrace indicates when the ransomware attack began, likely initial payload download.  

Darktrace DETECT models:

  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System
Figure 2: PCAP of the HTTP connection to the rare endpoint 23.81.246[.]84 showing the failed proxy authentication.

C2 Beaconing 

Following the successful initial access, Hive actors begin to establish their C2 infrastructure on infected networks through numerous connections to C2 servers, and the download of additional stagers. 

On customer networks infected by Hive ransomware, Darktrace identified devices initiating a high volume of connections to multiple rare endpoints. This very likely represented C2 beaconing to the attacker’s infrastructure. In one particular example, further open-source intelligence (OSINT) investigation revealed that these endpoints were associated with Cobalt Strike.

Darktrace DETECT models:

  • Anomalous Connection / Multiple Connections to New External TCP
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compromise / High Volume of Connections with Beacon Score
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Suspicious HTTP Beacons to Dotted Quad 
  • Compromise / SSL or HTTP Beacon
  • Device / Lateral Movement and C2 Activity

Internal Reconnaissance, Lateral Movement and Privilege Escalation

After C2 infrastructure has been established, Hive actors typically begin to uninstall antivirus products in an attempt to remain undetected on the network [3]. They also perform internal reconnaissance to look for vulnerabilities and open channels and attempt to move laterally throughout the network.

Amid the C2 connections, Darktrace was able to detect network scanning activity associated with the attack when a device on one customer network was observed initiating an unusually high volume of connections to other internal devices. A critical network device was also seen writing an executable file “mimikatz.exe” via SMB which appears to be the Mimikatz attack tool commonly used for credential harvesting. 

There were also several detections of lateral movement attempts via RDP and DCE-RPC where the attackers successfully authenticated using an “Administrator” credential. In one instance, a device was also observed performing ITaskScheduler activity. This service is used to remotely control tasks running on machines and is commonly observed as part of malicious lateral movement activity. Darktrace DETECT understood that the above activity represented a deviation from the devices’ normal pattern of behavior and the following models were breached:

Darktrace DETECT models:

  • Anomalous Connection / Anomalous DRSGetNCChanges Operation
  • Anomalous Connection / New or Uncommon Service Control
  • Anomalous Connection / Unusual Admin RDP Session
  • Anomalous Connection / Unusual SMB Version 1 Connectivity
  • Compliance / SMB Drive Write
  • Device / Anomalous ITaskScheduler Activity
  • Device / Attack and Recon Tools
  • Device / Attack and Recon Tools In SMB
  • Device / EXE Files Distributed to Multiple Devices
  • Device / Suspicious Network Scan Activity
  • Device / Increase in New RPC Services
  • User / New Admin Credentials on Server

Data Exfiltration

At this stage of the attack, Hive actors have been known to carry out data exfiltration activity on infected networks using a variety of different methods. The Cybersecurity & Infrastructure Security Agency (CISA) reported that “Hive actors exfiltrate data likely using a combination of Rclone and the cloud storage service Mega[.]nz” [4]. Darktrace DETECT identified an example of this when a device on one customer network was observed making HTTP connections to endpoints related to Mega, including “w.apa.mega.co[.]nz”, with the user agent “rclone/v1.57.0” with at least 3 GiB of data being transferred externally (Figure 3). The same device was also observed transferring at least 3.6 GiB of data via SSL to the rare external IP, 158.51.85[.]157.

Figure 3: A summary of a device’s external connections to multiple endpoints and the respective amounts of data exfiltrated to Mega storage endpoints.

In another case, a device was observed uploading over 16 GiB of data to a rare external endpoint 93.115.27[.]71 over SSH. The endpoint in question was seen in earlier beaconing activity suggesting that this was likely an exfiltration event. 

However, Hive ransomware, like any other RaaS kit, can differ greatly in its techniques and features, and it is important to note that data exfiltration may not always be present in a Hive ransomware attack. In one incident detected by Darktrace, there were no signs of any data leaving the customer environment, indicating data exfiltration was not part of the Hive actor’s objectives.

Darktrace DETECT models:

  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Lots of New Connections
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Device / New User Agent and New IP
  • Unusual Activity / Unusual External Data to New Endpoints
  • Unusual Activity / Unusual External Data Transfer
  • Unusual Activity / Enhanced Unusual External Data Transfer

Ransomware Deployment

In the final stage of a typical Hive ransomware attack, the ransomware payload is deployed and begins to encrypt files on infected devices. On one customer network, Darktrace detected several devices connecting to domain controllers (DC) to read a file named “xxx.exe”. Several sources have linked this file name with the Hive ransomware payload [5].

In another example, Darktrace DETECT observed multiple devices downloading the executable files “nua64.exe” and “nua64.dll” from a rare external location, 194.156.90[.]25. OSINT investigation revealed that the files are associated with Hive ransomware.

Figure 4: Security vendor analysis of the malicious file hash [6] associated with Hive ransomware. 

Shortly after the download of this executable, multiple devices were observed performing an unusual amount of file encryption, appending randomly generated strings of characters to file extensions. 

Although it has been reported that earlier versions of Hive ransomware encrypted files with a “.hive” extension [7], Darktrace observed across multiple customers that encrypted files had extensions that were partially-randomized, but consistently 20 characters long, matching the regular expression “[a-zA-Z0-9\-\_]{8}[\-\_]{1}[A-Za-z0-9\-\_]{11}”.

Figure 5: Device Event Log showing SMB reads and writes of encrypted files with a randomly generated extension of 20 characters. 

Following the successful encryption of files, Hive proceeds to drop a ransom note, named “HOW_TO_DECRYPT.txt”, into each affected directory. Typically, the ransom note will contain a link to Hive’s “sales department” and, in the event that exfiltration took place, a link to the “HiveLeaks” site, where attackers threaten to publish exfiltrated data if their demands are not met (Figure 6).  In cases of Hive ransomware detected by Darktrace, multiple devices were observed attempting to contact “HiveLeaks” TOR domains, suggesting that endpoint users had followed links provided to them in ransom notes.

Figure 6: Sample of a Hive ransom note [4].

Examples of file extensions:

  • 36C-AT9-_wm82GvBoCPC
  • 36C-AT9--y6Z1G-RFHDT
  • 36C-AT9-_x2x7FctFJ_q
  • 36C-AT9-_zK16HRC3QiL
  • 8KAIgoDP-wkQ5gnYGhrd
  • kPemi_iF_11GRoa9vb29
  • kPemi_iF_0RERIS1m7x8
  • kPemi_iF_7u7e5zp6enp
  • kPemi_iF_y4u7pB3d3f3
  • U-9Xb0-k__T0U9NJPz-_
  • U-9Xb0-k_6SkA8Njo5pa
  • zm4RoSR1_5HMd_r4a5a9 

Darktrace DETECT models:

  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Sustained MIME Type Conversion
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Compliance / SMB Drive Write
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Compromise / Ransomware / Possible Ransom Note Write
  • Compromise / High Priority Tor2Web
  • Compromise / Tor2Web
  • Device / EXE Files Distributed to Multiple Devices

Conclusion

As Hive ransomware attacks are carried out by different affiliates using varying deployment kits, the tactics employed tend to vary and new IoCs are regularly identified. Furthermore, in 2022 a new variant of Hive was written using the Rust programming language. This represented a major upgrade to Hive, improving its defense evasion techniques and making it even harder to detect [8]. 

Hive is just one of many RaaS offerings currently on the market, and this market is only expected to grow in usage and diversity of presentations.  As ransomware becomes more accessible and easier to deploy it is essential for organizations to adopt efficient security measures to identify ransomware at the earliest possible stage. 

Darktrace DETECT’s Self-Learning AI understands customer networks and learns the expected patterns of behavior across an organization’s digital estate. Using its anomaly-based detection Darktrace is able to identify emerging threats through the detection of unusual or unexpected behavior, without relying on rules and signatures, or known IoCs. 

Credit to: Emily Megan Lim, Cyber Analyst, Hyeongyung Yeom, Senior Cyber Analyst & Analyst Team Lead.

Appendices

MITRE AT&CK Mapping

Reconnaissance

T1595.001 – Scanning IP Blocks

T1595.002 – Vulnerability Scanning

Resource Development

T1583.006 – Web Services

Initial Access

T1078 – Valid Accounts

T1190 – Exploit Public-Facing Application

T1200 – Hardware Additions

Execution

T1053.005 – Scheduled Task

T1059.001 – PowerShell

Persistence/Privilege Escalation

T1053.005 – Scheduled Task

T1078 – Valid Accounts

Defense Evasion

T1078 – Valid Accounts

T1207 – Rogue Domain Controller

T1550.002 – Pass the Hash

Discovery

T1018 – Remote System Discovery

T1046 – Network Service Discovery

T1083 – File and Directory Discovery

T1135 – Network Share Discovery

Lateral Movement

T1021.001 – Remote Desktop Protocol

T1021.002 – SMB/Windows Admin Shares

T1021.003 – Distributed Component Object Model

T1080 – Taint Shared Content

T1210 – Exploitation of Remote Services

T1550.002 – Pass the Hash

T1570 – Lateral Tool Transfer

Collection

T1185 – Man in the Browser

Command and Control

T1001 – Data Obfuscation

T1071 – Application Layer Protocol

T1071.001 – Web Protocols

T1090.003 – Multi-hop proxy

T1095 – Non-Application Layer Protocol

T1102.003 – One-Way Communication

T1571 – Non-Standard Port

Exfiltration

T1041 – Exfiltration Over C2 Channel

T1567.002 – Exfiltration to Cloud Storage

Impact

T1486 – Data Encrypted for Impact

T1489 – Service Stop

List of IoCs 

23.81.246[.]84 - IP Address - Likely Malicious File Download Endpoint

146.70.87[.]132 - IP Address - Possible Ransomware Endpoint

5.199.162[.]220 - IP Address - C2 Endpoint

23.227.178[.]65 - IP Address - C2 Endpoint

46.166.161[.]68 - IP Address - C2 Endpoint

46.166.161[.]93 - IP Address - C2 Endpoint

93.115.25[.]139 - IP Address - C2 Endpoint

185.150.1117[.]189 - IP Address - C2 Endpoint

192.53.123[.]202 - IP Address - C2 Endpoint

209.133.223[.]164 - IP Address - Likely C2 Endpoint

cltrixworkspace1[.]com - Domain - C2 Endpoint

vpnupdaters[.]com - Domain - C2 Endpoint

93.115.27[.]71 - IP Address - Possible Exfiltration Endpoint

158.51.85[.]157 - IP Address - Possible Exfiltration Endpoint

w.api.mega.co[.]nz - Domain - Possible Exfiltration Endpoint

*.userstorage.mega.co[.]nz - Domain - Possible Exfiltration Endpoint

741cc67d2e75b6048e96db9d9e2e78bb9a327e87 - SHA1 Hash - Hive Ransomware File

2f9da37641b204ef2645661df9f075005e2295a5 - SHA1 Hash - Likely Hive Ransomware File

hiveleakdbtnp76ulyhi52eag6c6tyc3xw7ez7iqy6wc34gd2nekazyd[.]onion - TOR Domain - Likely Hive Endpoint

References

[1] https://www.justice.gov/opa/pr/us-department-justice-disrupts-hive-ransomware-variant

[2] https://www.varonis.com/blog/hive-ransomware-analysis

[3] https://www.trendmicro.com/vinfo/us/security/news/ransomware-spotlight/ransomware-spotlight-hive 

[4]https://www.cisa.gov/news-events/cybersecurity-advisories/aa22-321a

[5] https://www.trendmicro.com/en_us/research/22/c/nokoyawa-ransomware-possibly-related-to-hive-.html

[6] https://www.virustotal.com/gui/file/60f6a63e366e6729e97949622abd9de6d7988bba66f85a4ac8a52f99d3cb4764/detection

[7] https://heimdalsecurity.com/blog/what-is-hive-ransomware/

[8] https://www.microsoft.com/en-us/security/blog/2022/07/05/hive-ransomware-gets-upgrades-in-rust/ 

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
Emily Megan Lim
Cyber Analyst

More in this series

No items found.

Blog

/

Identity

/

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

Default blog imageDefault blog image

The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1.  Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

Proactive Security

/

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Default blog imageDefault blog image

Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

Detecting threats without relying on CVE disclosure

Abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems. Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Often, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender: attack vs patch.

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security. After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

Autonomous Response

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

  1. https://www.first.org/blog/20250607-Vulnerability-Forecast-for-2025
  2. https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575
  3. https://thehackernews.com/2025/05/china-linked-hackers-exploit-sap-and.html

Related Darktrace blogs:

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

Continue reading
About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
Your data. Our AI.
Elevate your network security with Darktrace AI