Blog
/
Network
/
May 25, 2022

Understanding Grief Ransomware Attacks

Discover the latest insights on Grief ransomware and how to protect your organization. Stay informed on evolving cybersecurity threats with the cyber experts.
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
Dr. Oakley Cox-Robinson
Senior Director of Product
Default blog image
25
May 2022

The Grief ransomware strain, also referred to as PayOrGrief, quickly gained a reputation for disruption in mid-to-late 2021. The gang behind the malware used quadruple-extortion ransomware tactics and targeted a range of victims including municipalities and school districts.

In July 2021, just weeks after the strain was first reported to cyber security teams, Grief successfully targeted Thessaloniki, the second largest city in Greece. Faced with a $20 million ransom demand, the municipality’s security team was forced to shut down all of its websites and public-facing services and launch a full investigation into the breach.

Double act: Grief and DoppelPaymer

From its emergence in May 2021, Grief used novel malware which confounded security tools trained on historical attacks. By July, however, the sophistication and efficiency of the group’s attacks led many to suspect that Grief’s operators had experience beyond their supposed two months of operation.

Grief is now widely reported to be a rebrand of the DoppelPaymer ransomware gang, which ended its operations in May 2021 and was believed to be affiliated with the Russian ransomware gang Evil Corp. After adopting the new moniker, however, Grief regularly blew past traditional security tools, amassing well over $10 million in ransom payments in just four months.

Adaptations and rebrands are common techniques adopted by criminal gangs using the Ransomware-as-a-Service business model. The success of Grief’s rebrand illustrates how rapidly a ransomware group can update its attacks and render them unrecognizable to signature-based tools.

Revealing Grief’s tricks with Cyber AI Analyst

In July 2021, PayOrGrief targeted a European manufacturing company which had Darktrace deployed across its network. Darktrace’s early detection of the attack, along with the real-time visibility into its lifecycle offered by Darktrace’s Cyber AI Analyst, meant that each stage of the attack was clear to see.

Figure 1: Timeline of the PayOrGrief attack

The initial intrusion compromised four devices, which Darktrace detected when these devices connected to rare external IPs and downloaded encoded text files. It is likely that the devices were compromised as the result of a targeted phishing campaign, which are often used in Grief attacks as a way of injecting malware such as Dridex onto devices. If deployed within the targeted organization, Antigena Email would have identified the phishing campaign and halted it, before it reached employee inboxes. In this case, however, the attack continued.

Following the initial compromise, C2 (Command and Control) connections were made over an encrypted channel using invalid SSL certificates. An upload of 50MB of data was made from one of the infected devices to the company’s corporate server, which gave the attackers access to the company’s crown jewels: its most sensitive data. From this privileged position, and with keep-alive beacons in place, the attack was ready for detonation.

Several devices were detected attempting to upload data totaling more than 100 GB to the external file storage platform, Mega, using encrypted HTTPS on port 443. However, the attackers did not receive the total package of data they had expected. The organization had deployed Darktrace’s Autonomous Response to protect its key assets and most sensitive data. The AI recognized the anomalous behavior as a significant deviation from the business’s normal ‘pattern of life’ and autonomously blocked uploads from protected devices, preventing exfiltration wherever it was able to do so.

Figure 2: Data exfiltration from a single device, investigated by Cyber AI Analyst

The attackers then continued to spread through the digital environment. Using ‘Living off the Land’ techniques including RDP and SMB, they performed internal reconnaissance, escalated their privileges and moved laterally to additional digital assets. With access to new admin credentials, just ten hours after the initial C2 communications, the attackers commenced ransomware encryption.

It’s highly possible, therefore, that Grief has targeted Darktrace customers previously and been neutralized too early for the attack to be identified and attributed. In this instance, the organization had deployed Autonomous Response only on certain areas of the network, and we are therefore able to see how the attack progressed on unprotected devices.

Unusual suspects

The Indicators of Compromise (IoCs) for Grief ransomware have now been incorporated by many traditional security tools, but this is a short-term solution, and won’t account for further changes in both threat actor tactics and the digital environments they target. Once the Grief moniker has been exhausted, it is more than likely that another will be adopted in its place.

The AI-driven approach to cyber security tackles threats regardless of when and where they arrive, or what name they arrive under. By focusing on developing its sophisticated understanding of the entire digital estate, Darktrace’s Autonomous Response targets specific anomalies with specific, proportionate responses, even when they are part of entirely novel attacks. And when given the freedom to take action against these threats the moment they’re detected, Autonomous Response can ensure that organizations stay protected even when human teams are unavailable.

Thanks to Darktrace analyst Beverly McCann for her insights on the above threat find.

Technical details

Darktrace model detections

  • Device / Suspicious SMB Scanning Activity
  • Device / New User Agents
  • Anomalous Server Activity / Rare External from Server
  • Compliance / External Windows Communications
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Compliance / Remote Management Tool on Server
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Lots of New Connections
  • Unusual Activity / Unusual File Storage Data Transfer
  • Unusual Activity / Enhanced Unusual External Data Transfer [Enhanced Monitoring]
  • Anomalous Connection / Uncommon 1GiB Outbound
  • Unusual Activity / Unusual External Data to New Ips
  • Anomalous Connection / SMB Enumeration
  • Multiple Device Correlations / Behavioral Change Across Multiple Devices
  • Device / New or Uncommon WMI Activity
  • Unusual Activity / Unusual External Connections
  • Device / ICMP Address Scan
  • Anomalous Connection / Unusual Admin RDP Session
  • Compliance / SMB Version 1 Usage
  • Anomalous Connection / Unusual SMB Version 1
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Unusual Activity / Anomalous SMB Move and Write
  • Compromise / Ransomware / Suspicious SMB Activity [Enhanced Monitoring]
  • Anomalous Connection / Suspicious Read Write Ratio and Unusual SMB
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Unusual Remote Command Execution
  • User / New Admin Credentials On Client
  • Device / New or Uncommon SMB Named Pipe
  • Device / Multiple Lateral Movement Model Breaches [Enhanced Monitoring]
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / SMA Lateral Movement
  • Anomalous File / Internal / Unusual Internal EXE File Transfer
  • Anomalous Server Activity / Unusual Unresponsive Server
  • Device / Internet Facing Device with High Priority Alert
  • Multiple Device Correlations / Spreading Unusual SMB Activity
  • Multiple Device Correlations / Multiple Devices Breaching the Same Model

Darktrace Autonomous Response alerts

  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Antigena / Network / Insider Threat / Antigena Breaches Over Time Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches over Time Block
  • Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block
  • Antigena / Network / Insider Threat / Antigena SMB Enumeration Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / External Threat / Antigena Ransomware Block
  • Antigena / Network / External Threat / SMB Ratio Antigena Block

MITRE ATT&CK techniques observed

Reconnaissance
T1595 — Active Scanning

Resource Development
T1608 — Stage Capabilities

Initial Access
T1190 — Exploit Public-Facing Application

Persistence
T1133 — External Remote Services

Defense Evasion
T1079 — Valid Accounts

Discovery
T1046 — Network Service Scanning
T1083 — File and Directory Discovery
T1018 — Remote System Discovery

Lateral Movement
T1210 — Exploitation of Remote Services
T1080 — Taint Shared Content
T1570 — Lateral Tool Transfer
T1021 — Remote Services

Command and Control
T1071 — Application Layer Protocol
T1095 — Non-Application Layer Protocol
T1571 — Non-Standard Port

Exfiltration
T1041 — Exfiltration over C2 Channel
T1567 — Exfiltration Over Web Service
T1029 — Scheduled Transfer


Impact
T1486 — Data Encrypted for Impact
T1489 — Service Stop
T1529 — System Shutdown/Reboot

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
Dr. Oakley Cox-Robinson
Senior Director of Product

More in this series

No items found.

Blog

/

/

September 3, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

Default blog imageDefault blog image

Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

Continue reading
About the author

Blog

/

OT

/

September 3, 2026

Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device

Default blog imageDefault blog image

Key takeaways

  • Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
  • The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
  • The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.

Mirai and the Katana variant

Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].

Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].

In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access.  Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].

Darktrace’s detection of Mirai Botnet activity on a camera device

In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.

In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.

The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.

Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.

Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.

Figure 2:  Darktrace’s detection of the unusual data upload activity by the affected camera device.

Darktrace continued to observe similar data transfers to multiple rare endpoints  including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).

Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.

Exploitation continued

Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.

The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.

The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].

Figure 4: Darktrace’s  detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.

A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”,  from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.

Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address,  66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.

Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.

Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.

Conclusion

This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.

This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.

As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.

Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Device / Initial Attack Chain Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Anomalous Connection / Data Sent to Rare Domain

·      Unusual Activity / Enhanced Unusual External Data Transfer

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Device / Significant UDP Increase

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Unusual Activity / Unusual External Activity

·      Compliance / SSH to Rare External Destination

·      Unusual Activity / Unusual DNS

·      Device / External Network Scan

·      Device / Suspicious DNS Activity

·      Device / Large Number of Model Alerts

List of Indicators of Compromise (IoCs)

Indicator of Compromise Type Description
195.177.94[.]105 IP C2 endpoint
50.7.49[.]4:30171 IP Possible C2 endpoint
129.121.114[.]124 IP C2 endpoint
hxxp://195.177.94[.]105/n3 URL Likely C2 endpoint
hxxp://195.177.94[.]105/n2 URL Likely C2 endpoint
hxxp://129.121.114[.]124/lil URL Likely C2 endpoint
hxxp://129.121.114[.]124/HHn URL Possible C2 endpoint
hxxp://129.121.114[.]124/JFc URL Possible C2 endpoint
hxxp://129.121.114[.]124/jum URL Likely C2 endpoint
hxxp://129.121.114[.]124/OaSf URL Likely C2 endpoint
hxxp://129.121.114[.]124/OPWg URL Possible C2 endpoint
hxxp://129.121.114[.]124/vHwK URL Possible C2 endpoint
hxxp://129.121.114[.]124/VLv URL Possible C2 endpoint
hxxp://129.121.114[.]124/WbJ URL Possible C2 endpoint
hxxp://129.121.114[.]124/zkR URL Possible C2 endpoint
Ab17883ae4c3bc6afa18c439166eeeb4b03186e3093d984e3a95f573e0fcb7d8 SHA-256 Mirai payload
3d587e809dac49d34a3f717e072fd0aebe5e71db63333e45c81577d6b4266f87 SHA-256 Mirai payload
Bf6e81733a7e209d3dce80d15bf3c5d300752d961fae6b45d90c9bbe7f8c89a2 SHA-256 Possible payload
f25488303813ab1ec0eaa71562938601aac185e8aaf93adb84522557f7cf4dd6 SHA-256 Possible payload
0cb4ff6b71f4423184bfa35c34e9090297637208b0e30205d4b224e56abde2ef SHA-256 Possible payload
19c24cbeaf06b2e7697083f33a85521a9315105c784691bde7420fde4cc69410 SHA-256 Likely Mirai payload
1e74f734fff8df91f4f7172d0de10c421eca78aeb800e8a48e16bc5dbde5d20e SHA-256 Possible payload
6e71f7763d1f29d5712106ebb122e281c32787540aa2342b0fe5351d585d18d7 SHA-256 Possible payload
71f4ff7cdb6d6a7d2673c543c5d2535093afbd707b20a5b9ddf735466c1105c1 SHA-256 Possible payload
76db7ee73ebf15e48a3cb24a074d92248671ef2c6ed3bc3e708377341fb7674d SHA-256 Possible payload
da87a65f7beb438e61f0b61964fed8aa305a380f569042f84c55eca8fa7929b8 SHA-256 Possible payload
e15809eb6ba66477175270d62cfa53e4bf278595f69938708c81c4bc457930fe SHA-256 Mirai payload

MITRE ATT&CK Mapping

Tactic Technique ID Technique / Sub-technique
Initial Access T1659 Content Injection
T1189 Drive-by Compromise
Exfiltration T1041 Exfiltration Over C2 Channel
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol
Command and Control T1105 Ingress Tool Transfer
T1095 Non-Application Layer Protocol
T1571 Non-Standard Port
Reconnaissance T1595.001 Scanning IP Blocks
Continue reading
About the author
Parvatha Ananthakannan
Cyber Analyst
Your data. Our AI.
Elevate your network security with Darktrace AI