New backdoor targeting Ukrainian entities with possible links to Laundry Bear
A new JavaScript-based backdoor named DRILLAPP is targeting Ukrainian entities, likely linked to Russian threat actors associated with Laundry Bear. The malware uses judicial and charity-themed lures to deploy via the Microsoft Edge browser, exploiting its capabilities to evade detection. DRILLAPP enables extensive malicious actions including file manipulation, microphone and webcam access, and persistence mechanisms. Two variants have been observed, with the second variant introducing enhanced capabilities. The campaign leverages browser-based execution to bypass traditional endpoint defenses and gain access to sensitive resources. Attribution to Laundry Bear is low confidence but based on shared tactics and techniques. No known public exploits exist yet, and the campaign is assessed as medium severity. Organizations in Ukraine and allied countries with strategic interests are at heightened risk. Mitigation requires focused detection of browser-based backdoors, monitoring of suspicious Edge activity, and restricting execution of untrusted JavaScript content.
AI Analysis
Technical Summary
The DRILLAPP backdoor campaign represents a sophisticated threat targeting Ukrainian organizations, attributed with low confidence to the Russian-linked Laundry Bear group. The attackers employ social engineering using judicial and charity-themed lures to deliver a JavaScript backdoor that executes within the Microsoft Edge browser environment. This approach leverages Edge's native capabilities to evade traditional antivirus and endpoint detection systems, as the malicious code runs in a trusted browser context rather than as a standalone executable. DRILLAPP provides extensive functionality including file system manipulation, microphone and webcam access for espionage purposes, and persistence via system modifications. Two variants have been identified; the second variant introduces additional capabilities, indicating ongoing development and refinement by the threat actors. The campaign uses various techniques mapped to MITRE ATT&CK, such as T1113 (screen capture), T1033 (system owner/user discovery), T1056.001 (input capture: keylogging), T1059.007 (JavaScript), T1074.001 (data staged), T1082 (system information discovery), T1005 (data from local system), T1083 (file and directory discovery), T1057 (process discovery), T1547.001 (registry run keys/startup folder), T1071.001 (web protocol), T1105 (ingress tool transfer), and T1204.001 (user execution: malicious file). The use of browser-based backdoors is notable for evading detection and complicating incident response. While no known public exploits are reported, the campaign's targeting of Ukrainian entities amid ongoing geopolitical tensions underscores its strategic intent. The low-confidence attribution to Laundry Bear is based on overlapping tactics and infrastructure similarities with previously documented operations. This threat highlights the evolving use of browser environments as attack vectors in nation-state campaigns.
Potential Impact
The DRILLAPP backdoor campaign poses significant risks to confidentiality, integrity, and availability for targeted organizations, particularly in Ukraine. By leveraging browser-based execution, attackers can bypass many traditional endpoint security controls, increasing the likelihood of successful compromise. The ability to manipulate files, capture audio and video, and maintain persistence enables comprehensive espionage and potential sabotage. Sensitive data exfiltration and surveillance could compromise national security, diplomatic communications, and critical infrastructure operations. The campaign's use of social engineering increases the risk of initial infection, while the evolving variants suggest ongoing threat actor investment and capability growth. Organizations worldwide with geopolitical or strategic ties to Ukraine may face secondary risks from similar tactics. The medium severity reflects the targeted nature and complexity of the attack, balanced against the lack of widespread exploitation or automated propagation. However, the potential impact on critical Ukrainian institutions and allied interests is substantial, warranting heightened vigilance and response readiness.
Mitigation Recommendations
To mitigate DRILLAPP and similar browser-based backdoor threats, organizations should implement layered defenses focused on browser security and user awareness. Specifically: 1) Enforce strict execution policies to block or restrict untrusted JavaScript and CPL files, especially those delivered via email or web downloads. 2) Monitor Microsoft Edge browser processes for anomalous behavior such as unexpected network connections, unusual child processes, or access to microphone and webcam APIs. 3) Deploy endpoint detection and response (EDR) solutions capable of detecting browser-based malicious activity and behavioral indicators consistent with DRILLAPP's tactics. 4) Implement network segmentation and egress filtering to limit data exfiltration pathways and command-and-control communications. 5) Conduct targeted user training emphasizing the risks of judicial and charity-themed phishing lures and the importance of verifying email sources. 6) Regularly audit and harden system startup and registry keys to detect and prevent persistence mechanisms. 7) Utilize threat intelligence feeds to update detection rules with indicators of compromise related to DRILLAPP and Laundry Bear. 8) Employ multi-factor authentication and least privilege principles to reduce the impact of compromised credentials. 9) Maintain up-to-date software and browser versions to reduce exploitation of known vulnerabilities. 10) Establish incident response plans that include browser-based attack scenarios to ensure rapid containment and remediation.
Affected Countries
Ukraine, United States, United Kingdom, Germany, Poland, Canada, France, Estonia, Lithuania, Latvia
Indicators of Compromise
- hash: 0931c9edff01d16474d858f170662655
- hash: 2fd16e8c5cc95469ee34117b499beb81
- hash: 3080e3341220ff532706dfb2d6b7e8eb
- hash: 3be54a5eb71e0c45e144253d29f9532d
- hash: 591784a7c54046569b7b8bcc9f519044
- hash: 61e4d994bdbd97ab6fe017924aa67c05
- hash: 77e068e1a172217a8ceeb02837d4627a
- hash: 85b28710c665fbf98b7f928d1d2d9815
- hash: 93a640f2a33850810431b4db6d05d0df
- hash: 9e2f9fa5acd379c07b54d65c220b46fd
- hash: a4ec609ae0d54bbd2121d6f8fa372c85
- hash: a53ea1be69a2c45db4dddb815dd79c9b
- hash: bdfb29a28bfca6583bd6bfaa96a8b65b
- hash: befb27c65c7c30cad66936a62f0b6ab7
- hash: f332226c4d424951a7690bb4f9504575
- hash: f4834d63d2816f25cca1646f81cf4dd2
- hash: 3e524eea80139f4afaf998b27a1182fb2194c8e2
- hash: 44a3bf54a2d802fa57ce574851986db42414f123
- hash: 45204fb72243d1d8626e861f14981590b0055d9b
- hash: 4891841b406a00b445a11db1852d9c67287be316
- hash: 4a463351d7bbff714e60a068822fbc495b982407
- hash: 588635b3cb1d7e6709d5ce837172c14faa31b2ec
- hash: 6135b621f7f8d1f32c7cc99bd4b99012d46bacbd
- hash: 63521be6b3131f8ee4304eb91bb75292ecf56a39
- hash: 6bc452f0ec9b2bc06603b97d5f04b975406e282f
- hash: 6c390b6635b35db5505669276e1c6db774eae263
- hash: 74990b49353a96ef15e0dbf8ffddc94e0113b87f
- hash: af9b062bac22e8686d76be7865c1033957d39599
- hash: afcbe5c98eb7cb9d8199055f42729bb049a0b7d4
- hash: b143d890080b8bdbeaf0ea3c5e0673cf6624cac7
- hash: d34051b38ab0807798e85e8cda1bbf5a2ab9a4eb
- hash: f28249f9436a9d7f511cdc39858eaaa755eccf29
- hash: 107b2badfc93fcdd3ffda7d3999477ced3f39f43f458dd0f6a424c9ab52681c3
- hash: 21fefc3913d3d2dfde7f0dff54800ca7512eb5df9513b1a457a2af25fdd51b26
- hash: 2b5d8f8db5fd38ae1c34807dcba35b057cffa61eb14ba3b558f82eb630480c3f
- hash: 32973ef02e10a585a4a0196b013265e29fc57d8e1c50752f7b39e43b9f388715
- hash: 352f34ea5cc40e2b3ec056ae60fa19a368dbd42503ef225cb1ca57956eb05e81
- hash: 51e86408904c0ca3778361cde746783a0f2b9fd2a6782aa7e062aa597151876e
- hash: 5b978cdc46afa28d83e532cd19622d9097bebedf87efc4c87bd35d8ffad9e672
- hash: 6178b1af51057c0bac75a842afff500a8fa3ed957d79a712a6ef089bec7e7a8b
- hash: 66a7828bc8c6c783b2ffa3c906d53f6dae1bbddc019283cc369d7d73247c5181
- hash: 6fea579685d2433cedb1c32ef704575dcbc1d0a623769e824023ffccd0dedaae
- hash: 76eb713e38f145ee68b89f2febd8f9a28bbb2b464da61cb029d84433a0b2c746
- hash: 801c47550799831bfb1ac6c5c3fd698be95da19fc85bd65f5d8639f26244d2a9
- hash: 886df55794cbca146de96dcc626471b3c097a5c20ba488033b24f4347aa20a14
- hash: 8c6ea44ce7f4ed4e4e7e19e11b3b345d58785c93b33aa795ddd1b0d753236b05
- hash: 9367f4b4d2775ff47279d143dd9a0ef544ddff81946aab33da9350a49f14e1e1
- hash: 993d55f60414bf2092f421c3d0ac6af1897a21cc4ea260ae8e610a402bf4c81c
- hash: a545908c931ec47884b5ccfb1f112435f5d0cdac140e664673672c9df9016672
- hash: ac60eefc2607216f8126c0b22b6243f3862ef2bb265c585deee0d00a20a436b3
- hash: b891fa118db5190f07b18be46eb9bc10677f9afab1406a7d52ce587522ab3d28
- hash: bad7c6f6ca25363a02eaceb3ed1e378218dc4a246a63d723cfcc5feee3af5056
- hash: c6905bae088982a2b234451b45db742098f2e2ab4fd6ca62c8f4e801160552aa
- hash: ccb7d999ee4d979e175b8c87e09ccda0cbc93b6140471283e3a1f1f9da33759d
- hash: e20831cecd763d0dc91fb39f3bd61d17002608c5a40a6cf0bd16111f4e50d341
- hash: eb9c1649e01db6a9a94d5d50373e54865d672b14ad6f221c98047c562d3cc0f3
- hash: ee90b01b16099e0bb23d4653607a3a559590fc8d0c43120b8456fb1860d2e630
- hash: fb16933b09a4fcca5beff93da05566e924017fb534a2f45caf57b57a633f43a6
- ip: 188.137.228.162
- ip: 80.89.224.13
New backdoor targeting Ukrainian entities with possible links to Laundry Bear
Description
A new JavaScript-based backdoor named DRILLAPP is targeting Ukrainian entities, likely linked to Russian threat actors associated with Laundry Bear. The malware uses judicial and charity-themed lures to deploy via the Microsoft Edge browser, exploiting its capabilities to evade detection. DRILLAPP enables extensive malicious actions including file manipulation, microphone and webcam access, and persistence mechanisms. Two variants have been observed, with the second variant introducing enhanced capabilities. The campaign leverages browser-based execution to bypass traditional endpoint defenses and gain access to sensitive resources. Attribution to Laundry Bear is low confidence but based on shared tactics and techniques. No known public exploits exist yet, and the campaign is assessed as medium severity. Organizations in Ukraine and allied countries with strategic interests are at heightened risk. Mitigation requires focused detection of browser-based backdoors, monitoring of suspicious Edge activity, and restricting execution of untrusted JavaScript content.
AI-Powered Analysis
Technical Analysis
The DRILLAPP backdoor campaign represents a sophisticated threat targeting Ukrainian organizations, attributed with low confidence to the Russian-linked Laundry Bear group. The attackers employ social engineering using judicial and charity-themed lures to deliver a JavaScript backdoor that executes within the Microsoft Edge browser environment. This approach leverages Edge's native capabilities to evade traditional antivirus and endpoint detection systems, as the malicious code runs in a trusted browser context rather than as a standalone executable. DRILLAPP provides extensive functionality including file system manipulation, microphone and webcam access for espionage purposes, and persistence via system modifications. Two variants have been identified; the second variant introduces additional capabilities, indicating ongoing development and refinement by the threat actors. The campaign uses various techniques mapped to MITRE ATT&CK, such as T1113 (screen capture), T1033 (system owner/user discovery), T1056.001 (input capture: keylogging), T1059.007 (JavaScript), T1074.001 (data staged), T1082 (system information discovery), T1005 (data from local system), T1083 (file and directory discovery), T1057 (process discovery), T1547.001 (registry run keys/startup folder), T1071.001 (web protocol), T1105 (ingress tool transfer), and T1204.001 (user execution: malicious file). The use of browser-based backdoors is notable for evading detection and complicating incident response. While no known public exploits are reported, the campaign's targeting of Ukrainian entities amid ongoing geopolitical tensions underscores its strategic intent. The low-confidence attribution to Laundry Bear is based on overlapping tactics and infrastructure similarities with previously documented operations. This threat highlights the evolving use of browser environments as attack vectors in nation-state campaigns.
Potential Impact
The DRILLAPP backdoor campaign poses significant risks to confidentiality, integrity, and availability for targeted organizations, particularly in Ukraine. By leveraging browser-based execution, attackers can bypass many traditional endpoint security controls, increasing the likelihood of successful compromise. The ability to manipulate files, capture audio and video, and maintain persistence enables comprehensive espionage and potential sabotage. Sensitive data exfiltration and surveillance could compromise national security, diplomatic communications, and critical infrastructure operations. The campaign's use of social engineering increases the risk of initial infection, while the evolving variants suggest ongoing threat actor investment and capability growth. Organizations worldwide with geopolitical or strategic ties to Ukraine may face secondary risks from similar tactics. The medium severity reflects the targeted nature and complexity of the attack, balanced against the lack of widespread exploitation or automated propagation. However, the potential impact on critical Ukrainian institutions and allied interests is substantial, warranting heightened vigilance and response readiness.
Mitigation Recommendations
To mitigate DRILLAPP and similar browser-based backdoor threats, organizations should implement layered defenses focused on browser security and user awareness. Specifically: 1) Enforce strict execution policies to block or restrict untrusted JavaScript and CPL files, especially those delivered via email or web downloads. 2) Monitor Microsoft Edge browser processes for anomalous behavior such as unexpected network connections, unusual child processes, or access to microphone and webcam APIs. 3) Deploy endpoint detection and response (EDR) solutions capable of detecting browser-based malicious activity and behavioral indicators consistent with DRILLAPP's tactics. 4) Implement network segmentation and egress filtering to limit data exfiltration pathways and command-and-control communications. 5) Conduct targeted user training emphasizing the risks of judicial and charity-themed phishing lures and the importance of verifying email sources. 6) Regularly audit and harden system startup and registry keys to detect and prevent persistence mechanisms. 7) Utilize threat intelligence feeds to update detection rules with indicators of compromise related to DRILLAPP and Laundry Bear. 8) Employ multi-factor authentication and least privilege principles to reduce the impact of compromised credentials. 9) Maintain up-to-date software and browser versions to reduce exploitation of known vulnerabilities. 10) Establish incident response plans that include browser-based attack scenarios to ensure rapid containment and remediation.
Technical Details
- Author
- AlienVault
- Tlp
- white
- References
- ["https://lab52.io/blog/drillapp-new-backdoor-targeting-ukrainian-entities-with-possible-links-to-laundry-bear/"]
- Adversary
- Laundry Bear
- Pulse Id
- 69b934921c208cec80c35f6c
- Threat Score
- null
Indicators of Compromise
Hash
| Value | Description | Copy |
|---|---|---|
hash0931c9edff01d16474d858f170662655 | — | |
hash2fd16e8c5cc95469ee34117b499beb81 | — | |
hash3080e3341220ff532706dfb2d6b7e8eb | — | |
hash3be54a5eb71e0c45e144253d29f9532d | — | |
hash591784a7c54046569b7b8bcc9f519044 | — | |
hash61e4d994bdbd97ab6fe017924aa67c05 | — | |
hash77e068e1a172217a8ceeb02837d4627a | — | |
hash85b28710c665fbf98b7f928d1d2d9815 | — | |
hash93a640f2a33850810431b4db6d05d0df | — | |
hash9e2f9fa5acd379c07b54d65c220b46fd | — | |
hasha4ec609ae0d54bbd2121d6f8fa372c85 | — | |
hasha53ea1be69a2c45db4dddb815dd79c9b | — | |
hashbdfb29a28bfca6583bd6bfaa96a8b65b | — | |
hashbefb27c65c7c30cad66936a62f0b6ab7 | — | |
hashf332226c4d424951a7690bb4f9504575 | — | |
hashf4834d63d2816f25cca1646f81cf4dd2 | — | |
hash3e524eea80139f4afaf998b27a1182fb2194c8e2 | — | |
hash44a3bf54a2d802fa57ce574851986db42414f123 | — | |
hash45204fb72243d1d8626e861f14981590b0055d9b | — | |
hash4891841b406a00b445a11db1852d9c67287be316 | — | |
hash4a463351d7bbff714e60a068822fbc495b982407 | — | |
hash588635b3cb1d7e6709d5ce837172c14faa31b2ec | — | |
hash6135b621f7f8d1f32c7cc99bd4b99012d46bacbd | — | |
hash63521be6b3131f8ee4304eb91bb75292ecf56a39 | — | |
hash6bc452f0ec9b2bc06603b97d5f04b975406e282f | — | |
hash6c390b6635b35db5505669276e1c6db774eae263 | — | |
hash74990b49353a96ef15e0dbf8ffddc94e0113b87f | — | |
hashaf9b062bac22e8686d76be7865c1033957d39599 | — | |
hashafcbe5c98eb7cb9d8199055f42729bb049a0b7d4 | — | |
hashb143d890080b8bdbeaf0ea3c5e0673cf6624cac7 | — | |
hashd34051b38ab0807798e85e8cda1bbf5a2ab9a4eb | — | |
hashf28249f9436a9d7f511cdc39858eaaa755eccf29 | — | |
hash107b2badfc93fcdd3ffda7d3999477ced3f39f43f458dd0f6a424c9ab52681c3 | — | |
hash21fefc3913d3d2dfde7f0dff54800ca7512eb5df9513b1a457a2af25fdd51b26 | — | |
hash2b5d8f8db5fd38ae1c34807dcba35b057cffa61eb14ba3b558f82eb630480c3f | — | |
hash32973ef02e10a585a4a0196b013265e29fc57d8e1c50752f7b39e43b9f388715 | — | |
hash352f34ea5cc40e2b3ec056ae60fa19a368dbd42503ef225cb1ca57956eb05e81 | — | |
hash51e86408904c0ca3778361cde746783a0f2b9fd2a6782aa7e062aa597151876e | — | |
hash5b978cdc46afa28d83e532cd19622d9097bebedf87efc4c87bd35d8ffad9e672 | — | |
hash6178b1af51057c0bac75a842afff500a8fa3ed957d79a712a6ef089bec7e7a8b | — | |
hash66a7828bc8c6c783b2ffa3c906d53f6dae1bbddc019283cc369d7d73247c5181 | — | |
hash6fea579685d2433cedb1c32ef704575dcbc1d0a623769e824023ffccd0dedaae | — | |
hash76eb713e38f145ee68b89f2febd8f9a28bbb2b464da61cb029d84433a0b2c746 | — | |
hash801c47550799831bfb1ac6c5c3fd698be95da19fc85bd65f5d8639f26244d2a9 | — | |
hash886df55794cbca146de96dcc626471b3c097a5c20ba488033b24f4347aa20a14 | — | |
hash8c6ea44ce7f4ed4e4e7e19e11b3b345d58785c93b33aa795ddd1b0d753236b05 | — | |
hash9367f4b4d2775ff47279d143dd9a0ef544ddff81946aab33da9350a49f14e1e1 | — | |
hash993d55f60414bf2092f421c3d0ac6af1897a21cc4ea260ae8e610a402bf4c81c | — | |
hasha545908c931ec47884b5ccfb1f112435f5d0cdac140e664673672c9df9016672 | — | |
hashac60eefc2607216f8126c0b22b6243f3862ef2bb265c585deee0d00a20a436b3 | — | |
hashb891fa118db5190f07b18be46eb9bc10677f9afab1406a7d52ce587522ab3d28 | — | |
hashbad7c6f6ca25363a02eaceb3ed1e378218dc4a246a63d723cfcc5feee3af5056 | — | |
hashc6905bae088982a2b234451b45db742098f2e2ab4fd6ca62c8f4e801160552aa | — | |
hashccb7d999ee4d979e175b8c87e09ccda0cbc93b6140471283e3a1f1f9da33759d | — | |
hashe20831cecd763d0dc91fb39f3bd61d17002608c5a40a6cf0bd16111f4e50d341 | — | |
hasheb9c1649e01db6a9a94d5d50373e54865d672b14ad6f221c98047c562d3cc0f3 | — | |
hashee90b01b16099e0bb23d4653607a3a559590fc8d0c43120b8456fb1860d2e630 | — | |
hashfb16933b09a4fcca5beff93da05566e924017fb534a2f45caf57b57a633f43a6 | — |
Ip
| Value | Description | Copy |
|---|---|---|
ip188.137.228.162 | — | |
ip80.89.224.13 | — |
Threat ID: 69b93aa1771bdb1749a33193
Added to database: 3/17/2026, 11:27:29 AM
Last enriched: 3/17/2026, 11:42:55 AM
Last updated: 3/17/2026, 12:54:56 PM
Views: 8
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