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August 5, 2025

2025 Cyber Threat Landscape: Darktrace’s Mid-Year Review

Explore key cyber threat trends observed across Darktrace’s customer base in the first half of 2025. As threat actors increasingly adopt AI and diversify their techniques and tooling, anomaly-based detection continues to prove vital in defending against evolving attacks.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Emma Foulger
Global Threat Research Operations Lead
cyberseucity 2025 half year threat report Default blog image
05
Aug 2025

Note: Following initial publication, an error was identified in the previously reported number of QR codes observed in phishing emails in February 2025. This figure was originally stated as over 1 million, when in fact it was over 100,000. The error has since been corrected.

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2025: Threat landscape in review

The following is a retrospective of the first six months of 2025, highlighting key findings across the threat landscape impacting Darktrace customers.

Darktrace observed a wide range of tactics during this period, used by various types of threat actors including advanced persistent threats (APTs), Malware-as-a-Service (MaaS) and Ransomware-as-a-Service (RaaS) groups.

Methodology

Darktrace’s Analyst team conduct investigations and research into threats facing organizations and security teams across our customer base.  This includes direct investigations with our 24/7 Security Operations Centre (SOC), via services such as Managed Detection and Response (MDR) and Managed Threat Detection, as well as broader cross-fleet research through our Threat Research function.

At the core of our research is Darktrace’s anomaly-based detection, which the Analyst team contextualizes and analyzes to provide additional support to customers and deepen our understanding of the threats they face.

Threat actors are incorporating AI into offensive operations

Threat actors are continuously evolving their tactics, techniques, and procedures (TTPs), posing an ongoing challenge to effective defense hardening. Increasingly, many threat actors are adopting AI, particularly large language models (LLMs), into their operations to enhance the scale, sophistication, and efficacy of their attacks.

The evolving functionality of malware, such as the recently reported LameHug malware by CERT-UA, which uses an open-source LLM, exemplifies this observation [1].

Threat landscape trends in 2025

Threat actors applying AI to Email attacks

LLMs present a clear opportunity for attackers to take advantage of AI and create effective phishing emails at speed. While Darktrace cannot definitively confirm the use of AI to create the phishing emails observed across the customer base, the high volume of phishing emails and notable shifts in tactic could potentially be explained by threat actors adopting new tooling such as LLMs.

  • The total number of malicious emails detected by Darktrace from January to May 2025 was over 12.6 million
  • VIP users continue to face significant threat, with over 25% of all phishing emails targeting these users in the first five months of 2025 ‍
  • QR code-based phishing emails have remained a consistent tactic, with a similar proportion observed in January-May 2024 and 2025. The highest numbers were observed in February 2025, with over 100,000 detected in that month alone.
  • Shifts towards increased sophistication within phishing emails are emerging, with a year-on-year increase in the proportion of phishing emails containing either a high text volume or multistage payloads. In the first five months of 2025, 32% of phishing emails contained a high volume of text.

The increase in proportion of phishing emails with a high volume of text in particular could point towards threat actors leveraging LLMs to create phishing emails with large, but believable, text in an easy and efficient way.

The above email statistics are derived from analysis of monitored Darktrace / EMAIL model data for all customer deployments hosted in the cloud between January 1 and May 31, 2025.

Campaign Spotlight: Simple, Quick - ClickFix

An interesting technique Darktrace observed multiple times throughout March and April was ClickFix social engineering, which exploits the intersection between humans and technology to trick users into executing malicious code on behalf of the attacker.

  • While this technique has been around since 2024, Darktrace observed campaign activity in the first half of 2025 suggesting a resurgence.  
  • A range of threat actors – from APTs to MaaS and RaaS have adopted this technique to deliver secondary payloads, like information stealing malware.
  • Attackers use fraudulent or compromised legitimate websites to inject malicious plugins that masquerade as fake CAPTCHAs.
  • Targeted users believe they are completing human verification or resolving a website issue, unaware that they are being guided through a series of simple steps to execute PowerShell code on their system.
  • Darktrace observed campaign activity during the first half of 2025 across a range of sectors, including Government, Healthcare, Insurance, Retail and, Non-profit.

Not just AI: Automation is enabling Ransomware and SaaS exploitation

The rise of phishing kits like FlowerStorm and Mamba2FA, which enable phishing and abuse users’ trust by mimicking legitimate services to bypass multi-factor authentication (MFA), highlight how the barriers to entry for sophisticated attacks continue to fall, enabling new threat actors. Combined with Software-as-a-Service (SaaS) account compromise, these techniques make up a substantial portion of cybercriminal activity observed by Darktrace so far this year.

Credentials remain the weak link

A key theme across multiple cases of ransomware was threat actors abusing compromised credentials to gain initial entry into networks via:

  • Unauthorized access to internet-facing technology such as RDP servers and virtual private networks (VPNs).
  • Unauthorized access to SaaS accounts.

SaaS targeted ransomware is on the rise

The encryption of files within SaaS environments observed by Darktrace demonstrates a continued trend of ransomware actors targeting these platforms over traditional networks, potentially driven by a higher return on investment.

SaaS accounts are often less protected than traditional systems because of Single Sign-On (SSO).  Additionally, platforms like Salesforce often host sensitive data, including emails, financial records, customer information, and network configuration details. This stresses the need for robust identity management practices and continuous monitoring.

RaaS is adding complexity and speed to cyber attacks

RaaS has dominated the attack landscape, with groups like Qilin, RansomHub, and Lynx all appearing multiple times in cases across Darktrace’s customer base over the past six months. Detecting ransomware attacks before the encryption stage remains a significant challenge, particularly in RaaS operations where different affiliates often use varying techniques for initial entry and earlier stages of the attack. Darktrace’s recent analysis of Scattered Spider underscores the challenge of hardening defenses against such varying techniques.

CVE exploitation continues despite available patches

Darktrace has also observed ransomware gangs exploiting known Common Vulnerabilities and Exposures (CVEs), including the Medusa ransomware group’s use of the SimpleHelp vulnerabilities: CVE-2024-57727 and CVE-2024-57728 in March, despite patches being made available in January [2].

Misused tools + delayed patches = growing cyber risk

The exploitation of common remote management tools like SimpleHelp highlights the serious challenges defenders face when patch management cycles are suboptimal. As threat actors continue to abuse legitimate services for malicious purposes, the challenges facing defenders will only grow more complex.

Edge exploitation

It comes as no surprise that exploitation of internet-facing devices continued to feature prominently in Darktrace’s Threat Research investigations during the first half of 2025.

Observed CVE exploitation included:

Many of Darktrace’s observations of CVE exploitation so far in 2025 align with wider industry reporting, which suggests that Chinese-nexus threat actors were deemed to likely have exploited these technologies prior to public disclosure. In the case of CVE-2025-0994 - a vulnerability affecting Trimble Cityworks, an asset management system designed for use by local governments, utilities, airports, and public work agencies [3] - Darktrace observed signs of exploitation as early as January 19, well before vulnerability’s public disclosure on February 6 [4]. Darktrace’s early identification of the exploitation stemmed from the detection of a suspicious file download from 192.210.239[.]172:3219/z44.exe - later linked to Chinese-speaking threat actors in a campaign targeting the US government [5].

This case demonstrates the risks posed by the exploitation of internet-facing devices, not only those hosting more common technologies, but also software associated specifically tied to Critical National Infrastructure (CNI); a lucrative target for threat actors. This also highlights Darktrace’s ability to detect exploitation of internet-facing systems, even without a publicly disclosed CVE. Further examples of how Darktrace’s anomaly detection can uncover malicious activity ahead of public vulnerability disclosures can be found here.

New threats and returning adversaries

In the first half of 2025, Darktrace observed a wide range of threats, from sophisticated techniques employed by APT groups to large-scale campaigns involving phishing and information stealers.

BlindEagle (APT-C-36)

Among the observed APT activity, BlindEagle (APT-C-36) was seen targeting customers in Latin America (LATM), first identified in February, with additional cases seen as recently as June.

Darktrace also observed a customer targeted in a China-linked campaign involving the LapDogs ORB network, with activity spanning from December 2024 and June 2025. These likely nation-state attacks illustrate the continued adoption of cyber and AI capabilities into the national security goals of certain countries.

Sophisticated malware functionality

Further sophistication has been observed within specific malware functionality - such as the malicious backdoor Auto-Color, which has now been found to employ suppression tactics to cover its tracks if it is unable to complete its kill chain - highlighting the potential for advanced techniques across every layer of an attack.‍

Familiar foes

Alongside new and emerging threats, previously observed and less sophisticated tools, such as worms, Remote Access Trojans (RATs), and information stealers, continue to impact Darktrace customers.

The Raspberry Robin worm... First seen in 2021, has been repeatedly identified within Darktrace’s customer base since 2022. Most recently, Darktrace’s Threat Research team identified cases in April and May this year. Recent open-source intelligence (OSINT) reporting suggests that Raspberry Robin continues to evolve its role as an Initial Access Broker (IAB), paving the way for various attacks and remaining a concern [6].

RATs also remain a threat, with examples like AsyncRAT and Gh0st RAT impacting Darktrace customers.

In April multiple cases of MaaS were observed in Darktrace’s customer base, with information stealers Amadey and Stealc, as well as GhostSocks being distributed as a follow up payload after an initial Amadey infection.

Conclusion

As cyber threats evolve, attackers are increasingly harnessing AI to craft highly convincing email attacks, automating phishing campaigns at unprecedented scale and speed. This, coupled with rapid exploitation of vulnerabilities and the growing sophistication of ransomware gangs operating as organized crime syndicates, makes today’s threat landscape more dynamic and dangerous than ever. Cyber defenders collaborate to combat these threats – the coordinated takedown of Lumma Stealer in May was a notable win for both industry and law-enforcement [7], however OSINT suggests that this threat persists [8], and new threats will continue to arise.

Traditional security tools that rely on static rules or signature-based detection often struggle to keep pace with these fast-moving, adaptive threats. In this environment, anomaly-based detection tools are no longer optional—they are essential. By identifying deviations in normal user and system behavior, tools like Darktrace provide a proactive layer of defense capable of detecting novel and emerging threats, even those that bypass conventional security measures. Investing in anomaly-based detection is critical to staying ahead of attackers who now operate with automation, intelligence, and global coordination.

Credit to Emma Foulger (Global Threat Research Operations Lead), Nathaniel Jones (VP, Security & AI Strategy, Field CISO),  Eugene Chua (Principal Cyber Analyst & Analyst Team Lead), Nahisha Nobregas (Senior Cyber Analyst), Nicole Wong (Principal Cyber Analyst), Justin Torres (Senior Cyber Analyst), Matthew John (Director of Operations, SOC), Sam Lister (Specialist Security Researcher), Ryan Traill (Analyst Content Lead) and the Darktrace Incident Management team.

The information contained in this blog post is provided for general informational purposes only and represents the views and analysis of Darktrace as of the date of publication. While efforts have been made to ensure the accuracy and timeliness of the information, the cybersecurity landscape is dynamic, and new threats or vulnerabilities may have emerged since this report was compiled.

This content is provided “as is” and without warranties of any kind, either express or implied. Darktrace makes no representations or warranties regarding the completeness, accuracy, reliability, or suitability of the information, and expressly disclaims all warranties.

Nothing in this blog post should be interpreted as legal, technical, or professional advice. Users of this information assume full responsibility for any actions taken based on its content, and Darktrace shall not be liable for any loss or damage resulting from reliance on this material. Reference to any specific products, companies, or services does not constitute or imply endorsement, recommendation, or affiliation.

Appendices

Indicators of Compromise (IoCs)

IoC - Type - Description + Probability

LapDogs ORB network, December 2024-June 2025

www.northumbra[.]com – Hostname – Command and Control (C2) server

103.131.189[.]2 – IP Address - C2 server, observed December 2024 & June 2025

103.106.230[.]31 – IP Address - C2 server, observed December 2024

154.223.20[.]56 – IP Address – Possible C2 server, observed December 2024

38.60.214[.]23 – IP Address – Possible C2 server, observed January & February 2025

154.223.20[.]58:1346/systemd-log – URL – Possible ShortLeash payload, observed December 2024

CN=ROOT,OU=Police department,O=LAPD,L=LA,ST=California,C=US - TLS certificate details for C2 server

CVE-2025-0994, Trimble Cityworks exploitation, January 2025

192.210.239[.]172:3219/z44.exe – URL - Likely malicious file download

AsyncRAT, February-March 2025

windows-cam.casacam[.]net – Hostname – Likely C2 server

88.209.248[.]141 – IP Address – Likely C2 server

207.231.105[.]51 – IP Address – Likely C2 server

163.172.125[.]253 – IP Address – Likely C2 server

microsoft-download.ddnsfree[.]com – Hostname – Likely C2 server

95.217.34[.]113 – IP Address – Likely C2 server

vpnl[.]net – Hostname – Likely C2 server

157.20.182[.]16 – IP Address - Likely C2 server

185.81.157[.]19 – IP Address – Likely C2 server

dynamic.serveftp[.]net – IP Address – Likely C2 server

158.220.96.15 – IP Address – Likely C2 server

CVE-2024-57727 & CVE-2024-57728, SimpleHelp RMM exploitation, March 2025

213.183.63[.]41 – IP Address - C2 server

213.183.63[.]41/access/JWrapper-Windows64JRE-version.txt?time=3512082867 – URL - C2 server

213.183.63[.]41/access/JWrapper-Windows64JRE-00000000002-archive.p2.l2 – URL - C2 server

pruebas.pintacuario[.]mx – Hostname – Possible C2 server

144.217.181[.]205 – IP Address – Likely C2 server

erp.ranasons[.]com – Hostname – Possible destination for exfiltration

143.110.243[.]154 – IP Address – Likely destination for exfiltration

Blind Eagle, April-June 2025

sostenermio2024.duckdns[.]org/31agosto.vbs – URL – Possible malicious file download

Stealc, April 2025

88.214.48[.]93/ea2cb15d61cc476f[.]php – URL – C2 server

Amadey & GhostSocks, April 2025

195.82.147[.]98 – IP Address - Amadey C2 server

195.82.147[.]98/0Bdh3sQpbD/index.php – IP Address – Likely Amadey C2 activity

194.28.226.181 – IP Address – Likely GhostSocks C2 server

RaspberryRobin, May 2025

4j[.]pm – Hostname – C2 server

4xq[.]nl – Hostname – C2 server

8t[.]wf – Hostname – C2 server

Gh0stRAT, May 2025

lu.dssiss[.]icu  - Hostname – Likely C2 server

192.238.133[.]162:7744/1-111.exe – URL – Possible addition payload

8e9dec3b028f2406a8c546a9e9ea3d50609c36bb - SHA1 - Possible additional payload

f891c920f81bab4efbaaa1f7a850d484 - MD5 – Possible additional payload

192.238.133[.]162:7744/c3p.exe – URL - Possible additional payload

03287a15bfd67ff8c3340c0bae425ecaa37a929f - SHA1 - Possible additional payload

02aa02aee2a6bd93a4a8f4941a0e6310 - MD5 - Possible additional payload

192.238.133[.]162:7744/1-1111.exe – URL - Possible additional payload

1473292e1405882b394de5a5857f0b6fa3858fd1 - SHA1 - Possible additional payload

69549862b2d357e1de5bab899ec0c817 - MD5 - Possible additional payload

192.238.133[.]162:7744/1-25.exe – URL -  Possible additional payload

20189164c4cd5cac7eb76ba31d0bd8936761d7a7  - SHA1 - Possible additional payload

f42aa5e68b28a3f335f5ea8b6c60cb57 – MD5 - Possible additional payload

192.238.133[.]162:7744/Project1_se.exe – URL - Possible additional payload

fea1e30dfafbe9fa9abbbdefbcbe245b6b0628ad - SHA1 - Possible additional payload

5ea622c630ef2fd677868cbe8523a3d5 - MD5 - Possible additional payload

192.238.133[.]162:7744/Project1_se.exe - URL - Possible additional payload

aa5a5d2bd610ccf23e58bcb17d6856d7566d71b9  - SHA1 - Possible additional payload

9d33029eaeac1c2d05cf47eebb93a1d0 - MD5 - Possible additional payload

References and further reading

1.        https://cip.gov.ua/en/news/art28-atakuye-sektor-bezpeki-ta-oboroni-za-dopomogoyu-programnogo-zasobu-sho-vikoristovuye-shtuchnii-intelekt?utm_medium=email&_hsmi=113619842&utm_content=113619842&utm_source=hs_email

2.        https://www.s-rminform.com/latest-thinking/cyber-threat-advisory-medusa-and-the-simplehelp-vulnerability

3.        https://assetlifecycle.trimble.com/en/products/software/cityworks

4.     https://nvd.nist.gov/vuln/detail/CVE-2025-0994

5.     https://blog.talosintelligence.com/uat-6382-exploits-cityworks-vulnerability/

6.        https://www.silentpush.com/blog/raspberry-robin/

7.        https://blogs.microsoft.com/on-the-issues/2025/05/21/microsoft-leads-global-action-against-favored-cybercrime-tool/

8.     https://www.trendmicro.com/en_sg/research/25/g/lumma-stealer-returns.html

Related Darktrace investigations

-              ClickFix

-              FlowerStorm

-              Mamba 2FA

-              Qilin Ransomware

-              RansomHub Ransomware

-              RansomHub Revisited

-              Lynx Ransomware

-              Scattered Spider

-              Medusa Ransomware

-              Legitimate Services Malicious Intentions

-              CVE-2025-0282 and CVE-2025-0283 – Ivanti CS, PS and ZTA

-              CVE-2025-31324 – SAP Netweaver

-              Pre-CVE Threat Detection

-              BlindEagle (APT-C-36)

-              Raspberry Robin Worm

-              AsyncRAT

-              Amadey

-              Lumma Stealer

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
Emma Foulger
Global Threat Research Operations Lead

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October 15, 2026

CikaBika Bot: Inside a Discord RAT

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Key Takeaways

·      Darktrace malware researchers have found an ongoing malware campaign using a document-themed lure containing a malicious LNK file and a PDF with an embedded payload.

·      The Nim based loader deploys a Rust-based remote access trojan (RAT), using a UAC bypass and DLL search-order hijacking technique targeting Microsoft Edge.

·      The RAT uses Discord for C2, creating dedicated channels to manage infected devices and execute commands.

·      CikaBika Bot supports system reconnaissance, remote command execution, screenshot capture, and file transfers.

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Introduction

In a threat landscape where attackers continue to rely on social engineering techniques to deliver malware, legitimate services are increasingly being abused to support malicious operations. Communications platforms like Discord can provide attackers with a ready-made channel for command-and-control (C2), allowing them to manage compromised devices without establishing dedicated infrastructure.

Malware researchers at Darktrace have identified a campaign distributing CikaBika, a Rust-based RAT that uses Discord for C2. The infection chain begins with a disk image and a malicious Windows shortcut. A Nim loader extracts the final payload and attempts to plant it in Microsoft Edge’s installation directory using a PowerShell UAC bypass. Once running, the RAT collects host information, registers the device with Discord, and accepts commands through a dedicated channel.

Technical Analysis

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Figure 1: Contents of Proposition_Projet.img.

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Initial access and PDF payload

The malware is packaged within a disk image named Proposition_Projet.img. Inside are a PDF titled “Presentation_Generale.pdf.pdf” and a Windows shortcut (LNK) disguised as a Word document, “Proposition_Projet_Personnalisable.docx”. The delivery method has not been confirmed; however, the filenames and document-themed lure are consistent with a phishing campaign.

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Figure 2: PowerShell command embedded in the malicious shortcut.


Opening the shortcut launches a hidden PowerShell process while displaying the PDF as a decoy. The French-language document explains how to produce a written summary or contraction of a text. In the background, PowerShell reads the PDF as bytes and uses the value at offset 239 (0x20) as an XOR key to decode an embedded executable. The resulting payload is written to %TEMP%\IfyuUapz.txt and executed using cmd /c.

Nim loader and UAC bypass

“IfyuUapz.txt” is a Nim-based loader that contains two Base64-encoded blobs: a Rust dynamic-link library (DLL)  and a PowerShell deployment script. Both are decoded and written to temporary files, referenced to within the loader as “dllFile” and “copyPs1”. The loader runs the script through a hidden PowerShell process using an encoded command, then attempts to load the DLL using Nim’s loadLib function. If loading fails, it invokes regsvr32.exe and retries.

Figure 3: Silent DLL deployer.

The PowerShell component is responsible for moving the DLL to C:\Program Files (x86)\Microsoft\Edge\Application\dbghelp.dll. It first copies the DLL to C:\Windows\Temp\dbghelp.tmp, then modifies HKCU\Software\Classes\ms-settings\shell\open\command so that launching computerdefaults.exe can trigger a command to move the staged DLL into the protected Edge directory. This is an attempt to bypass User Account Control (UAC), which depends on the target’s Windows configuration.

The script also compiles a small C# helper that calls ShowWindow to hide windows associated with Windows Settings and ApplicationFrameHost. It launches the bypass helper three times, checks for the presence of the destination DLL, and attempts to delete the temporary scripts, intermediate DLL, and associated registry changes.

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Figure 4: Legitimate msedge.exe function to load the legitimate dbghelp.dll.

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The legitimate msedge.exe has a delay-loaded dependency on dbghelp.dll, and the malicious DLL exports a subset of the functions associated with Microsoft’s debugging library. The attacker appears to be exploiting this dependency through DLL search-order hijacking. However, it does not export every function in Edge’s delay-import table, meaning its compatibility with the host is incomplete. The observed code establishes the intended sideloading route, rather than proving that Edge successfully loaded the planted DLL.

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Rust RAT payload

Figure 5: Discord configuration within the RAT.

The malicious dbghelp.dll (IfyuUapz.txt) is a Rust-based RAT. At startup, the RAT registers the compromised device with Discord and begins collecting information about the host. Its reconnaissance routine gathers system details, runs tasklist to enumerate processes, executes ipconfig to obtain network configuration, and attempts to take a screenshot. The resulting artifacts include tasklist_startup.txt, network_info.txt, and screenshot.png.

Figure 6: Network configuration notification in Discord channel.

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Figure 7: Network configuration output from network_info.txt.

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Discord C2

The RAT uses Discord channels to send commands and report results. The operator can send commands to the device’s channel and receive the resulting output in the same conversation. This gives the attacker an interactive command interface without having to operate and maintain a dedicated C2 server.

Figure 8: Commands available in Discord.

The command set includes !shell for remote command execution; !sysinfo and !info for host details; !isOn for status checks; !tasklist and !ps for process enumeration; and !kill for process termination. File-transfer commands include !upload and !download. Additional strings and command handlers reference screenshots (!src), camera access (!camera), streaming (!stream), and broadcasting (!broadcast).

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Figure 9: Device registration in Discord.

During device registration, the malware retrieves the victim’s public IP address using ipify.com. The registration message records the device ID, hostname, registration time, private and public IP addresses, status, uptime, and the Discord channel assigned to that device.

Operator activity and exfiltration

Figure 10: Powershell commands sent through Discord to exfiltrate data.

Once a device is registered, the attacker can issue commands through its dedicated Discord channel. In the observed activity, the attacker compressed a directory into a ZIP and exfiltrated it using ngrok.

Figure 11: Download command to retrieve a document from the target.

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The operator can also use !download to retrieve files from target. The example above displays a request for a specific document on the victim’s desktop. The requested file is then returned through the device’s Discord channel, giving the attacker a direct way to store and access selected data.

Conclusion

CikaBika Bot demonstrates how attackers continue to rely on social engineering techniques to deliver malware. Although the initial distribution method has not been confirmed, the use of a disk image containing a shortcut disguised as a Word document, alongside a legitimate-looking PDF, suggests the campaign was designed for phishing.

While Nim is less commonly encountered than C in Windows malware, it has increasingly appeared in malicious tooling. In this case, Nim is used to unpack and deploy a Rust-based RAT, adding another layer to the infection chain.

Once installed, CikaBika Bot uses Discord as its C2 platform, allowing the attacker to manage compromised devices, execute commands, and retrieve files without maintaining dedicated C2 infrastructure. The observed use of Discord channels for individual victims, alongside additional data exfiltration through ngrok, shows how legitimate services can be repurposed to support malicious operations.

Credit to Tara Gould (Malware Research Lead)

Indicators of Compromise (IoCs)

Presentation_Generale.pdf.pdf - eeb52cb95113a131c2c7713c17872da6

Proposition_Projet_Personnalisable.docx.lnk - 62e3e85eca78a0cd7e6669e9f33d936d

dbghelp.dll - c66858718d883bcfb9ce2c19b59f96c9

IfyuUapz.txt - 2deb724a709b0b892248405323350e9b

MITRE ATTACK

T1204.002 – User Execution: Malicious File
T1036.007 – Masquerading: Double File Extension
T1059.001 – PowerShell
T1027 – Obfuscated Files or Information
T1140 – Deobfuscate/Decode Files or Information
T1548.002 – Bypass User Account Control
T1112 – Modify Registry
T1574.001 – DLL Search Order Hijacking
T1036.005 – Masquerading: Match Legitimate Resource Name or Location
T1082 – System Information Discovery
T1016 – System Network Configuration Discovery
T1057 – Process Discovery
T1113 – Screen Capture
T1059.003 – Command and Scripting Interpreter: Windows Command Shell
T1102.002 – Web Service: Bidirectional Communication
T1560.001 – Archive Collected Data: Archive via Utility
T1567 – Exfiltration Over Web Service
T1070.004 – Indicator Removal: File Deletion
T1218.010 – Regsvr32
T1005 – Data from Local System
T1074.001 – Data Staged: Local Data Staging
T1041 – Exfiltration Over C2 Channel
T1564.003 – Hide Artifacts: Hidden Window
T1027.013 – Encrypted/Encoded File

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About the author
Tara Gould
Malware Research Lead

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October 5, 2026

AI-Assisted Attacks Still Leave a Behavioral Trace  

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Key Insights

  • Darktrace identified behavioral indicators associated with two campaigns linked to AI-assisted threat activity, highlighting the growing role of AI in modern cyber-attacks.
  • Observed activity involved suspicious WebDAV file transfers, disguised executable downloads, beaconing to rare infrastructure, unusual process execution, and communications with C2 infrastructure linked to active intrusion campaigns.

Introduction

Just as organizations are incorporating AI into their operations to take advantage of its benefits, threat actors are doing the same, creating new challenges for defenders.

Much of the discussion around AI risk has focused on the expanding attack surface created by AI systems within organizations. These systems are often granted privileged access and heightened permissions to carry out their duties, introducing new security risks and unintended consequences.

At the same time, threat actors are learning to leverage AI to enable malicious activities such as vulnerability discovery, exploit creation, and progressing through the Cyber Kill Chain more quickly. By accelerating development, adaptation, and scaling, AI enables attackers to operate more efficiently while making some capabilities more accessible to less skilled operators.

Whether AI is the target or the enabler, the resulting activity still manifests through networks, identities, endpoints and cloud services. Those interactions create observable signals that defenders can investigate, regardless of how the attack was developed.

AI as part of the attacker’s workflow

Darktrace has previously documented how threat actors are increasingly incorporating AI into offensive operations [1]. Two recent investigations from open-source intelligence (OSINT) illustrate this. In both cases, researchers identified the role of AI within malicious operations. Separately, Darktrace detected activity in customer environments that aligned with the infrastructure and techniques reported in those campaigns. These perspectives provide a view of both attacker workflow and operational consequences.

Although AI played different roles in each campaign, it did not remove the need for the attackers to interact with their targets. Payloads still had to be delivered, processes executed, and command-and-control (C2) connections established, creating behavioral anomalies that Darktrace was able to identify.

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Case 1: A Mexican government impersonation campaign with LLM-assisted malware development

Rapid7 reported on a malware delivery operation that used generative AI to assist development, testing, documentation and refinement of attacker infrastructure. Between May and June 2026, Darktrace similarly observed two chains of suspicious activity across customer environments in the Americas that exhibited clear similarities in behavior.

In both cases Darktrace observed:

  • WebDAV communication with onedrive[.]cv·138.124.123[.]87, retrieving a file from the path /Downloads/CURP/
  • Transfer of a masqueraded .scr executable
  • Subsequent communication with google.services[.]ug·77.110.127[.]205 over unusual high ports
  • Additional Darktrace detections correlating the unusual behavior seen spanning payload delivery and C2 communication
  • Darktrace’s Autonomous Response capability alerted across multiple stages of the attack

The infrastructure and behavior observed by Darktrace closely aligned with a campaign reported by Rapid7, in which a WebDAV malware delivery environment was exposed. Rapid7 assessed that threat actors had used generative AI to support the development, testing, documentation and refinement of the operation. The observed activity also aligned with reporting on a campaign in which impersonation of Mexico’s government Unique Population Registry Code (CURP) identity-record service led to delivery of PureRAT, a .NET-based information stealer and remote access trojan (RAT) [2]. The infrastructure overlap and consistent behavioural sequence provides strong alignment and offers a view of how an AI-assisted development pipeline ultimately manifested inside target environments.

Case 2: A suspected China-linked intrusion campaign with AI-assisted automation

In July 2026, Hunt.io published research into a suspected China-based intrusion operation targeting government and financial services organizations [3]. Material recovered from exposed attacker infrastructure by Hunt.io indicated that Claude Code and DeepSeek-v4-pro were being used as active components of the attacker’s workflow. According to the research, the models supported activities including attack reasoning, script generation, execution, exploit adaptation, and phishing-page development.

The investigation identified 192.229.115[.]229 and 192.229.115[.]230 as infrastructure associated with suspected TencShell operations and a possible second C2 framework known as Gshell [3].

Darktrace identified likely related activity within a financial services customer environment involving a newly observed laptop running the Windows 11 Pro operating system. Over a six-day period in July, the device made repeated outbound connections to 192.229.115[.]229 over port 8083.

Darktrace recognized the destination was highly rare for the environment, and the connectivity exhibited beaconing characteristics. During the same timeframe, Darktrace also identified suspicious process behavior associated with process chains involving svchost.exe and cmd.exe. The device repeatedly communicated with infrastructure identified in the Hunt.io research while exhibiting beaconing characteristics and suspicious process activity, strengthening the assessment that the activity likely was associated with the same operation.

Unlike many previous examples of AI-assisted cybercrime, the Hunt.io investigation provided rare visibility into how large language models were being incorporated directly into operational workflows rather than being used solely for content generation. Darktrace, meanwhile, observed how activity associated with that operation ultimately manifested inside a target environment, providing a complementary view of its operational impact.

Operational consequences of AI-assisted attacks

These investigations provide two complementary perspectives on AI-assisted cyber operations. OSINT research revealed how AI was incorporated into attacker workflows, while Darktrace observed the resulting activity within customer environments.

Although AI played different roles in each campaign, it did not remove the need for attackers to interact with their targets, deliver payloads, execute processes, and communicate with C2, all of which generated observable signals.

In these cases, Darktrace identified suspicious file delivery, unusual process behavior, beaconing activity, and communication with rare external infrastructure that aligned with campaigns later linked to AI-assisted operations. While AI may influence how attacks are developed, adapted, and scaled, it does not make them operationally invisible.

For defenders, the broader lesson extends beyond these specific campaigns. As AI becomes increasingly embedded within both enterprise operations and attacker workflows, understanding what a model was asked to do is often less important than understanding the actions it ultimately took and the consequences those actions produced. Whether the actor is human, AI-assisted, or increasingly autonomous, activity still manifests through identities, endpoints, applications, cloud services and network infrastructure.

Credit to Angel Arribas Lopez (Associate Principal Cyber Analyst), Emma Foulger (Global Threat Research Operations Lead), Nathaniel Jones, SVP Global Threat Intelligence
Edited by Ryan Traill (Content Manager)

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Appendices

Darktrace Model Detections

Case 1

Anomalous File / Masqueraded File Transfer from New External Endpoint

Anomalous File / Script from Rare External Location

Anomalous File / EXE from Rare External Location

Anomalous File / Script and EXE from Rare External

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Anomalous Connection / Rare External SSL Self-Signed

Compromise / New or Repeated to Unusual SSL Port

Compromise / Large Number of Suspicious Failed Connections

Device / Initial Attack Chain Activity

Antigena / Network / External Threat::Antigena Suspicious File Block

Antigena / Network / Significant Anomaly::Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly::Antigena Controlled and Model Alert

Antigena / Network / External Threat::Antigena File then New Outbound Block

Antigena / Network / Significant Anomaly::Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly::Antigena Alerts Over Time Block

Case 2

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Compromise / High Volume of Connections with Beacon Score

Compromise / Large Number of Suspicious Failed Connections

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Indicators of Compromise (IoCs)

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Case 1

                                                                                                                                                                                                                                                 
IoCTypeDescription + Confidence
onedrive[.]cvHostnameLikely C2 server
138.124.123[.]87IP AddressPossible C2 server
hXXp://onedrive[.]cv/Downloads/CURP/ReportFinal.%E2%80%AE%E1%BA%9D%D4%81%EF%BD%90.scrURIPossible payload
google.services[.]ugHostnameLikely C2 server
77.110.127[.]205IP AddressLikely C2 server
google.services[.]ug:57666Hostname + PortLikely C2 communication
google.services[.]ug:57888Hostname + PortLikely C2 communication
google.services[.]ug:56001Hostname + PortLikely C2 communication

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Case 2

IoC Type Description + Confidence
192.229.115[.]229 IP Address Likely C2 communication
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About the author
Angel Arribas Lopez
Associate Principal Cyber Analyst
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