In 2024, one industry stood out in the India Cyber Threat Report—not for its technological advancements but for its vulnerability: healthcare. According to India Cyber Threat Report 2025, the healthcare sector accounted for 21.82% of all cyberattacks, making it the most targeted industry in India.
But why is healthcare such a lucrative target for cybercriminals?
The Perfect Storm of Opportunity
Healthcare organizations are in a uniquely precarious position. They house vast amounts of sensitive personal and medical data, operate mission-critical systems, and often lack mature cybersecurity infrastructure. In India, the rapid digitization of healthcare — from hospital management systems to telemedicine — has outpaced the sector’s ability to secure these new digital touchpoints.
This creates a perfect storm: high-value data, low resilience, and high urgency. Threat actors know that healthcare providers are more likely to pay ransoms quickly to restore operations, especially when patient care is on the line.
How Cybercriminals are Attacking
The India Cyber Threat Report highlights a mix of attack vectors used against healthcare organizations:
Ransomware: Threat groups such as LockBit 3.0 and RansomHub deploy advanced ransomware strains that encrypt data and disrupt services. These strains are often delivered through phishing campaigns or unpatched vulnerabilities.
Trojans and Infectious Malware: Malware masquerading as legitimate software is a standard tool for gaining backdoor access to healthcare networks.
Social Engineering and Phishing: Fake communications from supposed government health departments or insurance providers lure healthcare staff into compromising systems.
What Needs to Change
The key takeaway is clear: India’s healthcare organizations need to treat cybersecurity as a core operational function, not an IT side task. Here’s how they can begin to strengthen their cyber posture:
Invest in Behavior-Based Threat Detection: Traditional signature-based antivirus tools are insufficient. As seen in the rise from 12.5% to 14.5% of all malware detections, behavior-based detection is becoming critical to identifying unknown or evolving threats.
Harden Endpoint Security: With 8.44 million endpoints analyzed in the report, it’s evident that endpoint defense is a frontline priority. Solutions like Seqrite Endpoint Security offer real-time protection, ransomware rollback, and web filtering tailored for sensitive environments like hospitals.
Educate and Train Staff: Many successful attacks begin with a simple phishing email. Healthcare workers need regular training on identifying suspicious communications and maintaining cyber hygiene.
Backup and Response Plans: Ensure regular, encrypted backups of critical systems and have an incident response plan ready to reduce downtime and mitigate damage during an attack.
Looking Ahead
The India Cyber Threat Report 2025 is a wake-up call. As threat actors grow more sophisticated — using generative AI for deepfake scams and exploiting cloud misconfigurations — the time for reactive cybersecurity is over.
At Seqrite, we are committed to helping Indian enterprises build proactive, resilient, and adaptive security frameworks, especially in vital sectors like healthcare. Solutions like our Seqrite Threat Intel platform and Malware Analysis Platform (SMAP) are built to give defenders the needed edge.
Cyber safety is not just a technical concern — it’s a human one. Let’s secure healthcare, one system at a time.
A security researcher from Seqrite Labs has uncovered a malicious campaign targeting U.S. citizens as Tax Day approaches on April 15. Seqrite Labs has identified multiple phishing attacks leveraging tax-related themes as a vector for social engineering, aiming to exfiltrate user credentials and deploy malware. These campaigns predominantly utilize redirection techniques, such as phishing emails, and exploit malicious LNK files to further their objectives.
Each year, cybercriminals exploit the tax season as an opportunity to deploy various social engineering tactics to compromise sensitive personal and financial data. These adversaries craft highly deceptive campaigns designed to trick taxpayers into divulging confidential information, making fraudulent to counterfeit services, or inadvertently installing malicious payloads on their devices, thereby exposing them to identity theft and financial loss.
Infection Chain:
Fig 1: Infection chain
Initial analysis about campaign:
While tax-season phishing, attacks pose a risk to a broad spectrum of individuals, our analysis indicates that certain demographics are disproportionately vulnerable. Specifically, high-risk targets include individuals with limited knowledge of government tax processes, such as green card holders, small business owners, and new taxpayers.
Our findings reveal that threat actors are leveraging a sophisticated phishing technique in which they deliver files via email with deceptive extensions. One such example is a file named “104842599782-4.pdf.lnk,” which utilizes a malicious LNK extension. This tactic exploits user trust by masquerading as a legiti payments mate document, ultimately leading to the execution of malicious payloads upon interaction.
Decoy Document:
Threat actors are disseminating a transcript related to tax sessions, targeting individuals through email by sharing it as a malicious attachment. These cybercriminals are leveraging this document as a vector to deliver harmful payloads, thereby compromising the security of the recipients.
Fig 2: Decoy Document
Technical Analysis:
We have retrieved the LNK file, identified as “04842599782-4.pdf.lnk,” which was utilized in the attack. This LNK file embeds a Base64-encoded payload within its structure.
Fig 3: Inside LNK File
Upon decoding the string, we extracted a PowerShell command line that itself contains another Base64-encoded payload embedded within it.
Fig 4: Encoded PowerShell Command Line
Subsequently, upon decoding the nested Base64 string, we uncovered the final PowerShell command line embedded within the payload.
Fig 5: Decoded Command Line
The extracted PowerShell command line initiated the download of rev_pf2_yas.txt, which itself is a PowerShell script (Payload.ps1) containing yet another Base64-encoded payload embedded within it.
Fig 6: 2nd PowerShell command with Base64 Encoded
We have decoded the above Base64 encoded command line and get below final executable.
Fig 7: Decoded PowerShell Command
According to the PowerShell command line, the script Payload.ps1 (or rev_pf2_yas.txt) initiated the download of an additional file, revolaomt.rar, from the Command and Control (C2) server. This archive contained a malicious executable, named either Setup.exe or revolaomt.exe.
Detail analysis of Setup.exe / revolaomt.exe:
Fig 8: Detect it Easy
Upon detailed examination of the Setup.exe binary, it was identified as a PyInstaller-packaged Python executable. Subsequent extraction and decompilation revealed embedded Python bytecode artifacts, including DCTYKS.pyc and additional Python module components.
Fig 9: PyInstaller-packaged Python executableFig 10: In side DCTYKS.pyc
Upon analysis of the DCTYKS.pyc sample, it was determined that the file contains obfuscated or encrypted payload data, which is programmatically decrypted at runtime and subsequently executed, as illustrated in the figure above.
Fig 11: Encoded DCTYKS.pyc with Base64
Upon successful decryption of the script, it was observed that the sample embeds a Base64-encoded executable payload. The decrypted payload leverages process injection techniques to target mstsc.exe for execution. Further analysis of the second-stage payload revealed it to be a .NET-compiled binary.
Analysis 2nd Payload (Stealerium malware):
Fig 12: .NET Base Malware sample
The second-stage payload is identified as a .NET-based malware sample. Upon inspection of its class structures, methods, and overall functionality, the sample exhibits strong behavioural and structural similarities to the Stealerium malware family, specifically aligning with version 1.0.35.
Stealerium is an open-source information-stealing malware designed to exfiltrate sensitive data from web browsers, cryptocurrency wallets, and popular applications such as Discord, Steam, and Telegram. It performs extensive system reconnaissance by harvesting details including active processes, desktop screenshots, and available Wi-Fi network configurations. Additionally, the malware incorporates sophisticated anti-analysis mechanisms to identify execution within virtualized environments and detect the presence of debugging tools.
This AntiAnalysis class is part of malware designed to detect sandbox, virtual machines, emulators, suspicious processes, services, usernames, and more. It checks system attributes against blacklists fetched from online sources (github). If any suspicious environment is detected, it logs the finding and may trigger self-destruction. This helps the malware avoid analysis in controlled or security research setups.
Mutex Creation
Fig 16: Mutex Creation
This MutexControl class prevents multiple instances of the malware from running at the same time. It tries to create a system-wide mutex using a name from Config.Mutex (QT1bm11ocWPx). If the mutex already exists, it means another instance is running, so it exits the process. If an error occurs during this check, it logs the error and exits too.
Fig 17: Configuration of StringsCrypt.DecryptConfig
It configures necessary values by decrypting them with StringsCrypt.DecryptConfig. It handles the decryption of the server base URL and WebSocket address. If enabled, it also decodes cryptocurrency wallet addresses from Base64 and decrypts them using AES-256 encryption.
“hxxp://91.211.249.142:7816”
Radom Directory Creation
Fig 18: Random Directory Creation
The InitWorkDir() method generates a random subdirectory under %LOCALAPPDATA%, creates it if it doesn’t exist, and hides it for stealth purposes. This is likely used for storing data or maintaining persistence without detection.
\AppData\Local\e9d3e2dd2788c322ffd2c9defddf7728 random directory is created in hidden attribute.
BoT Registration
Fig 19: BOT Registration
The RegisterBot method initiates an HTTP POST request to register a bot instance, utilizing a unique hash identifier and an authorization token for authentication. It serializes the registration payload, appends the necessary HTTP headers, and logs the server response or any encountered exceptions. The method returns a boolean value—true upon successful execution, and false if an exception is raised during the process.
It extracts browser-related data (passwords, cookies, credit cards, history, bookmarks, autofill) from a given user data profile path.
FileZilla Credentials stealer activity
Fig 21: FileZilla Credential Stealer activity
The above code is part of a password-stealing component targeting FileZilla, an FTP client.
Gaming Platform Data Extraction Modules
Fig 22: Gaming platform data extraction
This component under bt.Stub.Target.Gaming is designed to collect data from the following platforms:
BattleNet
Minecraft
Steam
Uplay
Each class likely implements routines to extract user data, game configurations, or sensitive files for exfiltration.
Fig 23: Checks for a Minecraft installation
It checks for a Minecraft installation and creates a save directory to exfiltrate various data like mods, files, versions, logs, and screenshots. It conditionally captures logs and screenshots based on the Config.GrabberModule setting.
Messenger Data Stealer Modules
Itargets various communication platforms to extract user data or credentials from:
Discord
Element
ICQ
Outlook
Pidgin
Signal
Skype
Telegram
Tox
Below is one example of Outlook Credentials Harvesting
It targets specific registry keys associated with Outlook profiles to extract sensitive information like email addresses, server names, usernames, and passwords. It gathers data for multiple mail clients (SMTP, POP3, IMAP) and writes the collected information to a file (Outlook.txt).
Fig 24: Messenger Data Extraction
Webcam Screenshot Capture
Attempts to take a screenshot using a connected webcam, saving the image as a JPEG file. If only one camera is connected, it triggers a series of messages to capture the webcam image, which is then saved to the specified path (camera.jpg or a timestamped filename). The method is controlled by a configuration setting (Config.WebcamScreenshot).
Fig 25: Webcam Screen shot captures
Wi-Fi Password Retrieval
It retrieves the Wi-Fi password for a given network profile by running the command netsh wlan show profile and extracting the password from the output. The command uses findstr Key to filter the password, which is then split and trimmed to get the value
Fig 26: WI-FI Password Retrieval
VPN Data Extraction
It targets various VPN applications to exfiltrate sensitive information such as login credentials:
NordVpn
OpenVpn
ProtonVpn
For example, it extracts and saves NordVPN credentials from the user.config file found in NordVPN installation directories. It looks for “Username” and “Password” settings, decodes them, and writes them to a file (accounts.txt) in the specified savePath.
Fig 27: VPN Data Extraction
Porn Detection & Screenshot Capture
Fig 28: Porn Detection & Snapshot Captures.
It detects adult content by checking if the active window’s title contains specific keywords related to NSFW content (configured in Config.PornServices). If such content is detected, it triggers a screenshot capture.
Conclusion:
Based on our recent proactive threat analysis, we’ve identified that cybercriminals are actively targeting U.S. citizens around the tax filing period scheduled for April 15. These threat actors are leveraging the occasion to deploy Stealerium malware, using deceptive tactics to trick users.
Stealerium malware is designed to steal Personally Identifiable Information (PII) from infected devices and transmit it to attacker-controlled bots for further exploitation.
To safeguard your data and devices, we strongly recommend using Seqrite Endpoint Security, which provides advanced protection against such evolving threats.
Stay secure. Stay protected with Seqrite.
TTPS
Tactic
Technique ID
Name
Initial Access
T1566.001
Phishing: Spear phishing Attachment
Execution
T1059.001
Command and Scripting Interpreter: PowerShell
Evasion
T1140
Deobfuscate/Decode Files or Information
T1027
Obfuscated Files or Information
T1497
Virtualization/Sandbox Evasion
T1497.001
System Checks
Credential Access
T1555.003
Credentials from Password Stores: Credentials from Web Browsers
T1539
Steal Web Session Cookie
Discovery
T1217
Browser Information Discovery
T1016
System Network Configuration Discovery: Wi-Fi Discovery
Collection
T1113
Screen Capture
Exfiltration
T1567.004
Exfiltration Over Web Service: Exfiltration Over Webhook