Blog
/
Identity
/
April 29, 2025

MFA Under Attack: AiTM Phishing Kits Abusing Legitimate Services

Tycoon 2FA uses AiTM phishing and legitimate services to bypass MFA. Darktrace AI stopped it, read the blog to learn how Self-Learning AI detects sophisticated threats.
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
Alexandra Sentenac
Cyber Analyst
fingerprintDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
29
Apr 2025

In late 2024 and early 2025, the Darktrace Security Operations Center (SOC) investigated alerts regarding separate cases of Software-as-a-Service (SaaS) account compromises on two customer environments that presented several similarities, suggesting they were part of a wider phishing campaign.

This campaign was found to leverage the project collaboration and note-taking application, Milanote, and the Tycoon 2FA phishing kit.

Legitimate services abused

As highlighted in Darktrace's 2024 Annual Threat Report [1], threat actors are abusing legitimate services, like Milanote, in their phishing campaigns. By leveraging these trusted platforms and domains, malicious actors can bypass traditional security measures, making their phishing emails appear benign and increasing the likelihood of successful attacks.

Darktrace categorizes these senders and platforms as free content senders. These services allow users to send emails containing custom content (e.g., files) from fully validated, fixed service address belonging to legitimate corporations. Although some of these services permit full body and subject customization by attackers, the structure of these emails is generally consistent, making it challenging to differentiate between legitimate and malicious emails.

What is Tycoon 2FA?

Tycoon 2FA is an Adversary-in-the-Middle (AitM) phishing kit, first seen in August 2023 and distributed via the Phishing-as-a-Service (PhaaS) model [2]. It targets multi-factor authentication (MFA) by intercepting credentials and MFA tokens during authentication on fake Microsoft or Google login pages. The attacker captures session cookies after MFA is completed, allowing them to replay the session and access the user account, even if credentials are reset. The rise in MFA use has increased the popularity of AitM phishing kits like Tycoon 2FA and Mamba 2FA, another AiTM phishing kit investigated by Darktrace.

Initial access via phishing email

At the beginning of 2025, Darktrace observed phishing emails leveraging Milanote being sent to multiple internal recipients in an organization. In this attack, the same email was sent to 19 different users, all of which were held by Darktrace.

The subject line of the emails mentioned both a legitimate internal user of the company, the company name, as well as a Milanote board regarding a “new agreement” in German. It is a common social engineering technique to mention urgent matters, such as unpaid invoices, expired passwords, or awaiting voicemails, in the subject line to prompt immediate action from the user. However, this tactic is now widely covered in phishing awareness training, making users more suspicious of such emails. In this case, while the subject mentioned a “new agreement,” likely raising the recipient’s curiosity, the tone remained professional and not overly alarming. Additionally, the mention of a colleague and the standardized language typical of free content sender emails further helped dispel concerns regarding the email.

These emails were sent by the legitimate address support@milanote[.]com and referenced "Milanote" in the personal field of the header but originated from the freemail address “ahnermatternk.ef.od.13@gmail[.]com”. Darktrace / EMAIL recognized that none of the recipients had previously received a file share email from Milanote, making this sender unfamiliar in the customer's email environment

The emails contained several benign links to legitimate Milanote endpoints (including an unsubscribe link) which were not flagged by Darktrace. However, they also included a malicious link designed to direct recipients to a pre-filled credential harvesting page hosted on Milanote, prompting them to register for an account. Despite not blocking the legitimate Milanote links in the same email, Darktrace locked the malicious link, preventing users from visiting the credential harvester.

Credential harvesting page sent to recipients, as seen in. sandbox environment.
Figure 1: Credential harvesting page sent to recipients, as seen in. sandbox environment.

Around one minute later, one recipient received a legitimate email from Milanote confirming their successful account registration, indicating they had accessed the phishing page. This email had a lower anomaly score and was not flagged by Darktrace / EMAIL because, unlike the first email, it did not contain any suspicious links and was a genuine account registration notification. Similarly, in the malicious Milanote email, only the link leading to the phishing page was blocked, while the benign and legitimate Milanote links remained accessible, demonstrating Darktrace’s precise and targeted actioning.

A legitimate and a malicious Milanote email received by one recipient.
Figure 2: A legitimate and a malicious Milanote email received by one recipient.

Around the same time, Darktrace / NETWORK observed the same user’s device making DNS query for the domain name “lrn.ialeahed[.]com” , which has been flagged as a Tycoon 2FA domain [2], suggesting the use of this phishing platform.

Once the user had entered their details in the credential harvester, it is likely that they were presented a document hosted on Milanote that contained the final payload link – likely hidden behind text instructing users to access a “new agreement” document.

External research indicates that the user was likely directed to a Cloudflare Turnstile challenge meant to reroute unwanted traffic, such as automated security scripts and penetration testing tools [2] [3]. After these checks and other background processes are completed, the user is directed to the final landing page. In this case, it was likely a fake login prompt hosted on the attacker’s server, where the user is asked to authenticate to their account using MFA. By burrowing malicious links and files in this manner, threat actors can evade analysis by traditional security email gateways, effectively bypassing their protection.

Darktrace’s analysis of the structure and word content of the phishing emails resulted in an 82% probability score that the email was malicious, and the email further received a 67% phishing inducement score, representing how closely the structure and word content of the emails compared to typical phishing emails.

All these unusual elements triggered multiple alerts in Darktrace / EMAIL, focusing on two main suspicious aspects: a new, unknown sender with no prior correspondence with the recipients or the environment, and the inclusion of a link to a previously unseen file storage solution.

Milanote phishing email as seen within Darktrace / EMAIL.
Figure 3: Milanote phishing email as seen within Darktrace / EMAIL.

After detecting the fifth email, the “Sender Surge” model alert was triggered in Darktrace / EMAIL due to a significant number of recipients being emailed by this new suspicious sender in a short period. These recipients were from various departments across the customer’s organization, including sales, marketing, purchasing, and production. Darktrace / EMAIL determined that the emails were sent to a highly unusual group of internal recipients, further raising doubts about the business legitimacy.

Darktrace / EMAIL suggested actions to contain the attack by holding all Milanote phishing emails back from recipient’s inboxes, except for the detailed email with locked links. However, autonomous actions were not enabled at the time, allowing the initial email to reach recipients' inboxes, providing a brief window for interaction. Unfortunately, during this window, one recipient clicked on the Milanote payload link, leading to the compromise of their account.

SaaS account takeover

About three minutes after the malicious Milanote email was received, Darktrace / IDENTITY detected an unusual login to the email recipient’s SaaS account. The SaaS actor was observed accessing files from their usual location in Germany, while simultaneously, a 100% rare login occurred from a location in the US that had never been seen in the customer’s environment before. This login was also flagged as suspicious by Microsoft 365, triggering a 'Conditional Access Policy' that required MFA authentication, which was successfully completed.

Tycoon 2FA adnimistration panel login page dated from October 2023 [3].
Figure 4: Tycoon 2FA adnimistration panel login page dated from October 2023 [3].

Despite the successful authentication, Darktrace / IDENTITY recognized that the login from this unusual location, coupled with simultaneous activity in another geographically distant location, were highly suspicious. Darktrace went on to observe MFA-validated logins from three separate US-based IP addresses: 89.185.80[.]19, 5.181.3[.]68, and 38.242.7[.]252. Most of the malicious activity was performed from the latter, which is associated with the Hide My Ass (HMA) VPN network [5].

Darktrace’s detection of the suspicious login from the US while the legitimate user was logged in from Germany.
Figure 5: Darktrace’s detection of the suspicious login from the US while the legitimate user was logged in from Germany.
Darktrace’s detection of the suspicious login following successful MFA authentication.
Figure 6: Darktrace’s detection of the suspicious login following successful MFA authentication.

Following this, the malicious actor accessed the user’s inbox and created a new mailbox rule named “GTH” that deleted any incoming email containing the string “milanote” in the subject line or body. Rules like this are a common technique used by attackers to leverage compromised accounts for launching phishing campaigns and concealing replies to phishing emails that might raise suspicions among legitimate account holders. Using legitimate, albeit compromised, accounts to send additional phishing emails enhances the apparent legitimacy of the malicious emails. This tactic has been reported as being used by Tycoon 2FA attackers [4].

The attacker accessed over 140 emails within the legitimate user’s inbox, including both the inbox and the “Sent Items” folder. Notably, the attacker accessed five emails in the “Sent Items” folder and modified their attachments. These emails were mainly related to invoices, suggesting the threat actor may have been looking to hijack those email threads to send fake invoices or replicate previous invoice emails.

Darktrace’s Cyber AI AnalystTM launched autonomous investigations into the individual events surrounding this suspicious activity. It connected these separate events into a single, broad account takeover incident, providing the customer with a clearer view of the ongoing compromise.

Cyber AI Analyst’s detection of unusual SaaS account activities in a single incident.
Figure 7: Cyber AI Analyst’s detection of unusual SaaS account activities in a single incident.
Cyber AI Analyst investigation of suspicious activities performed by the attacker.
Figure 8: Cyber AI Analyst investigation of suspicious activities performed by the attacker.

Darktrace's response

Within three minutes of the first unusual login alert, Darktrace’s Autonomous Response intervened, disabling the compromised user account for two hours.

As the impacted customer was subscribed to the Managed Threat Detection Service, Darktrace’s SOC team investigated the activity further and promptly alerted the customer’s security team. With the user’s account still disabled by Autonomous Response, the attack was contained, allowing the customer’s security team valuable time to investigate and remediate. Within ten minutes of receiving the alert from Darktrace’s SOC, they reset the user’s password, closed all active SaaS sessions, and deleted the malicious email rule. Darktrace’s SOC further supported the customer through the Security Operations Service Support service by providing information about the data accessed and identifying any other affected users.

Autonomous Response actions carried out by Darktrace / IDENTITY to contain the malicious activity
Figure 9: Autonomous Response actions carried out by Darktrace / IDENTITY to contain the malicious activity.

A wider Milanote phishing campaign?

Around a month before this compromise activity, Darktrace alerted another customer to similar activities involving two compromised user accounts. These accounts created new inbox rules named “GFH” and “GVB” to delete all incoming emails containing the string “milanote” in their subject line and/or body.

The phishing emails that led to the compromise of these user accounts were similar to the ones discussed above. Specifically, these emails were sent via the Milanote platform and referenced a “new agreement” (in Spanish) being shared by a colleague. Additionally, the payload link included in the phishing emails showed the same UserPrincipalName (UPN) attribute (i.e., click?upn=u001.qLX9yCzR), which has been seen in other Milanote phishing emails leveraging Tycoon 2FA reported by OSINT sources [6]. Interestingly, in some cases, the email also referenced a “new agreement” in Portuguese, indicating a global campaign.

Based on the similarities in the rule’s naming convention and action, as well as the similarities in the phishing email subjects, it is likely that these were part of the same campaign leveraging Milanote and Tycoon 2FA to compromise user accounts. Since its introduction, the Tycoon 2FA phishing kit has undergone several enhancements to increase its stealth and obfuscation methods, making it harder for security tools to detect. For example, the latest versions contain special source code to obstruct web page analysis by defenders, prevent users from copying meaningful text from the phishing webpages, and disable the right-click menu to prevent offline analysis [4].

Conclusion

Threat actors are continually employing new methods to bypass security detection tools and measures. As highlighted in this blog, even robust security mechanisms like MFA can be compromised using AitM phishing kits. The misuse of legitimate services such as Milanote for malicious purposes can help attackers evade traditional email security solutions by blurring the distinction between legitimate and malicious content.

This is why security tools based on anomaly detection are crucial for defending against such attacks. However, user awareness is equally important. Delays in processing can impact the speed of response, making it essential for users to be informed about these threats.

[related-resource]

Appendices

References

[1] https://www.darktrace.com/resources/annual-threat-report-2024

[2] https://www.validin.com/blog/tycoon_2fa_analyzing_and_hunting_phishing-as-a-service_domains

[3] https://blog.sekoia.io/tycoon-2fa-an-in-depth-analysis-of-the-latest-version-of-the-aitm-phishing-kit/#h-iocs-amp-technical-details

[4] https://blog.barracuda.com/2025/01/22/threat-spotlight-tycoon-2fa-phishing-kit

[5] https://spur.us/context/38.242.7.252    

[6] https://any.run/report/5ef1ac94e4c6c1dc35579321c206453aea80d414108f9f77abd2e2b03ffbd658/be5351d9-53c0-470b-8708-ee2e29300e70

Indicators of Compromise (IoCs)

IoC         Type      Description + Probability

89.185.80[.]19 - IP Address - Malicious login

5.181.3[.]68 - IP Address -Malicious login

38.242.7[.]252 - IP Address - Malicious login and new email inbox rule creation -  Hide My Ass VPN

lrn.ialeahed[.]com – Hostname - Likely Tycoon 2FA domain

Darktrace Model Detections

Email alerts

Platforms / Free Content Sender + High Sender Surge

Platforms / Free Content Sender + Sender Surge

Platforms / Free Content Sender + Unknown Initiator

Platforms / Free Content Sender

Platforms / Free Content Sender + First Time Recipient

Unusual / New Sender Surge

Unusual / Sender Surge

Antigena Anomaly / High Antigena Anomaly

Association / Unknown Sender

History / New Sender

Link / High Rarity Link to File Storage

Link/ Link To File Storage

Link / Link to File Storage + Unknown Sender

Link / Low Link Association

Platforms / Free Content Sender + First Time Initiator

Platforms / Free Content Sender + Unknown Initiator + Freemail

Platforms / Free Content Sender Link

Unusual / Anomalous Association

Unusual / Unlikely Recipient Association

IDENTITY

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compromise / Login from Rare High Risk Endpoint

SaaS / Access / M365 High Risk Level Login

SaaS / Compromise / Login From Rare Endpoint While User Is Active

SaaS / Access / MailItemsAccessed from Rare Endpoint

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

SaaS / Compliance / Anomalous New Email Rule

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS User Block

Antigena / SaaS / Antigena Unusual Activity Block

Antigena / SaaS / Antigena Suspicious SaaS and Email Activity Block

Cyber AI Analyst Incident

Possible Hijack of Office365 Account

MITRE ATT&CK Mapping

Tactic – Technique

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - Cloud Accounts

INITIAL ACCESS - Phishing

CREDENTIAL ACCESS - Steal Web Session Cookie

PERSISTENCE - Account Manipulation

PERSISTENCE - Outlook Rules

RESOURCE DEVELOPMENT - Email Accounts

RESOURCE DEVELOPMENT - Compromise Accounts

Experts breakdown of Identity Security in the Cloud Era

This white paper discusses the current identity threat landscape and how defenders can adopt new tools to better secure their users and data.

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
Alexandra Sentenac
Cyber Analyst

More in this series

No items found.

Blog

/

Network

/

February 6, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

AppleScript Abuse: Unpacking a macOS Phishing CampaignDefault blog imageDefault blog image

Introduction

Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.

The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.

Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].

Technical analysis

The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.

The AppleScript header prompting execution of the script.
Figure 1: The AppleScript header prompting execution of the script.

Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.

Curl request to receive the next stage.
Figure 2: Curl request to receive the next stage.

This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.

The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].

 Fake dialog prompt for system password.
Figure 3: Fake dialog prompt for system password.

The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.

Figure 4: Requirements gathered on trusted binary.

Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, Script Editor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]

To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].

Example of how users interact with TCC.
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.

After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:

  • Full disk access
  • Screen recording
  • Accessibility
  • Camera
  • Apple Events 
  • Input monitoring

The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.

Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.

 Snippet of decoded Base64 response.
Figure 7: Snippet of decoded Base64 response.

A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:

/controller.php?req=instd

/controller.php?req=tell

/controller.php?req=skip

These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.

LaunchAgent patterns to be replaced with victim information.
Figure 8: LaunchAgent patterns to be replaced with victim information.

The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.

Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.

Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met.  However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.

Conclusion

This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.

Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections.  These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.

Credit to Tara Gould (Malware Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

88.119.171[.]59

sevrrhst[.]com

https://sevrrhst[.]com/inc/register.php?req=next

https://stomcs[.]com/inc/register.php?req=next
https://techcross-es[.]com

Confirmation_Token_Vesting.docx.scpt - d3539d71a12fe640f3af8d6fb4c680fd

EDD_Questionnaire_Individual_Blank_Form.docx.scpt - 94b7392133935d2034b8169b9ce50764

Investor Profile (Japan-based) - Shiro Arai.pdf.scpt - 319d905b83bf9856b84340493c828a0c

MITRE ATTACK

T1566 - Phishing

T1059.002 - Command and Scripting Interpreter: Applescript

T1059.004 – Command and Scripting Interpreter: Unix Shell

T1059.007 – Command and Scripting Interpreter: JavaScript

T1222.002 – File and Directory Permissions Modification

T1036.005 – Masquerading: Match Legitimate Name or Location

T1140 – Deobfuscate/Decode Files or Information

T1547.001 – Boot or Logon Autostart Execution: Launch Agent

T1553.006 – Subvert Trust Controls: Code Signing Policy Modification

T1082 – System Information Discovery

T1057 – Process Discovery

T1105 – Ingress Tool Transfer

References

[1] https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos

[2] https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

[3] https://www.darktrace.com/blog/crypto-wallets-continue-to-be-drained-in-elaborate-social-media-scam

[4] https://developer.apple.com/documentation/appkit

[5] https://www.huntress.com/blog/full-transparency-controlling-apples-tcc

Continue reading
About the author
Tara Gould
Malware Research Lead

Blog

/

Network

/

February 5, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

darktace malware analysis snappybeeDefault blog imageDefault blog image

Introduction

The aim of this blog is to be an educational resource, documenting how an analyst can perform malware analysis techniques such as unpacking. This blog will demonstrate the malware analysis process against well-known malware, in this case SnappyBee.

SnappyBee (also known as Deed RAT) is a modular backdoor that has been previously attributed to China-linked cyber espionage group Salt Typhoon, also known as Earth Estries [1] [2]. The malware was first publicly documented by TrendMicro in November 2024 as part of their investigation into long running campaigns targeting various industries and governments by China-linked threat groups.

In these campaigns, SnappyBee is deployed post-compromise, after the attacker has already obtained access to a customer's system, and is used to establish long-term persistence as well as deploying further malware such as Cobalt Strike and the Demodex rootkit.

To decrease the chance of detection, SnappyBee uses a custom packing routine. Packing is a common technique used by malware to obscure its true payload by hiding it and then stealthily loading and executing it at runtime. This hinders analysis and helps the malware evade detection, especially during static analysis by both human analysts and anti-malware services.

This blog is a practical guide on how an analyst can unpack and analyze SnappyBee, while also learning the necessary skills to triage other malware samples from advanced threat groups.

First principles

Packing is not a new technique, and threat actors have generally converged on a standard approach. Packed binaries typically feature two main components: the packed data and an unpacking stub, also called a loader, to unpack and run the data.

Typically, malware developers insert a large blob of unreadable data inside an executable, such as in the .rodata section. This data blob is the true payload of the malware, but it has been put through a process such as encryption, compression, or another form of manipulation to render it unreadable. Sometimes, this data blob is instead shipped in a different file, such as a .dat file, or a fake image. When this happens, the main loader has to read this using a syscall, which can be useful for analysis as syscalls can be easily identified, even in heavily obfuscated binaries.

In the main executable, malware developers will typically include an unpacking stub that takes the data blob, performs one or more operations on it, and then triggers its execution. In most samples, the decoded payload data is loaded into a newly allocated memory region, which will then be marked as executable and executed. In other cases, the decoded data is instead dropped into a new executable on disk and run, but this is less common as it increases the likelihood of detection.

Finding the unpacking routine

The first stage of analysis is uncovering the unpacking routine so it can be reverse engineered. There are several ways to approach this, but it is traditionally first triaged via static analysis on the initial stages available to the analyst.

SnappyBee consists of two components that can be analyzed:

  • A Dynamic-link Library (DLL) that acts as a loader, responsible for unpacking the malicious code
  • A data file shipped alongside the DLL, which contains the encrypted malicious code

Additionally, SnappyBee includes a legitimate signed executable that is vulnerable to DLL side-loading. This means that when the executable is run, it will inadvertently load SnappyBee’s DLL instead of the legitimate one it expects. This allows SnappyBee to appear more legitimate to antivirus solutions.

The first stage of analysis is performing static analysis of the DLL. This can be done by opening the DLL within a disassembler such as IDA Pro. Upon opening the DLL, IDA will display the DllMain function, which is the malware’s initial entry point and the first code executed when the DLL is loaded.

The DllMain function
Figure 1: The DllMain function

First, the function checks if the variable fdwReason is set to 1, and exits if it is not. This variable is set by Windows to indicate why the DLL was loaded. According to Microsoft Developer Network (MSDN), a value of 1 corresponds to DLL_PROCESS_ATTACH, meaning “The DLL is being loaded into the virtual address space of the current process as a result of the process starting up or as a result of a call to LoadLibrary” [3]. Since SnappyBee is known to use DLL sideloading for execution, DLL_PROCESS_ATTACH is the expected value when the legitimate executable loads the malicious DLL.

SnappyBee then uses the GetModule and GetProcAddress to dynamically resolve the address of the VirtualProtect in kernel32 and StartServiceCtrlDispatcherW in advapi32. Resolving these dynamically at runtime prevents them from showing up as a static import for the module, which can help evade detection by anti-malware solutions. Different regions of memory have different permissions to control what they can be used for, with the main ones being read, write, and execute. VirtualProtect is a function that changes the permissions of a given memory region.

SnappyBee then uses VirtualProtect to set the memory region containing the code for the StartServiceCtrlDispatcherW function as writable. It then inserts a jump instruction at the start of this function, redirecting the control flow to one of the SnappyBee DLL’s other functions, and then restores the old permissions.

In practice, this means when the legitimate executable calls StartServiceCtrlDispatcherW, it will immediately hand execution back to SnappyBee. Meanwhile, the call stack now appears more legitimate to outside observers such as antimalware solutions.

The hooked-in function then reads the data file that is shipped with SnappyBee and loads it into a new memory allocation. This pattern of loading the file into memory likely means it is responsible for unpacking the next stage.

The start of the unpacking routine that reads in dbindex.dat.
Figure 2: The start of the unpacking routine that reads in dbindex.dat.

SnappyBee then proceeds to decrypt the memory allocation and execute the code.

The memory decryption routine.
Figure 3: The memory decryption routine.

This section may look complex, however it is fairly straight forward. Firstly, it uses memset to zero out a stack variable, which will be used to store the decryption key. It then uses the first 16 bytes of the data file as a decryption key to initialize the context from.

SnappyBee then calls the mbed_tls_arc4_crypt function, which is a function from the mbedtls library. Documentation for this function can be found online and can be referenced to better understand what each of the arguments mean [4].

The documentation for mbedtls_arc4_crypt.
Figure 4: The documentation for mbedtls_arc4_ crypt.

Comparing the decompilation with the documentation, the arguments SnappyBee passes to the function can be decoded as:

  • The context derived from 16-byte key at the start of the data is passed in as the context in the first parameter
  • The file size minus 16 bytes (to account for the key at the start of the file) is the length of the data to be decrypted
  • A pointer to the file contents in memory, plus 16 bytes to skip the key, is used as the input
  • A pointer to a new memory allocation obtained from VirtualAlloc is used as the output

So, putting it all together, it can be concluded that SnappyBee uses the first 16 bytes as the key to decrypt the data that follows , writing the output into the allocated memory region.

SnappyBee then calls VirtualProtect to set the decrypted memory region as Read + Execute, and subsequently executes the code at the memory pointer. This is clearly where the unpacked code containing the next stage will be placed.

Unpacking the malware

Understanding how the unpacking routine works is the first step. The next step is obtaining the actual code, which cannot be achieved through static analysis alone.

There are two viable methods to retrieve the next stage. The first method is implementing the unpacking routine from scratch in a language like Python and running it against the data file.

This is straightforward in this case, as the unpacking routine in relatively simple and would not require much effort to re-implement. However, many unpacking routines are far more complex, which leads to the second method: allowing the malware to unpack itself by debugging it and then capturing the result. This is the approach many analysts take to unpacking, and the following will document this method to unpack SnappyBee.

As SnappyBee is 32-bit Windows malware, debugging can be performed using x86dbg in a Windows sandbox environment to debug SnappyBee. It is essential this sandbox is configured correctly, because any mistake during debugging could result in executing malicious code, which could have serious consequences.

Before debugging, it is necessary to disable the DYNAMIC_BASE flag on the DLL using a tool such as setdllcharacteristics. This will stop ASLR from randomizing the memory addresses each time the malware runs and ensures that it matches the addresses observed during static analysis.

The first place to set a breakpoint is DllMain, as this is the start of the malicious code and the logical place to pause before proceeding. Using IDA, the functions address can be determined; in this case, it is at offset 10002DB0. This can be used in the Goto (CTRL+G) dialog to jump to the offset and place a breakpoint. Note that the “Run to user code” button may need to be pressed if the DLL has not yet been loaded by x32dbg, as it spawns a small process to load the DLL as DLLs cannot be executed directly.

The program can then run until the breakpoint, at which point the program will pause and code recognizable from static analysis can be observed.

Figure 5: The x32dbg dissassembly listing forDllMain.

In the previous section, this function was noted as responsible for setting up a hook, and in the disassembly listing the hook address can be seen being loaded at offset 10002E1C. It is not necessary to go through the whole hooking process, because only the function that gets hooked in needs to be run. This function will not be naturally invoked as the DLL is being loaded directly rather than via sideloading as it expects. To work around this, the Extended Instruction Pointer (EIP) register can be manipulated to point to the start of the hook function instead, which will cause it to run instead of the DllMain function.

To update EIP, the CRTL+G dialog can again be used to jump to the hook function address (10002B50), and then the EIP register can be set to this address by right clicking the first instruction and selecting “Set EIP here”. This will make the hook function code run next.

Figure 6: The start of the hookedin-in function

Once in this function, there are a few addresses where breakpoints should be set in order to inspect the state of the program at critical points in the unpacking process. These are:

-              10002C93, which allocates the memory for the data file and final code

-              10002D2D, which decrypts the memory

-              10002D81, which runs the unpacked code

Setting these can be done by pressing the dot next to the instruction listing, or via the CTRL+G Goto menu.

At the first breakpoint, the call to VirtualAlloc will be executed. The function returns the memory address of the created memory region, which is stored in the EAX register. In this case, the region was allocated at address 00700000.

Figure 7: The result of the VirtualAlloc call.

It is possible to right click the address and press “Follow in dump” to pin the contents of the memory to the lower pane, which makes it easy to monitor the region as the unpacking process continues.

Figure 8: The allocated memory region shown in x32dbg’s dump.

Single-stepping through the application from this point eventually reaches the call to ReadFile, which loads the file into the memory region.

Figure 9: The allocated memory region after the file is read into it, showing high entropy data.

The program can then be allowed to run until the next breakpoint, which after single-stepping will execute the call to mbedtls_arc4_crypt to decrypt the memory. At this point, the data in the dump will have changed.

Figure 10: The same memory region after the decryption is run, showing lower entropy data.

Right-clicking in the dump and selecting "Disassembly” will disassemble the data. This yields valid shell code, indicating that the unpacking succeeded, whereas corrupt or random data would be expected if the unpacking had failed.

Figure 11: The disassembly view of the allocated memory.

Right-clicking and selecting “Follow in memory map” will show the memory allocation under the memory map view. Right-clicking this then provides an option to dump the entire memory block to file.

Figure 12: Saving the allocated memory region.

This dump can then be opened in IDA, enabling further static analysis of the shellcode. Reviewing the shellcode, it becomes clear that it performs another layer of unpacking.

As the debugger is already running, the sample can be allowed to execute up to the final breakpoint that was set on the call to the unpacked shellcode. Stepping into this call will then allow debugging of the new shellcode.

The simplest way to proceed is to single-step through the code, pausing on each call instruction to consider its purpose. Eventually, a call instruction that points to one of the memory regions that were assigned will be reached, which will contain the next layer of unpacked code. Using the same disassembly technique as before, it can be confirmed that this is more unpacked shellcode.

Figure 13: The unpacked shellcode’s call to RDI, which points to more unpacked shellcode. Note this screenshot depicts the 64-bit variant of SnappyBee instead of 32-bit, however the theory is the same.

Once again, this can be dumped out and analyzed further in IDA. In this case, it is the final payload used by the SnappyBee malware.

Conclusion

Unpacking remains one of the most common anti-analysis techniques and is a feature of most sophisticated malware from threat groups. This technique of in-memory decryption reduces the forensic “surface area” of the malware, helping it to evade detection from anti-malware solutions. This blog walks through one such example and provides practical knowledge on how to unpack malware for deeper analysis.

In addition, this blog has detailed several other techniques used by threat actors to evade analysis, such as DLL sideloading to execute code without arising suspicion, dynamic API resolving to bypass static heuristics, and multiple nested stages to make analysis challenging.

Malware such as SnappyBee demonstrates a continued shift towards highly modular and low-friction malware toolkits that can be reused across many intrusions and campaigns. It remains vital for security teams  to maintain the ability to combat the techniques seen in these toolkits when responding to infections.

While the technical details of these techniques are primarily important to analysts, the outcomes of this work directly affect how a Security Operations Centre (SOC) operates at scale. Without the technical capability to reliably unpack and observe these samples, organizations are forced to respond without the full picture.

The techniques demonstrated here help close that gap. This enables security teams to reduce dwell time by understanding the exact mechanisms of a sample earlier, improve detection quality with behavior-based indicators rather than relying on hash-based detections, and increase confidence in response decisions when determining impact.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

SnappyBee Loader 1 - 25b9fdef3061c7dfea744830774ca0e289dba7c14be85f0d4695d382763b409b

SnappyBee Loader 2 - b2b617e62353a672626c13cc7ad81b27f23f91282aad7a3a0db471d84852a9ac          

SnappyBee Payload - 1a38303fb392ccc5a88d236b4f97ed404a89c1617f34b96ed826e7bb7257e296

References

[1] https://www.trendmicro.com/en_gb/research/24/k/earth-estries.html

[2] https://www.darktrace.com/blog/salty-much-darktraces-view-on-a-recent-salt-typhoon-intrusion

[3] https://learn.microsoft.com/en-us/windows/win32/dlls/dllmain#parameters

[4] https://mbed-tls.readthedocs.io/projects/api/en/v2.28.4/api/file/arc4_8h/#_CPPv418mbedtls_arc4_cryptP20mbedtls_arc4_context6size_tPKhPh

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer
Your data. Our AI.
Elevate your network security with Darktrace AI