I carried out a static analysis of DeepSeek, a Chinese LLM chatbot, using version 1.8.0 from the Google Play Store. The objective was to identify potential security and privacy issues.

I have actually discussed DeepSeek previously here.

Additional security and personal privacy issues about DeepSeek have actually been raised.

See also this analysis by NowSecure of the iPhone version of DeepSeek

The findings detailed in this report are based purely on static analysis. This suggests that while the code exists within the app, there is no conclusive proof that all of it is executed in practice. Nonetheless, the existence of such code warrants scrutiny, particularly given the growing issues around information privacy, monitoring, the potential abuse of AI-driven applications, and cyber-espionage dynamics in between international powers.

Key Findings

Suspicious Data Handling & Exfiltration

- Hardcoded URLs direct data to external servers, raising issues about user activity monitoring, such as to ByteDance "volce.com" endpoints. NowSecure determines these in the iPhone app the other day too.

• Bespoke file encryption and data obfuscation approaches exist, with indicators that they could be used to exfiltrate user details.
• The app contains hard-coded public secrets, instead of relying on the user gadget's chain of trust.
• UI interaction tracking captures detailed user behavior without clear consent. - WebView control exists, which could enable for the app to gain access to private external internet browser information when links are opened. More details about WebView adjustments is here
  
  Device Fingerprinting & Tracking
  
  A substantial portion of the analyzed code appears to focus on gathering device-specific details, which can be utilized for tracking and fingerprinting.
  
  - The app collects different distinct gadget identifiers, including UDID, Android ID, IMEI, IMSI, and provider details.
• System residential or commercial properties, set up bundles, and lovewiki.faith root detection mechanisms recommend possible anti-tampering measures. E.g. probes for the presence of Magisk, a tool that privacy advocates and security researchers use to root their . - Geolocation and network profiling are present, showing possible tracking capabilities and making it possible for or disabling of fingerprinting programs by region. - Hardcoded gadget model lists recommend the application may behave in a different way depending upon the identified hardware.
• Multiple vendor-specific services are used to draw out extra gadget details. E.g. if it can not identify the device through standard Android SIM lookup (because permission was not approved), it tries manufacturer particular extensions to access the same details.
  
  Potential Malware-Like Behavior
  
  While no conclusive conclusions can be drawn without vibrant analysis, a number of observed habits align with recognized spyware and malware patterns:
  
  - The app uses reflection and UI overlays, which could assist in unapproved screen capture or phishing attacks.
• SIM card details, identification numbers, and other device-specific data are aggregated for unknown functions.
• The app implements country-based gain access to constraints and "risk-device" detection, recommending possible security systems.
• The app executes calls to load Dex modules, where extra code is loaded from files with a.so extension at runtime.
• The.so submits themselves reverse and make extra calls to dlopen(), which can be used to fill additional.so files. This center is not normally examined by Google Play Protect and other fixed analysis services.
• The.so files can be implemented in native code, such as C++. The usage of native code adds a layer of intricacy to the analysis process and obscures the full level of the app's abilities. Moreover, native code can be leveraged to more quickly intensify opportunities, possibly making use of vulnerabilities within the operating system or device hardware.
  
  Remarks
  
  While information collection prevails in modern applications for debugging and enhancing user experience, aggressive fingerprinting raises substantial privacy issues. The DeepSeek app requires users to visit with a legitimate email, which ought to already supply sufficient authentication. There is no valid factor for the app to aggressively gather and send unique gadget identifiers, IMEI numbers, SIM card details, and other non-resettable system properties.
  
  The extent of tracking observed here surpasses common analytics practices, potentially enabling relentless user tracking and re-identification across gadgets. These behaviors, integrated with obfuscation strategies and wiki.vst.hs-furtwangen.de network interaction with third-party tracking services, warrant a higher level of scrutiny from security researchers and users alike.
  
  The work of runtime code loading in addition to the bundling of native code suggests that the app could allow the release and execution of unreviewed, from another location provided code. This is a serious prospective attack vector. No proof in this report is provided that remotely released code execution is being done, just that the center for this appears present.
  
  Additionally, the app's technique to spotting rooted devices appears excessive for an AI chatbot. Root detection is often justified in DRM-protected streaming services, where security and material security are critical, or in competitive video games to avoid unfaithful. However, there is no clear rationale for such stringent steps in an application of this nature, raising more questions about its intent.
  
  Users and organizations considering installing DeepSeek needs to understand these possible risks. If this application is being used within a business or federal government environment, extra vetting and security controls should be enforced before allowing its deployment on managed devices.
  
  Disclaimer: The analysis presented in this report is based upon static code review and does not imply that all discovered functions are actively utilized. Further examination is needed for definitive conclusions.