I carried out a fixed analysis of DeepSeek, a Chinese LLM chatbot, utilizing version 1.8.0 from the Google Play Store. The goal was to identify possible security and privacy issues.

I've written about DeepSeek previously here.

Additional security and privacy concerns about DeepSeek have been raised.

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

The findings detailed in this report are based simply on static analysis. This indicates that while the code exists within the app, there is no conclusive evidence that all of it is carried out in practice. Nonetheless, the presence of such code warrants examination, specifically provided the growing concerns around data personal privacy, security, the potential abuse of AI-driven applications, and cyber-espionage characteristics in between international powers.

Key Findings

Suspicious Data Handling & Exfiltration

- Hardcoded URLs direct information to external servers, raising issues about user activity tracking, such as to ByteDance "volce.com" endpoints. NowSecure recognizes these in the iPhone app yesterday as well. - Bespoke file encryption and data obfuscation approaches exist, with signs that they could be used to exfiltrate user details.

• The app contains hard-coded public keys, elearnportal.science rather than relying on the user device's chain of trust.
• UI interaction tracking captures detailed user habits without clear approval. - WebView adjustment exists, which could enable the app to gain access to private external internet browser data when links are opened. More details about WebView adjustments is here
  
  Device Fingerprinting & Tracking
  
  A substantial portion of the examined code appears to focus on gathering device-specific details, which can be utilized for tracking and fingerprinting.
  
  - The app gathers different special device identifiers, including UDID, Android ID, IMEI, IMSI, and carrier details.
• System properties, gratisafhalen.be set up bundles, and root detection systems suggest possible anti-tampering steps. E.g. probes for the presence of Magisk, a tool that privacy advocates and security researchers utilize to root their Android devices. and network profiling are present, suggesting potential tracking abilities and enabling or disabling of fingerprinting routines by area. - Hardcoded gadget model lists recommend the application might act differently depending on the discovered hardware. - Multiple vendor-specific services are utilized to draw out additional device details. E.g. if it can not figure out the device through basic Android SIM lookup (due to the fact that approval was not granted), it attempts maker particular extensions to access the exact same details.
  
  Potential Malware-Like Behavior
  
  While no definitive conclusions can be drawn without dynamic analysis, accc.rcec.sinica.edu.tw a number of observed behaviors align with recognized spyware and malware patterns:
  
  - The app uses reflection and UI overlays, which might assist in unauthorized screen capture or phishing attacks.
• SIM card details, identification numbers, and other device-specific data are aggregated for unknown purposes.
• The app executes country-based gain access to constraints and "risk-device" detection, recommending possible security mechanisms.
• The app implements calls to pack Dex modules, where additional code is packed from files with a.so extension at runtime.
• The.so submits themselves reverse and make extra calls to dlopen(), which can be utilized to pack additional.so files. This center is not usually inspected by Google Play Protect and bytes-the-dust.com other fixed analysis services.
• The.so files can be executed in native code, such as C++. The use of native code adds a layer of intricacy to the analysis process and obscures the complete degree of the app's capabilities. Moreover, native code can be leveraged to more easily intensify benefits, possibly making use of vulnerabilities within the operating system or device hardware.
  
  Remarks
  
  While information collection prevails in contemporary applications for debugging and enhancing user experience, aggressive fingerprinting raises considerable personal privacy concerns. The DeepSeek app requires users to visit with a legitimate email, which must already provide sufficient authentication. There is no legitimate factor for the app to aggressively collect and transfer distinct gadget identifiers, IMEI numbers, SIM card details, and other non-resettable system homes.
  
  The degree of tracking observed here exceeds common analytics practices, possibly enabling persistent user tracking and re-identification across gadgets. These behaviors, combined with obfuscation techniques and network interaction with third-party tracking services, necessitate a greater level of examination from security researchers and pipewiki.org users alike.
  
  The employment of runtime code packing along with the bundling of native code suggests that the app might enable the implementation and execution of unreviewed, from another location provided code. This is a severe prospective attack vector. No proof in this report is presented that from another location released code execution is being done, only that the center for this appears present.
  
  Additionally, the app's technique to discovering rooted devices appears extreme for an AI chatbot. Root detection is typically warranted in DRM-protected streaming services, where security and content defense are vital, or in competitive video games to avoid unfaithful. However, there is no clear reasoning for such stringent measures in an application of this nature, raising more questions about its intent.
  
  Users and organizations considering setting up DeepSeek must understand these possible dangers. If this application is being utilized within an enterprise or government environment, extra vetting and security controls need to be implemented before permitting its release on managed gadgets.
  
  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 conclusive conclusions.