I conducted a fixed analysis of DeepSeek, a Chinese LLM chatbot, utilizing variation 1.8.0 from the Google Play Store. The objective was to determine potential security and privacy problems.

I’ve blogged about DeepSeek formerly here.

Additional security and privacy concerns about DeepSeek have been raised.

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

The findings detailed in this report are based purely on static analysis. This means that while the code exists within the app, there is no conclusive evidence that all of it is executed in practice. Nonetheless, the existence of such code warrants analysis, specifically provided the growing issues around information personal privacy, monitoring, the prospective misuse of AI-driven applications, and cyber-espionage dynamics in between worldwide powers.

Key Findings

Suspicious Data Handling & Exfiltration

- Hardcoded URLs direct information to external servers, raising issues about user activity monitoring, such as to ByteDance “volce.com” endpoints. NowSecure determines these in the iPhone app yesterday too.

• Bespoke encryption and data obfuscation approaches exist, with indications that they might be utilized to exfiltrate user details.
• The app contains hard-coded public keys, instead of relying on the user device’s chain of trust.
• UI interaction tracking records detailed user behavior without clear permission.
• WebView adjustment exists, which could permit the app to gain access to personal external browser information when links are opened. More details about WebView adjustments is here
  
  Device Fingerprinting & Tracking
  
  A significant part of the examined code appears to focus on event device-specific details, which can be utilized for tracking and fingerprinting.
  
  - The app collects different special gadget identifiers, consisting of UDID, Android ID, IMEI, IMSI, and carrier details.
• System properties, installed packages, and root detection systems suggest potential anti-tampering measures. E.g. probes for the presence of Magisk, a tool that personal privacy advocates and security scientists utilize to root their Android devices. - Geolocation and network profiling are present, showing prospective tracking abilities and making it possible for or disabling of fingerprinting routines by region.
• Hardcoded device model lists recommend the application may behave in a different way depending on the found hardware.
• Multiple vendor-specific services are used to extract extra device details. E.g. if it can not figure out the gadget through standard Android SIM lookup (since permission was not approved), it tries producer particular extensions to access the same details.
  
  Potential Malware-Like Behavior
  
  While no definitive conclusions can be drawn without dynamic analysis, a number of observed habits align with recognized spyware and malware patterns:
  
  - The app uses reflection and UI overlays, which might 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 surveillance mechanisms.
• The app executes calls to fill Dex modules, where additional code is loaded from files with a.so extension at runtime.
• The.so files themselves reverse and make additional calls to dlopen(), which can be used to fill additional.so files. This center is not generally checked by Google Play Protect and other static analysis services.
• The.so files can be carried out in native code, such as C++. The use of native code adds a layer of intricacy to the analysis procedure and obscures the full degree of the app’s abilities. Moreover, native code can be leveraged to more easily escalate benefits, possibly making use of vulnerabilities within the os or device hardware.
  
  Remarks
  
  While data collection prevails in modern applications for debugging and enhancing user experience, aggressive fingerprinting raises considerable privacy concerns. The DeepSeek app needs users to log in with a valid email, which ought to currently provide adequate . There is no legitimate factor for the app to strongly gather and transfer distinct device identifiers, IMEI numbers, SIM card details, and other non-resettable system homes.
  
  The degree of tracking observed here surpasses normal analytics practices, possibly allowing relentless user tracking and re-identification throughout devices. These behaviors, integrated with obfuscation methods and network interaction with third-party tracking services, necessitate 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 recommends that the app might enable the deployment and execution of unreviewed, remotely provided code. This is a serious prospective attack vector. No evidence in this report exists that remotely 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 frequently justified in DRM-protected streaming services, where security and material security are important, or in competitive computer game 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 companies thinking about setting up DeepSeek must know these prospective risks. If this application is being used within an enterprise or government environment, extra vetting and security controls ought to be imposed before enabling its release on handled gadgets.
  
  Disclaimer: orcz.com The analysis provided in this report is based upon static code evaluation and does not indicate that all spotted functions are actively used. Further investigation is required for conclusive conclusions.