erikschuessler

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

I have actually composed about DeepSeek formerly here.

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

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

The findings detailed in this report are based simply on fixed analysis. This implies 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 examination, specifically offered the growing concerns around data privacy, monitoring, the potential misuse of AI-driven applications, and cyber-espionage characteristics between global powers.

Key Findings

Suspicious Data Handling & Exfiltration

- Hardcoded URLs direct data to external servers, raising concerns about user activity tracking, such as to ByteDance "volce.com" endpoints. NowSecure recognizes these in the iPhone app yesterday also.

Bespoke file encryption and data obfuscation techniques 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 gadget's chain of trust.
UI interaction tracking catches detailed user habits without clear authorization.
WebView manipulation exists, which might permit the app to gain access to personal external web browser information when links are opened. More details about WebView manipulations is here

Device Fingerprinting & Tracking

A significant part of the analyzed code appears to focus on event device-specific details, which can be utilized for tracking and fingerprinting.

- The app gathers numerous special device identifiers, including UDID, Android ID, IMEI, IMSI, and carrier details.
System residential or commercial properties, set up packages, ura.cc and root detection systems recommend possible anti-tampering procedures. E.g. probes for the existence of Magisk, a tool that personal privacy and security researchers use to root their Android gadgets.
Geolocation and network profiling exist, showing prospective tracking capabilities and allowing or disabling of fingerprinting routines by area.
Hardcoded device design lists suggest the application may act differently depending on the spotted hardware.
Multiple vendor-specific services are used to extract additional device details. E.g. if it can not determine the gadget through standard Android SIM lookup (because authorization was not granted), it tries maker specific extensions to access the same details.

Potential Malware-Like Behavior

While no definitive conclusions can be drawn without dynamic analysis, numerous observed habits align with known spyware and malware patterns:

- The app uses reflection and UI overlays, which might help with unapproved screen capture or phishing attacks.
SIM card details, identification numbers, and other device-specific information are aggregated for unknown purposes.
The app carries out country-based gain access to constraints and "risk-device" detection, suggesting possible surveillance systems.
The app implements calls to pack Dex modules, where extra code is packed from files with a.so extension at runtime.
The.so files themselves reverse and make additional calls to dlopen(), which can be used to load additional.so files. This center is not normally inspected by Google Play Protect and other static analysis services.
The.so files can be carried out in native code, such as C++. Using native code includes a layer of complexity to the analysis process and obscures the complete extent of the app's capabilities. Moreover, native code can be leveraged to more easily intensify benefits, possibly making use of vulnerabilities within the os or gadget hardware.

Remarks

While data collection prevails in contemporary applications for debugging and enhancing user experience, aggressive fingerprinting raises considerable personal privacy issues. The DeepSeek app needs users to visit with a legitimate email, which must currently provide enough authentication. There is no legitimate factor for the app to aggressively gather and transmit special device identifiers, IMEI numbers, SIM card details, and asystechnik.com other non-resettable system residential or commercial properties.

The extent of tracking observed here exceeds typical analytics practices, potentially enabling relentless user tracking and re-identification across devices. These behaviors, combined with obfuscation techniques and network communication with third-party tracking services, warrant a higher level of analysis from security scientists and users alike.

The work of runtime code filling as well as the bundling of native code recommends that the app could allow the deployment and execution of unreviewed, from another location delivered code. This is a severe possible attack vector. No evidence in this report is provided that from another location released code execution is being done, only that the facility for this appears present.

Additionally, the app's approach to finding rooted gadgets appears extreme for an AI chatbot. Root detection is typically justified in DRM-protected streaming services, where security and content security are crucial, or in competitive video games to prevent unfaithful. However, there is no clear reasoning for such stringent steps in an application of this nature, raising more questions about its intent.

Users and companies thinking about setting up DeepSeek must be mindful of these prospective risks. If this application is being used within a business or federal government environment, additional vetting and security controls should be imposed before allowing its deployment on managed devices.

Disclaimer: The analysis provided in this report is based upon static code review and does not suggest that all identified functions are actively used. Further examination is required for conclusive conclusions.