Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

LM-Scout: Analyzing the Security of Language Model Integration in Android Apps

Muhammad Ibrahim, Gűliz Seray Tuncay, Z. Berkay Celik, Aravind Machiry, Antonio Bianchi

TL;DR

This paper analyzes the security of LM usage in Android apps, presenting LM-Scout as a two-phase methodology combining manual reconnaissance and automated analysis to uncover insecure LM restrictions. On a dataset of 181 LM-using apps, 127 allowed bypasses ($70\\%$), prompting a large-scale scan of 2,950 apps with LM-Scout, which discovered 120 vulnerable apps and produced 65 static and 61 dynamic exploits, including API key leaks and token-based authorization weaknesses. The findings underscore the lack of server-side enforcement and Android-specific LM integration frameworks, and offer recommendations for secure LM integration, including server-side restrictions, dedicated LM SDKs, and anti-tampering measures. The work contributes to the security literature by providing a large-scale Android LM security assessment and a practical automated tool for vulnerability discovery.

Abstract

Developers are increasingly integrating Language Models (LMs) into their mobile apps to provide features such as chat-based assistants. To prevent LM misuse, they impose various restrictions, including limits on the number of queries, input length, and allowed topics. However, if the LM integration is insecure, attackers can bypass these restrictions and gain unrestricted access to the LM, potentially harming developers' reputations and leading to significant financial losses. This paper presents the first systematic study of insecure usage of LMs by Android apps. We first manually analyze a preliminary dataset of apps to investigate LM integration methods, construct a taxonomy that categorizes the LM usage restrictions implemented by the apps, and determine how to bypass them. Alarmingly, we can bypass restrictions in 127 out of 181 apps. Then, we develop LM-Scout, a fully automated tool to detect on a large-scale vulnerable usage of LMs in 2,950 mobile apps. LM-Scout shows that, in many cases (i.e., 120 apps), it is possible to find and exploit such security issues automatically. Finally, we identify the root causes for the identified issues and offer recommendations for secure LM integration.

LM-Scout: Analyzing the Security of Language Model Integration in Android Apps

TL;DR

This paper analyzes the security of LM usage in Android apps, presenting LM-Scout as a two-phase methodology combining manual reconnaissance and automated analysis to uncover insecure LM restrictions. On a dataset of 181 LM-using apps, 127 allowed bypasses (), prompting a large-scale scan of 2,950 apps with LM-Scout, which discovered 120 vulnerable apps and produced 65 static and 61 dynamic exploits, including API key leaks and token-based authorization weaknesses. The findings underscore the lack of server-side enforcement and Android-specific LM integration frameworks, and offer recommendations for secure LM integration, including server-side restrictions, dedicated LM SDKs, and anti-tampering measures. The work contributes to the security literature by providing a large-scale Android LM security assessment and a practical automated tool for vulnerability discovery.

Abstract

Developers are increasingly integrating Language Models (LMs) into their mobile apps to provide features such as chat-based assistants. To prevent LM misuse, they impose various restrictions, including limits on the number of queries, input length, and allowed topics. However, if the LM integration is insecure, attackers can bypass these restrictions and gain unrestricted access to the LM, potentially harming developers' reputations and leading to significant financial losses. This paper presents the first systematic study of insecure usage of LMs by Android apps. We first manually analyze a preliminary dataset of apps to investigate LM integration methods, construct a taxonomy that categorizes the LM usage restrictions implemented by the apps, and determine how to bypass them. Alarmingly, we can bypass restrictions in 127 out of 181 apps. Then, we develop LM-Scout, a fully automated tool to detect on a large-scale vulnerable usage of LMs in 2,950 mobile apps. LM-Scout shows that, in many cases (i.e., 120 apps), it is possible to find and exploit such security issues automatically. Finally, we identify the root causes for the identified issues and offer recommendations for secure LM integration.
Paper Structure (23 sections, 12 figures, 2 tables)

This paper contains 23 sections, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Language Models
  4. Language Models in Mobile Apps
  5. Motivation
  6. Overview
  7. Reconnaissance
  8. Taxonomy of LM Restrictions
  9. App Analysis in Reconnaissance
  10. Reconnaissance Results
  11. Reconnaissance Insights
  12. Automated Analysis: LM-Scout
  13. Static Analysis
  14. Dynamic Analysis
  15. Exploit Verification
  16. ...and 8 more sections

Figures (12)

  • Figure 1: Language Model app integration frameworks. Solid lines represent app developer's infrastructure. Dashed lines represent third-party services. (a) An app directly communicating with a LM-Server provided by third-party. (b) An app utilizing LM-Server hosted by the developer by communicating through the App-Server. (c) An app utilizing LM-Server hosted by the developer by communicating through the App-Server. (d) Third-party LM-Server only handles natural language queries and sends corresponding parameters to the API-Server, which performs the API call and sends API response to the App-Server. (e) OpenAI Plugin framework, in which the third-party LM-Server handles both natural language queries and API calls.
  • Figure 2: App analysis and LM restriction taxonomy (R-App and R-LM) formulation in the Reconnaissance phase
  • Figure 3: Overview of LM-Scout
  • Figure 4: LM-Scout App Interaction. 1) Box 2 is selected to continue. 2) Partially loaded advertisement bypassed by tapping Box 2. 3) Box 16 selected to access the LM interface. 4) Box 9 tapped to pull the keyboard, input the LM-Query and tap Box 12 to execute. 5) LM-Query response received.
  • Figure 5: ChatAIApp exhibiting Quota-R-App by limiting the input length to the LM.
  • ...and 7 more figures