Analysis on Password Protection in Android Applications

Android is currently the most popular smartphone operating system in use, with its success attributed to the large number of applications available from the Google Play Store. However, these contain issues relating to the storage of the user’s sensitive data, including contacts, location, and the phone’s unique identifier (IMEI). Use of these applications therefore risks exfiltration of this data, including unauthorized tracking of users’ behavior and violation of their privacy. Sensitive data leaks are currently detected with taint analysis approaches. This paper addresses these issues by proposing a new static taint analysis framework specifically for Android platforms, termed “B-Droid”. B-Droid is based on static taint analysis using a large set of sources and sinks techniques, side by side with the fuzz testing concept, in order to detect privacy leaks, whether malicious or unintentional by analyses the behavior of Applications Under Test (AUTs). This has the potential to offer improved precision in comparison to earlier approaches. To ensure the quality of our analysis, we undertook an evaluation testing a variety of Android applications installed on a mobile after filtering according to the relevant permissions. We found that B-Droid efficiently detected five of the most prevalent commercial spyware applications on the market, as well as issuing an immediate warning to the user, so that they can decide not to continue with the AUTs. This paper provides a detailed analysis of this method, along with its implementation and results.

Today’s Smartphone operating systems frequently fail to provide users with adequate control and visibility into how the third-party applications use their private data. With TaintDroid realized on Android system, we can detect user’s implicit taint and privacy leakage. But TaintDroid has some inherent defects. To better detect user’s implicit taint and privacy leakage in the Android platform, this paper analyzes implicit taint detection and then proposes an automated detection system based on dynamic taint tracking, called TaintChaser. Monitoring sensitive data with TaintChaser provides informed use of third-party applications for phone users and valuable input for smart-phone security service firms seeking to identify misbehaving applications. TaintChaser can detect behaviors of user’s data leakage in Android applications at a fine granularity level and the system can also analyze and test massive Android software in an automatic way. It uses TaintChaser to automatically analyze 38,268 popular Android applications and finds that 34.41 % of them may leak user’s privacy.

Logging is an essential software practice that is used by developers to debug, diagnose and audit software systems. Despite the advantages of logging, poor logging practices can potentially leak sensitive data. The problem of data leakage is more severe in applications that run on mobile devices, since these devices carry sensitive identification information ranging from physical device identifiers (e.g., IMEI MAC address) to communications network identifiers (e.g., SIM, IP, Bluetooth ID), and application-specific identifiers related to the location and the users' accounts. This preliminary study explores the impact of logging practices on data leakage of such sensitive information. Particularly, we want to investigate whether log-related statements inserted into an application code could lead to data leakage. While studying logging practices in mobile applications is an active research area, to our knowledge, this is the first study that explores the interplay between logging and security in the context of mobile applications for Android. We propose an approach called MobiLogLeak, an approach that identifies log statements in deployed apps that leak sensitive data. MobiLogLeak relies on taint flow analysis. Among 5,000 Android apps that we studied, we found that 200 apps leak sensitive data through logging.

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

As mobile applications become increasingly complex and privacy regulations continue to evolve, the task of accurately identifying app violations in compliance detection has become a major challenge. Prior works mainly relied on taint analysis and dynamic monitoring to address this issue. However, taint analysis requires specifying sources and sinks for each violation, leading to multiple analysis rounds and inefficiency. Meanwhile, dynamic monitoring suffers from incomplete coverage, resulting in high false negatives. In this paper, we propose a novel graph structure, called Behavior Property Graph (BPG), for detecting non-compliant behaviors in Android applications. BPG integrates the features from various graph representations, including Abstract Syntax Tree (AST), Control Flow Graph (CFG), Call Graph (CG), Program Dependency Graph (PDG), and Pointer Assignment Graph (PAG), enabling comprehensive modeling of complex app behaviors. Violations are identified by querying the BPG using behavioral patterns extracted from real-world apps. We develop a prototype system called BPGᴇɴ to generate BPGs and evaluated its performance by testing seven types of non-compliant behaviors on a dataset of 200 real-world apps. Notably, BPGᴇɴ detects 14 violations within 13 previously unreported non-compliant applications. The results show that BPGᴇɴ can efficiently and effectively detect app compliance violations.

Managing privacy leakage is of great importance in the Android platform. The variety of new user-privacy fraud reveals a new challenge in predicting potential privacy disclosure threats and protecting the privacy inside our pocket. In this paper, we present an analysis framework, called AppLeak, for information loss evaluation, privacy leakage detection, and privacy risk assessment on Android applications. With newly formalized privacy measures, AppLeak can effectively and efficiently support mobile user in identifying privacy risks of specific mobile applications.

An Improved Data Leakage Detection System is designed to mitigate the leakage of crucial and sensitive data in a cloud computing environment. Generally, leakage of data in computing system has caused a lot of irreparable damage or catastrophe to various institutions or organizations worldwide. Therefore, this research aims at detecting and preventing any intentional or non-intentional data leakages using dynamic password or key for data decryption security mechanisms. To achieve this the OOADM methodology was adopted. The new system was implemented using ASP.net MVC and Microsoft SQL Server Management Studio as the backend. And by incorporating an Audit trail/Transaction log mechanism, the new system monitors the activities within and outside the computing environment with date and time stamp. Hence, the system can be applied in any environment for the prevention and detection of any data leakage.

In this study, we present the uitHyDroid system, which allows the detection of sensitive data leakage via multi-applications using hybrid analysis. uitHyDroid uses static analysis to collect user interface elements that must interact to illuminate possible sensitive data flows. In addition, dynamic analysis is used to capture inter-application communications to link partial sensitive data flows from static analysis. This approach is faster than using only dynamic analysis. In this study, we use hooking technology to conduct dynamic analysis phase emulator modification. The experimental results show that the proposed system can detect most sensitive data leakages for both our dataset and real-world applications.

Currently, websites are widely used in various fields, including government. However, government websites are often the target of hacking. The XY Regency Communication and Information Agency experienced a website breach in 2023, which affected one of its subdomains, e-office.xy-regency.go.id, which stored employee data. This hack resulted in the leakage of sensitive data, user account takeovers, service disruptions, and a decline in public trust. The purpose of this study was to identify and test the vulnerabilities of the e-office.xy-regency.go.id website using the NIST SP 800-115 method, which includes the planning, discovery, attack, and reporting phases. Vulnerabilities were classified using the Common Vulnerability Scoring System (CVSS) and the OWASP Top 10 standard. This study identified two high-level vulnerabilities, namely Cross-Site Scripting (XSS) with a CVSS score of 8.2, classified as High severity, and Cross-Site Request Forgery (CSRF) with a CVSS score of 9.3, classified as Critical severity. These vulnerabilities could allow hackers to execute malicious scripts and manipulate users without their knowledge. Recommendations include implementing input validation on text boxes by limiting characters to letters or combinations of letters and numbers, and ensuring that all forms and endpoints that handle sensitive data are protected with unique and unpredictable CSRF tokens. Future research should focus on analyzing domain vulnerabilities, identifying the origin and potential of attacks, and developing effective protection and recovery strategies.

S. Saisree and V. Balaji With the growth of internet technologies data handling and sharing is inevitable and this is achieved by cloud computing. When a data outsourcer distributes sensitive data to the set of their subscribed and authentic agents, it is always possible that one of the authentic agents may leak the data to an unauthentic third party, this causes the sensitive data leak.

Mobile operating systems like Android failed to provide sufficient protection on personal data, and privacy leakage becomes a major concern. To understand the security risks and privacy leakage, analysts have to carry out data-flow analysis. In 2014, Android upgraded with a fundamentally new design known as Android RunTime (ART) environment in Android 5.0. ART adopts ahead-of-time compilation strategy and replaces previous virtual-machine-based Dalvik. Unfortunately, many data-flow analysis systems like TaintDroid were designed for the legacy Dalvik environment. This makes data-flow analysis of new apps and malware infeasible. We design a multi-level information-flow tracking system for the new Android system called TaintART. TaintART employs a multi-level taint analysis technique to minimize the taint tag storage. Therefore, taint tags can be stored in processor registers to provide efficient taint propagation operations. We also customize the ART compiler to maximize performance gains of the ahead-of-time compilation optimizations. Based on the general design of TaintART, we also implement a multi-level privacy enforcement to prevent sensitive data leakage. We demonstrate that TaintART only incurs less than 15% overheads on a CPU-bound microbenchmark and negligible overhead on built-in or third-party applications. Compared to legacy Dalvik environment in Android 4.4, TaintART achieves about 99.7% faster performance for Java runtime benchmark.

Cross-program propagation of tainted data (such as sensitive information or user input) in an interactive IoT system is listed among the OWASP IoT top 10 most critical security risks. When programs run on distinct devices, as it occurs in IoT systems, they communicate through different channels in order to implement some functionality. Hence, in order to prove the overall system secure, an analysis must consider how these components interact. Standard taint analyses detect if a value coming from a source (such as methods that retrieve user input or sensitive data) flows into a sink (typically, methods that execute SQL queries or send data into the Internet), unsanitized (that is, not properly escaped). This work devises a cross-program taint analysis that leverages an existing intra-program taint analysis to detect security vulnerabilities in multiple communicating programs. The proposed framework has been implemented above the intra-program taint analysis of the Julia static analyzer. Preliminary experimental results on multi-program IoT systems, publicly available on GitHub, show that the technique is effective and detects inter-program flows of tainted data that could not be discovered by analyzing each program in isolation.

Embedded system security is often compromised when "trusted" software is subverted to result in unintended behavior, such as leakage of sensitive data or execution of malicious code. Several countermeasures have been proposed in the literature to counteract these intrusions. A common underlying theme in most of them is to define security policies at the system level in an application-independent manner and check for security violations either statically or at run time. In this paper, we present a methodology that addresses this issue from a different perspective. It defines correct execution as synonymous with the way the program was intended to run and employs a dedicated hardware monitor to detect and prevent unintended program behavior. Specifically, we extract properties of an embedded program through static program analysis and use them as the bases for enforcing permissible program behavior at run time. The processor architecture is augmented with a hardware monitor that observes the program's dynamic execution trace, checks whether it falls within the allowed program behavior, and flags any deviations from expected behavior to trigger appropriate response mechanisms. We present properties that capture permissible program behavior at different levels of granularity, namely inter-procedural control flow, intra-procedural control flow, and instruction-stream integrity. We outline a systematic methodology to design application-specific hardware monitors for any given embedded program. Hardware implementations using a commercial design flow, and cycle-accurate performance simulations indicate that the proposed technique can thwart several common software and physical attacks, facilitating secure program execution with minimal overheads

Based on the analysis of the process of data masking, the formal definition of data masking was presented and a generic data masking model was proposed from a theoretical point of view. The key problems and algorithms of the model were described and analyzed. The generic model can ensure similarity and availability of data without the leakage of sensitive data. So it has a strong practical value.

Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP).This extension could introduce new security vulnerabilities.We present a systematic investigation of LLM vulnerabilities to hidden adversarial prompts through malicious font injection in external resources like webpages, where attackers manipulate code-to-glyph mapping to inject deceptive content which are invisible to users.We evaluate two critical attack scenarios:(1) "malicious content relay" and ( 2) "sensitive data leakage" through MCP-enabled tools.Our experiments reveal that indirect prompts with injected malicious font can bypass LLM safety mechanisms through external resources, achieving varying success rates based on data sensitivity and prompt design.Our research underscores the urgent need for enhanced security measures in LLM deployments when processing external content.