arXiv subjects

Mariano Ceccato

Publications and source records attributed to Mariano Ceccato.

At least 19 recordsLinked to original sources

arXiv2026

TeleGapper: On the (un)reliability of Privacy Policies in Telegram Mini apps

Telegram Mini Apps are Web applications embedded within the Telegram client, forming an ecosystem of third-party services within one of the world's most widely used messaging platforms. Despite their growing adoption and access to Telegram-provided context, their privacy properties remain largely unexplored. Unlike ecosystems such as WeChat, which rely on tightly controlled, proprietary execution frameworks, Telegram adopts a different model: Mini Apps run inside a WebView, combining platform-provided context with standard Web capabilities and unrestricted outbound networking. This enables applications to transmit sensitive information to analytics, advertising, tracking, or other third parties through ordinary Web requests, often with limited visibility. Privacy disclosures are therefore critical for transparency. Telegram allows Mini Apps either to define an application-specific privacy policy or to rely on a platform-provided default policy. While the latter reduces the developer's disclosure burden, it may lead to generic statements that do not accurately capture actual data practices of individual Mini Apps. In this paper, we present TeleGapper, a black-box dynamic analysis framework to assess the privacy posture of Mini Apps by capturing runtime network traffic, identifying third-party communications, and comparing observed data flows against disclosed privacy information. We evaluate 278 working Mini Apps collected from tApps Center, a community-driven catalogue for discovering third-party applications in Telegram. We find that 59.4% contact at least one undisclosed third party, 78.8% rely exclusively on Telegram's default privacy policy, and none provides a consent or opt-out mechanism. These findings expose a substantial transparency and compliance gap in a widely used yet understudied ecosystem.

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arXiv2026

Evaluating LLMs for Obfuscation Detection and Classification in Android Apps

Android applications (apps) developers increasingly rely on code obfuscation techniques to hinder reverse engineering and protect intellectual property. However, obfuscation also reduces the effectiveness of static analysis and vulnerability detection tools, creating challenges for Android security analysis. Existing approaches for detecting obfuscation in Android apps predominantly rely on handcrafted heuristics, engineered features, or task-specific learning pipelines, which may struggle to generalize across evolving obfuscation strategies. This paper presents a large-scale empirical study investigating the capability of Large Language Models (LLMs) to detect obfuscation in Android apps through semantic reasoning. Our study evaluates whether off-the-shelf LLMs can identify obfuscated code without relying on handcrafted rules, predefined signatures, or dedicated model training. The empirical evaluation is conducted on both a controlled benchmark containing an app obfuscated with multiple techniques and a real-world dataset of Android apps collected from Google Play. The study further examines the impact of prompt design, model selection, and decision thresholds across several open-weight and proprietary LLMs. Finally, the analysis compares LLM-based reasoning with existing SAST-based obfuscation-detection approaches and discusses the broader implications and limitations of applying LLMs to Android security analysis.

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arXiv2026

Beyond the Tip of the Iceberg: Understanding SATD in Dockerfiles through the Lens of Co-evolution

Dockerfiles enable the creation of portable container-based execution environments for the application code, and have become an important part of the modern software development process. As Dockerfiles are a form of Infrastructure-as-Code (IaC), they can include temporary workarounds and other suboptimal implementations, leading to the accrual of technical debt that affects their reliability, security, and maintainability in the future. Prior work characterized self-admitted technical debt (SATD) in Dockerfile comments and the surrounding file chunks. This single-file view is incomplete since source code evolution involves changes across different types of software artifacts such as production, test, build, and other configuration files. Thus, we address this gap by studying SATD events in Dockerfiles alongside the related source code. We find that approximately 27% of admission events and 40% of repayment events are coupled to non-Dockerfile artifacts, and coupling sources are subtype-specific. We also observed that coupled SATD in general are repaid significantly faster overall (p = 0.0201), while coupled SATD regarding missing functionalities persists longer than its isolated counterparts; Lastly, we conducted open and axial coding of coupled SATD events, and we observe that external dependency issues, more particularly regarding unreleased upstream packages and bug fixes, are the most common cause of admission triggers in the source code; we also observe that architectural refactoring is the most common prerequisite for the repayment of SATD in Dockerfiles. These findings indicate that both practitioners (e.g. developers and project managers) and SATD researchers should integrate the source code-side co-evolution, rather than the single-file view, as the primary unit of analysis.

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arXiv2026

PolicyGapper: Automated Detection of Inconsistencies Between Google Play Data Safety Sections and Privacy Policies Using LLMs

Mobile application developers are required to disclose how they collect, use, and share user data in compliance with privacy regulations. To support transparency, major app marketplaces have introduced standardized disclosure mechanisms. In 2022, Google mandated the Data Safety Section (DSS) on Google Play, requiring developers to summarize their data practices. However, compiling accurate DSS disclosures is challenging, as they must remain consistent with the corresponding privacy policy (PP), and no automated tool currently verifies this alignment. Prior studies indicate that nearly 80% of popular apps contain incomplete or misleading DSS declarations. We present PolicyGapper, an LLM-based methodology for automatically detecting discrepancies between DSS disclosures and privacy policies. PolicyGapper operates in four stages: scraping, pre-processing, analysis, and post-processing, without requiring access to application binaries. We evaluate PolicyGapper on a dataset of 330 top-ranked apps spanning all 33 Google Play categories, collected in Q3 2025. The approach identifies 2,689 omitted disclosures, including 2,040 related to data collection and 649 to data sharing. Manual validation on a stratified 10% subset, repeated across three independent runs, yields an average Precision of 0.75, Recall of 0.77, Accuracy of 0.69, and F1-score of 0.76. To support reproducibility, we release a complete replication package, including the dataset, prompts, source code, and results available at https://github.com/Mobile-IoT-Security-Lab/PolicyGapper and https://doi.org/10.5281/zenodo.19628493.

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arXiv2026

CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation

Automated GUI testing is crucial for ensuring the quality and reliability of Android apps. However, the efficacy of existing UI testing techniques is often limited, especially in terms of coverage. Recent studies, including the state-of-the-art, struggle to achieve more than 30% activity coverage in real-world apps. This limited coverage can be attributed to a combination of factors such as failing to generate complex user inputs, unsatisfied activation conditions regarding device configurations and external resources, and hard-to-reach code paths that are not easily accessible through the GUI. To overcome these limitations, we propose CovAgent, a novel agentic AI-powered approach to enhance Android app UI testing. Our fuzzer-agnostic framework comprises an AI agent that inspects the app's decompiled Smali code and component transition graph, and reasons about unsatisfied activation conditions within the app code logic that prevent access to the activities that are unreachable by standard and widely adopted GUI fuzzers. Then, another agent generates dynamic instrumentation scripts that satisfy activation conditions required for successful transitions to those activities. We found that augmenting existing fuzzing approaches with our framework achieves a significant improvement in test coverage over the state-of-the-art, LLMDroid, and other baselines such as Fastbot and APE (e.g., 101.1%, 116.3% and 179.7% higher activity coverage, respectively). CovAgent also outperforms all the baselines in other metrics such as class, method, and line coverage. We also conduct investigations into components within CovAgent to reveal further insights regarding the efficacy of Agentic AI in the field of automated app testing such as the agentic activation condition inference accuracy, and agentic activity-launching success rate.

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arXiv2025

Automated Testing of Broken Authentication Vulnerabilities in Web APIs with AuthREST

We present AuthREST, an open-source security testing tool targeting broken authentication, one of the most prevalent API security risks in the wild. AuthREST automatically tests web APIs for credential stuffing, password brute forcing, and unchecked token authenticity. Empirical results show that AuthREST is effective in improving web API security. Notably, it uncovered previously unknown authentication vulnerabilitiesin in four public APIs.

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arXiv2025

VLM-Fuzz: Vision Language Model Assisted Recursive Depth-first Search Exploration for Effective UI Testing of Android Apps

Testing Android apps effectively requires a systematic exploration of the app's possible states by simulating user interactions and system events. While existing approaches have proposed several fuzzing techniques to generate various text inputs and trigger user and system events for UI state exploration, achieving high code coverage remains a significant challenge in Android app testing. The main challenges are (1) reasoning about the complex and dynamic layout of UI screens; (2) generating required inputs/events to deal with certain widgets like pop-ups; and (3) coordination between current test inputs and previous inputs to avoid getting stuck in the same UI screen without improving test coverage. To address these problems, we propose a novel, automated fuzzing approach called VLM-Fuzz for effective UI testing of Android apps. We present a novel heuristic-based depth-first search (DFS) exploration algorithm, assisted with a vision language model (VLM), to effectively explore the UI states of the app. We use static analysis to analyze the Android Manifest file and the runtime UI hierarchy XML to extract the list of components, intent-filters and interactive UI widgets. VLM is used to reason about complex UI layout and widgets on an on-demand basis. Based on the inputs from static analysis, VLM, and the current UI state, we use some heuristics to deal with the above-mentioned challenges. We evaluated VLM-Fuzz based on a benchmark containing 59 apps obtained from a recent work and compared it against two state-of-the-art approaches: APE and DeepGUI. VLM-Fuzz outperforms the best baseline by 9.0%, 3.7%, and 2.1% in terms of class coverage, method coverage, and line coverage, respectively. We also ran VLM-Fuzz on 80 recent Google Play apps (i.e., updated in 2024). VLM-Fuzz detected 208 unique crashes in 24 apps, which have been reported to respective developers.

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arXiv2025

How Do Solidity Versions Affect Vulnerability Detection Tools? An Empirical Study

Context: Smart contract vulnerabilities pose significant security risks for the Ethereum ecosystem, driving the development of automated tools for detection and mitigation. Smart contracts are written in Solidity, a programming language that is rapidly evolving to add features and improvements to enhance smart contract security. New versions of Solidity change the compilation process, potentially affecting how tools interpret and analyze smart contract code. Objective: In such a continuously evolving landscape, we aim to investigate the compatibility of detection tools with Solidity versions. More specifically, we present a plan to study detection tools by empirically assessing (i) their compatibility with the Solidity pragma directives, (ii) their detection effectiveness, and (iii) their execution time across different versions of Solidity. Method: We will conduct an exploratory study by running several tools and collecting a large number of real-world smart contracts to create a balanced dataset. We will track and analyze the tool execution through SmartBugs, a framework that facilitates the tool execution and allows the integration of new tools.

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arXiv2024

DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning

Automatically crafting test scenarios for REST APIs helps deliver more reliable and trustworthy web-oriented systems. However, current black-box testing approaches rely heavily on the information available in the API's formal documentation, i.e., the OpenAPI Specification (OAS for short). While useful, the OAS mostly covers syntactic aspects of the API (e.g., producer-consumer relations between operations, input value properties, and additional constraints in natural language), and it lacks a deeper understanding of the API business logic. Missing semantics include implicit ordering (logic dependency) between operations and implicit input-value constraints. These limitations hinder the ability of black-box testing tools to generate truly effective test cases automatically. This paper introduces DeepREST, a novel black-box approach for automatically testing REST APIs. It leverages deep reinforcement learning to uncover implicit API constraints, that is, constraints hidden from API documentation. Curiosity-driven learning guides an agent in the exploration of the API and learns an effective order to test its operations. This helps identify which operations to test first to take the API in a testable state and avoid failing API interactions later. At the same time, experience gained on successful API interactions is leveraged to drive accurate input data generation (i.e., what parameters to use and how to pick their values). Additionally, DeepREST alternates exploration with exploitation by mutating successful API interactions to improve test coverage and collect further experience. Our empirical validation suggests that the proposed approach is very effective in achieving high test coverage and fault detection and superior to a state-of-the-art baseline.

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arXiv2023

Security layers and related services within the Horizon Europe NEUROPULS project

In the contemporary security landscape, the incorporation of photonics has emerged as a transformative force, unlocking a spectrum of possibilities to enhance the resilience and effectiveness of security primitives. This integration represents more than a mere technological augmentation; it signifies a paradigm shift towards innovative approaches capable of delivering security primitives with key properties for low-power systems. This not only augments the robustness of security frameworks, but also paves the way for novel strategies that adapt to the evolving challenges of the digital age. This paper discusses the security layers and related services that will be developed, modeled, and evaluated within the Horizon Europe NEUROPULS project. These layers will exploit novel implementations for security primitives based on physical unclonable functions (PUFs) using integrated photonics technology. Their objective is to provide a series of services to support the secure operation of a neuromorphic photonic accelerator for edge computing applications.

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arXiv2023

NEUROPULS: NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS

This special session paper introduces the Horizon Europe NEUROPULS project, which targets the development of secure and energy-efficient RISC-V interfaced neuromorphic accelerators using augmented silicon photonics technology. Our approach aims to develop an augmented silicon photonics platform, an FPGA-powered RISC-V-connected computing platform, and a complete simulation platform to demonstrate the neuromorphic accelerator capabilities. In particular, their main advantages and limitations will be addressed concerning the underpinning technology for each platform. Then, we will discuss three targeted use cases for edge-computing applications: Global National Satellite System (GNSS) anti-jamming, autonomous driving, and anomaly detection in edge devices. Finally, we will address the reliability and security aspects of the stand-alone accelerator implementation and the project use cases.

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arXiv2023

Automated Black-box Testing of Mass Assignment Vulnerabilities in RESTful APIs

Mass assignment is one of the most prominent vulnerabilities in RESTful APIs. This vulnerability originates from a misconfiguration in common web frameworks, such that naming convention and automatic binding can be exploited by an attacker to craft malicious requests writing confidential resources and (massively) overriding data, that should be read-only and/or confidential. In this paper, we adopt a black-box testing perspective to automatically detect mass assignment vulnerabilities in RESTful APIs. Execution scenarios are generated purely based on the OpenAPI specification, that lists the available operations and their message format. Clustering is used to group similar operations and reveal read-only fields, the latter are candidate for mass assignment. Then, interaction sequences are automatically generated by instantiating abstract testing templates, trying to exploit the potential vulnerabilities. Finally, test cases are run, and their execution is assessed by a specific oracle, in order to reveal whether the vulnerability could be successfully exploited. The proposed novel approach has been implemented and evaluated on a set of case studies written in different programming languages. The evaluation highlights that the approach is quite effective in detecting seeded vulnerabilities, with a remarkably high accuracy.

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arXiv2021

Empirical Comparison of Black-box Test Case Generation Tools for RESTful APIs

In literature, we can find research tools to automatically generate test cases for RESTful APIs, addressing the specificity of this particular programming domain. However, no direct comparison of these tools is available to guide developers in deciding which tool best fits their REST API project. In this paper, we present the results of an empirical comparison of automated black-box test case generation approaches for REST APIs. We surveyed the available black-box testing tools that have been proposed in recent literature, finding four usable prototypes: RestTestGen, RESTler, bBOXRT and RESTest. We used these tools to generate test cases for 14 real-world REST services. Then, testing results have been analyzed and compared in terms of robustness (i.e., success rate) and test coverage. Among the considered tools, RESTler appears to be the most solid, able to successfully test all case studies (the other tools experienced crashes). Conversely, test cases generated by RestTestGen scored the highest coverage, suggesting that its testing strategy is the most effective in testing REST APIs.

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arXiv2021

Restats: A Test Coverage Tool for RESTful APIs

Test coverage is a standard measure used to evaluate the completeness of a test suite. Coverage is typically computed on source code, by assessing the extent of source code entities (e.g., statements, data dependencies, control dependencies) that are exercised when running test cases. When considering REST APIs, an alternative perspective to assess test suite completeness is with respect to the service definition. This paper presents Restats, a test coverage tool for REST APIs that supports eight state-of-the-art test coverage metrics with a black-box perspective, i.e., only relying on the OpenAPI interface specification of the REST API under test. In fact, metrics are computed by only observing the HTTP requests and responses occurring at testing time, and no access to source/compiled code of the REST API is required. These coverage metrics come in handy for: (i) developers and test engineers working at development and maintenance tasks; (ii) stakeholders and customers who want to evaluate the completeness of acceptance tests; (iii) researches interested in comparing different automated test case generation strategies.

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arXiv2021

EtherSolve: Computing an Accurate Control-Flow Graph from Ethereum Bytecode

Motivated by the immutable nature of Ethereum smart contracts and of their transactions, quite many approaches have been proposed to detect defects and security problems before smart contracts become persistent in the blockchain and they are granted control on substantial financial value. Because smart contracts source code might not be available, static analysis approaches mostly face the challenge of analysing compiled Ethereum bytecode, that is available directly from the official blockchain. However, due to the intrinsic complexity of Ethereum bytecode (especially in jump resolution), static analysis encounters significant obstacles that reduce the accuracy of exiting automated tools. This paper presents a novel static analysis algorithm based on the symbolic execution of the Ethereum operand stack that allows us to resolve jumps in Ethereum bytecode and to construct an accurate control-flow graph (CFG) of the compiled smart contracts. EtherSolve is a prototype implementation of our approach. Experimental results on a significant set of real world Ethereum smart contracts show that EtherSolve improves the accuracy of the execrated CFGs with respect to the state of the art available approaches. Many static analysis techniques are based on the CFG representation of the code and would therefore benefit from the accurate extraction of the CFG. For example, we implemented a simple extension of EtherSolve that allows to detect instances of the re-entrancy vulnerability.

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arXiv2021

Deep Reinforcement Learning for Black-Box Testing of Android Apps

The state space of Android apps is huge and its thorough exploration during testing remains a major challenge. In fact, the best exploration strategy is highly dependent on the features of the app under test. Reinforcement Learning (RL) is a machine learning technique that learns the optimal strategy to solve a task by trial and error, guided by positive or negative reward, rather than by explicit supervision. Deep RL is a recent extension of RL that takes advantage of the learning capabilities of neural networks. Such capabilities make Deep RL suitable for complex exploration spaces such as the one of Android apps. However, state of the art, publicly available tools only support basic, tabular RL. We have developed ARES, a Deep RL approach for black-box testing of Android apps. Experimental results show that it achieves higher coverage and fault revelation than the baselines, which include state of the art RL based tools, such as TimeMachine and Q-Testing. We also investigated qualitatively the reasons behind such performance and we have identified the key features of Android apps that make Deep RL particularly effective on them to be the presence of chained and blocking activities.

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arXiv2020

A Framework for In-Vivo Testing of Mobile Applications

The ecosystem in which mobile applications run is highly heterogeneous and configurable. All layers upon which mobile apps are built offer wide possibilities of variations, from the device and the hardware, to the operating system and middleware, up to the user preferences and settings. Testing all possible configurations exhaustively, before releasing the app, is unaffordable. As a consequence, the app may exhibit different, including faulty, behaviours when executed in the field, under specific configurations. In this paper, we describe a framework that can be instantiated to support in-vivo testing of a mobile app. The framework monitors the configuration in the field and triggers in-vivo testing when an untested configuration is recognized. Experimental results show that the overhead introduced by monitoring is unnoticeable to negligible (i.e., 0-6%) depending on the device being used (high- vs. low-end). In-vivo test execution required on average 3s: if performed upon screen lock activation, it introduces just a slight delay before locking the device.

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arXiv2019

Obfuscating Java Programs by Translating Selected Portions of Bytecode to Native Libraries

Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, high level bytecode still contains high level relevant information that an attacker might exploit. In order to enable more resilient obfuscations, part of these programs might be implemented with programming languages (e.g., C) that compile to low level machine-dependent code. In fact, machine code contains and leaks less high level information and it enables more resilient obfuscations. In this paper, we present an approach to automatically translate critical sections of high level Java bytecode to C code, so that more effective obfuscations can be resorted to. Moreover, a developer can still work with a single programming language, i.e., Java.

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