As the adoption of wearable and smart devices increases, their privacy and security are still a concern. These devices collect sensitive data and constantly communicate with each other, posing new privacy threats that need to be understood and addressed. In this paper, we analyze the privacy of smart devices from a multi-device perspective. The central premise of our work is that information available at each device may be non-sensitive or lightly so, but by orchestrating information from multiple connected smart devices, it is possible to infer sensitive content. To verify this, we conduct a user study to understand user perceptions towards privacy on smart devices and contrast them with their actual behavior while operating these devices. We then present an attack framework that can leverage tightly coupled and connected smart devices, such as mobile, wearable, and smart TV, to leak sensitive information inferred from individually non-sensitive data. Finally, we introduce a tool based on NLP techniques to identify potential privacy vulnerabilities on smart devices and propose an integrated solution to increase smart devices' security. This analysis helps close the gap between user's perception and reality regarding privacy risks within their smart ecosystem.

Third-party libraries like Log4j accelerate software application development but introduce substantial risk. Vulnerabilities in these libraries have led to Software Supply Chain (SSC) attacks that compromised resources within the host system. These attacks benefit from current application permissions approaches: third-party libraries are implicitly trusted in the application runtime. An application runtime designed with ZeroTrust Architecture (ZTA) principles - secure access to resources, continuous monitoring, and least-privilege enforcement - could mitigate SSC attacks, as it would give zero implicit trust to these libraries. However, no individual security defense incorporates these principles at a low runtime cost. This paper proposes Zero-Trust Dependencies to mitigate SSC vulnerabilities: we apply the NIST ZTA to software applications. First, we assess the expected effectiveness and configuration cost of Zero-Trust Dependencies using a study of third-party software libraries and their vulnerabilities. Then, we present a system design,$\text{ZTD}_{\text{Sys}}$, that enables the application of Zero-Trust Dependencies to software applications and a prototype,$\text{ZTD}_{\text{JAVA}}$, for Java applications. Finally, with evaluations on recreated vulnerabilities and realistic applications, we show that$\text{ZTD}_{\text{JAVA}}$can defend against prevalent vulnerability classes, introduces negligible cost, and is easy to configure and use.

Program partitioning is a proven technique for isolating potentially vulnerable code from trusted program components. We argue that an extreme isolation mechanism is not needed for all use cases. However, existing approaches tightly couple the security policy (what to partition) with the isolation mechanism (how to partition) making them inflexible. We propose TypeFlexer, which cleanly separates these concerns through a type-directed design. Our novel type system uses tainted annotations to mark entities that must be isolated, ensuring that tainted components do not interfere with untainted ones. To facilitate this process, we introduce Typematic, an automated annotation tool that not only propagates taint information according to our type rules but also identifies critical taint explosion points, allowing developers to apply explicit sanitizations where needed. We demonstrate the flexibility of our approach by designing three distinct isolation mechanisms, each with unique security guarantees and performance trade-offs. Our evaluation shows that TypeFlexer effectively contains vulnerabilities with negligible overhead as compared to the $12.8 \%$ performance penalty seen in existing state-of-the-art program partitioning techniques.

GitHub workflows or GitHub CI is a popular continuous integration platform that enables developers to automate various software engineering tasks by specifying them as workflows, i.e., YAML files with a list of jobs. However, engineering valid workflows is tedious. They are also prone to severe security issues, which can result in supply chain vulnerabilities. Recent advancements in Large Language Models (LLMs) have demonstrated their effectiveness in various software development tasks. However, GitHub workflows differ from regular programs in both structure and semantics. We perform the first comprehensive study to understand the effectiveness of Large Language Models (LLMs) on five workflow-related tasks with different levels of prompts. We curated a set of ∼ 400K workflows and generated prompts with varying detail. We also fine-tuned LLMs on GitHub workflow tasks. Our evaluation of three state-of-the-art LLMs and their fine-tuned variants revealed various interesting findings on the current effectiveness and drawbacks of LLMs.

Incorrect handling of Software Application Programming Interfaces (APIs) errors results in bugs or security vulnerabilities that are hard to trigger during regular testing. Most of the existing techniques to detect such errors are based on static analysis and fail to identify certain cases where API return values are incorrectly handled. Furthermore, most of these techniques suffer from a very high false positive rate (≥50%), raising concerns regarding their practical use. We propose a dynamic analysis approach to detect API error handling bugs based on coverage-guided software fault injection. Specifically, we inject faults into APIs and observe how a program handles them. Our fault injection mechanism is generic and targeted to explore a given program's error handling behavior effectively. We avoid false positives by proactively filtering out crashes caused by infeasible faults. We implemented our technique in an automated pipeline called FuzzERR and applied it to 20 different programs spanning 444 APIs. Our evaluation shows that FuzzERR found 31 new and previously unknown bugs resulting from incorrect handling of API errors. Moreover, a comparative evaluation showed that FuzzERR significantly outperformed the state-of-the-art tools.

Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model while preserving the privacy of their sensitive data. Nevertheless, the inherent decentralized and data-opaque characteristics of FL render its susceptibility to data poisoning attacks. These attacks introduce malformed or malicious inputs during local model training, subsequently influencing the global model and resulting in erroneous predictions. Current FL defense strategies against data poisoning attacks either involve a trade-off between accuracy and robustness or necessitate the presence of a uniformly distributed root dataset at the server. To overcome these limitations, we present FedZZ, which harnesses a zone-based deviating update (ZBDU) mechanism to effectively counter data poisoning attacks in FL. The ZBDU approach identifies the clusters of benign clients whose collective updates exhibit notable deviations from those of malicious clients engaged in data poisoning attack. Further, we introduce a precision-guided methodology that actively characterizes these client clusters (zones), which in turn aids in recognizing and discarding malicious updates at the server. Our evaluation of FedZZ across two widely recognized datasets: CIFAR10 and EMNIST, demonstrate its efficacy in mitigating data poisoning attacks, surpassing the performance of prevailing state-of-the-art methodologies in both single and multi-client attack scenarios and varying attack volumes. Notably, FedZZ also functions as a robust client selection strategy, even in highly non-IID and attack-free scenarios. Moreover, in the face of escalating poisoning rates, the model accuracy attained by FedZZ displays superior resilience compared to existing techniques. For instance, when confronted with a 50% presence of malicious clients, FedZZ sustains an accuracy of 67.43%, while the accuracy of the second-best solution, FL-Defender, diminishes to 43.36%.

Rust's high-performance memory safety features help eliminate an entire class of vulnerabilities, making it an attractive choice for mission-critical applications. Another important advantage of using Rust is the availability of a large number of libraries, i.e., crates, that make it easy to develop applications in Rust. However, crates need to be specially designed, i.e., no_std compatible, to be usable on embedded systems. Unfortunately, the majority of the crates are not no_std compatible. In this work, we tackle this problem by developing an automated code refactoring tool, Aunor, to specialize a given crate to be no_std compatible in a valid and backward compatible manner. Our preliminary evaluation shows Aunor is effective and could automatically convert 318 crates.

As Smart TV devices become more prevalent in our lives, it becomes increasingly important to evaluate the security of these devices. In addition to a smart and connected ecosystem through apps, Smart TV devices expose a WiFi remote protocol, that provides a virtual remote capability and allows a WiFi enabled device (e.g., a Smartphone) to control the Smart TV. The WiFi remote protocol might pose certain security risks that are not present in traditional TVs. In this paper, we assess the security of WiFi remote protocols by first identifying the desired security properties so that we achieve the same level of security as in traditional TVs. Our analysis of four popular Smart TV platforms, Android TV, Amazon FireOS, Roku OS, and WebOS (for LG TVs), revealed that all these platforms violate one or more of the identified security properties. To demonstrate the impact of these flaws, we develop Spook, which uses one of the commonly violated properties of a secure WiFi remote protocol to pair an Android mobile as a software remote to an Android TV. Subsequently, we hijack the Android TV device through the device debugger, enabling complete remote control of the device. All our findings have been communicated to the corresponding vendors. Google acknowledged our findings as a security vulnerability, assigned it a CVE, and released patches to the Android TV OS to partially mitigate the attack. We argue that these patches provide a stopgap solution without ensuring that WiFi remote protocol has all the desired security properties. We design and implement a WiFi remote protocol in the Android ecosystem using ARM TrustZone. Our evaluation shows that the proposed defense satisfies all the security properties and ensures that we have the flexibility of virtual remote without compromising security.

Embedded Network Stacks (ENS) enable low-resource devices to communicate with the outside world, facilitating the development of Internet of Things and Cyber-Physical Systems. Some defects in ENS are thus high-severity cybersecurity vulnerabilities: they are remotely triggerable and can impact the physical world. While prior research has shed light on the characteristics of defects in many classes of software systems, no study has described the properties of ENS defects nor identified a systematic technique to expose them. The most common automated approach to detecting ENS defects is feedback-driven randomized dynamic analysis (“fuzzing”), a costly and unpredictable technique. This paper provides the first systematic characterization of cybersecurity vulnerabilities in ENS. We analyzed 61 vulnerabilities across 6 open-source ENS. Most of these ENS defects are concentrated in the transport and network layers of the network stack, require reaching different states in the network protocol, and can be triggered by only 1–2 modifications to a single packet. We therefore propose a novel systematic testing framework that focuses on the transport and network layers, uses seeds that cover a network protocol's states, and systematically modifies packet fields. We evaluate this framework on 4 ENS and replicated 12 of the 14 reported IP/TCP/UDP vulnerabilities. On recent versions of these ENSs, it discovered 7 novel defects (6 assigned CVES) during a bounded systematic test that covered all protocol states and made up to 3 modifications per packet. We found defects in 3 of the 4 ENS we tested that had not been found by prior fuzzing research. Our results suggest that fuzzing should be deferred until after systematic testing is employed.

For portability, embedded systems software follows a layered design to reduce dependence on particular hardware behavior. We consider the problem of identifying layering violations: instances where the embedded application accesses non-adjacent layers. This paper presents our preliminary work to detect a class of layering violations called Non Conventional MMIO Accesses (NCMAs). We find them by searching for direct Memory Mapped Input Output (MMIO) accesses made outside of the Hardware Abstraction Layer (HAL). For evaluation, we curated a list of 988 applications spanning 5 Real Time Operating Systems (RTOSes) – the first large dataset of compilable embedded applications. Our system identified 369 NCMAs. We reported these issues to the corresponding developers and found interesting reasons for committing layering violations. We have open-sourced our tool and the collected dataset to foster future research.

Security and privacy-sensitive smartphone applications use trusted execution environments (TEEs) to protect sensitive operations from malicious code. By design, TEEs have privileged access to the entire system but expose little to no insight into their inner workings. Moreover, real-world TEEs enforce strict format and protocol interactions when communicating with trusted applications (TAs), which prohibits effective automated testing.TEEzz is the first TEE-aware fuzzing framework capable of effectively fuzzing TAs in situ on production smartphones, i.e., the TA runs in the encrypted and protected TEE and the fuzzer may only observe interactions with the TA but has no control over the TA’s code or data. Unlike traditional fuzzing techniques, which monitor the execution of a program being fuzzed and view its memory after a crash, TEEzz only requires a limited view of the target. TEEzz overcomes key limitations of TEE fuzzing (e.g., lack of visibility into the executed TAs, proprietary exchange formats, and value dependencies of interactions) by automatically attempting to infer the field types and message dependencies of the TA API through its interactions, designing state- and type-aware fuzzing mutators, and creating an in situ, on-device fuzzer.Due to the limited availability of systematic fuzzing research for TAs on commercial-off-the-shelf (COTS) Android devices, we extensively examine existing solutions, explore their limitations, and demonstrate how TEEzz improves the state-of-the-art. First, we show that general-purpose kernel driver fuzzers are ineffective for fuzzing TAs. Then, we establish a baseline for fuzzing TAs using a ground-truth experiment. We show that TEEzz outperforms other blackbox fuzzers, can improve greybox approaches (if TAs source code is available), and even outperforms greybox approaches for stateful targets. We found 13 previously unknown bugs in the latest versions of OPTEE TAs in total, out of which TEEzz is the only fuzzer to trigger three. We also ran TEEzz on popular phones and found 40 unique bugs for which one CVE was assigned so far.

Real-world software is usually built on top of other software provided as packages that are managed by package managers. Package managers facilitate code reusability and programmer productivity but incur significant software bloat by installing excessive dependent packages. This dependency hell increases potential security issues and hampers rapid response to newly discovered vulnerabilities. We propose a package-oriented debloating framework, PacJam, for adaptive and security-aware management of an application's dependent packages. PacJam improves upon existing debloating techniques by providing a configurable fallback mechanism via post-deployment policies. It also elides the need to completely specify the application's usage scenarios and does not require runtime support. Moreover, PacJam enables to rapidly mitigate newly discovered vulnerabilities with minimal impact on the application's functionality. We evaluate PacJam on 10 popular and diverse Linux applications comprising 575K-39M SLOC each. Compared to a state-of-the-art approach, piecewise debloating, PacJam debloats 66% of the packages per application on average, reducing the attack surface by removing 46% of CVEs and 69% (versus 66%) of gadgets, with significantly less runtime overhead and without the need to install a custom loader.

Binary analysis is an important capability required for many security and software engineering applications. Consequently, there are many binary analysis techniques and tools with varied capabilities. However, testing these tools requires a large, varied binary dataset with corresponding source-level information. In this paper, we present Cornucopia, an architecture agnostic automated framework that can generate a plethora of binaries from corresponding program source by exploiting compiler optimizations and feedback-guided learning. Our evaluation shows that Cornucopia was able to generate 309K binaries across four architectures (x86, x64, ARM, MIPS) with an average of 403 binaries for each program and outperforms BinTuner [53], a similar technique. Our experiments revealed issues with the LLVM optimization scheduler resulting in compiler crashes (∼ 300). Our evaluation of four popular binary analysis tools angr, Ghidra, ida, and radare, using Cornucopia generated binaries, revealed various issues with these tools. Specifically, we found 263 crashes in angr and one memory corruption issue in ida. Our differential testing on the analysis results revealed various semantic bugs in these tools. We also tested machine learning tools, Asm2Vec, SAFE, and Debin, that claim to capture binary semantics and show that they perform poorly (e.g., Debin F1 score dropped to 12.9% from reported 63.1%) on Cornucopia generated binaries. In summary, our exhaustive evaluation shows that Cornucopia is an effective mechanism to generate binaries for testing binary analysis techniques effectively.

Owing to the continued use of C (and C++), spatial safety violations (e.g., buffer overflows) still constitute one of today's most dangerous and prevalent security vulnerabilities. To combat these violations, Checked C extends C with bounds-enforced checked pointer types. Checked C is essentially a gradually typed spatially safe C - checked pointers are backwards-binary compatible with legacy pointers, and the language allows them to be added piecemeal, rather than necessarily all at once, so that safety retrofitting can be incremental. This paper presents a semi-automated process for porting a legacy C program to Checked C. The process centers on 3C, a static analysis-based annotation tool. 3C employs two novel static analysis algorithms - typ3c and boun3c - to annotate legacy pointers as checked pointers, and to infer array bounds annotations for pointers that need them. 3C performs a root cause analysis to direct a human developer to code that should be refactored; once done, 3C can be re-run to infer further annotations (and updated root causes). Experiments on 11 programs totaling 319KLoC show 3C to be effective at inferring checked pointer types, and experience with previously and newly ported code finds 3C works well when combined with human-driven refactoring.

Software is continually increasing in size and complexity, and therefore, vulnerability discovery would benefit from techniques that identify potentially vulnerable regions within large code bases, as this allows for easing vulnerability detection by reducing the search space. Previous work has explored the use of conventional code-quality and complexity metrics in highlighting suspicious sections of (source) code. Recently, researchers also proposed to reduce the vulnerability search space by studying code properties with neural networks. However, previous work generally failed in leveraging the rich metadata that is available for long-running, large code repositories.

Emulation is at the core of many security analyses. However, emulating embedded systems is still not possible in most cases. To facilitate this critical analysis, we present Conware, a hardware emulation framework that can automatically generate models for hardware peripherals, which alleviates one of the major challenges currently hindering embedded systems emulation. Conware enables individual peripherals to be modeled, exported, and combined with other peripherals in a pluggable fashion. Conware achieves this by first obtaining a recording of the low-level hardware interactions between the firmware and the peripheral, using either existing methods or our source-code instrumentation technique. These recordings are then used to create high-fidelity automata representations of the peripheral using novel automata-generation techniques. The various models can then be merged to facilitate full-system emulation of any embedded firmware that uses any of the modeled peripherals, even if that specific firmware or its target hardware was never directly instrumented. Indeed, we demonstrate that Conware is able to successfully emulate a peripheral-heavy firmware binary that was never instrumented, by merging the models of six unique peripherals that were trained on a development board using only the vendor-provided example code.

Hardware fault injection, or glitching, attacks can compromise the security of devices even when no software vulnerabilities exist. Attempts to analyze the hardware effects of glitching are subject to the Heisenberg effect and there is typically a disconnect between what people “think” is possible and what is actually possible with respect to these attacks. In this work, we attempt to provide some clarity to the impacts of attacks and defenses for control-flow modification through glitching. First, we introduce a glitching emulation framework, which provides a scalable playground to test the effects of bit flips on specific instruction set architectures (ISAs) (i.e., the fault tolerance of the instruction encoding). Next, we examine real glitching experiments using the ChipWhisperer, a popular microcontroller using open-source glitching hardware. These real-world experiments provide novel insights into how glitching attacks are realized and might be defended against in practice. Finally, we present GLITCHRESISTOR, an open-source, software-based glitching defense tool that can automatically insert glitching defenses into any existing source code, in an architecture-independent way. We evaluated GLITCHRESISTOR, which integrates numerous software-only defenses against powerful and real-world glitching attacks. Our findings indicate that software-only defenses can be implemented with acceptable run-time and size overheads, while completely mitigating some single-glitch attacks, minimizing the likelihood of a successful multi-glitch attack (i.e., a success rate of 0.000306%), and detecting failed glitching attempts at a high rate (between 79.2% and 100%).

Software APIs exhibit rich diversity and complexity which not only renders them a common source of programming errors but also hinders program analysis tools for checking them. Such tools either expect a precise API specification, which requires program analysis expertise, or presume that correct API usages follow simple idioms that can be automatically mined from code, which suffers from poor accuracy. We propose a new approach that allows regular programmers to find API misuses. Our approach interacts with the user to classify valid and invalid usages of each target API method. It minimizes user burden by employing an active learning algorithm that ranks API usages by their likelihood of being invalid. We implemented our approach in a tool called ARBITRAR for C/C++ programs, and applied it to check the uses of 18 API methods in 21 large real-world programs, including OpenSSL and Linux Kernel. Within just 3 rounds of user interaction on average per API method, ARBITRAR found 40 new bugs, with patches accepted for 18 of them. Moreover, ARBITRAR finds all known bugs reported by a state-of-the-art tool APISAN in a benchmark suite comprising 92 bugs with a false positive rate of only 51.5% compared to APISAN’s 87.9%.

Internet of Things (IoT) devices have rooted themselves in the everyday life of billions of people. Thus, researchers have applied automated bug finding techniques to improve their overall security. However, due to the difficulties in extracting and emulating custom firmware, black-box fuzzing is often the only viable analysis option. Unfortunately, this solution mostly produces invalid inputs, which are quickly discarded by the targeted IoT device and do not penetrate its code. Another proposed approach is to leverage the companion app (i.e., the mobile app typically used to control an IoT device) to generate well-structured fuzzing inputs. Unfortunately, the existing solutions produce fuzzing inputs that are constrained by app-side validation code, thus significantly limiting the range of discovered vulnerabilities.In this paper, we propose a novel approach that overcomes these limitations. Our key observation is that there exist functions inside the companion app that can be used to generate optimal (i.e., valid yet under-constrained) fuzzing inputs. Such functions, which we call fuzzing triggers, are executed before any data-transforming functions (e.g., network serialization), but after the input validation code. Consequently, they generate inputs that are not constrained by app-side sanitization code, and, at the same time, are not discarded by the analyzed IoT device due to their invalid format. We design and develop Diane, a tool that combines static and dynamic analysis to find fuzzing triggers in Android companion apps, and then uses them to fuzz IoT devices automatically. We use Diane to analyze 11 popular IoT devices, and identify 11 bugs, 9 of which are zero days. Our results also show that without using fuzzing triggers, it is not possible to generate bug-triggering inputs for many devices.

Low-energy Bluetooth devices have become ubiquitous and widely used for different applications. Among these, Bluetooth trackers are becoming popular as they allow users to track the location of their physical objects. To do so, Bluetooth trackers are often built-in within other commercial products connected to a larger crowdsourced tracking system. Such a system, however, can pose a threat to the security and privacy of the users, for instance, by revealing the location of a user's valuable object. In this paper, we introduce a set of security properties and investigate the state of commercial crowdsourced tracking systems, which present common design flaws that make them insecure. Leveraging the results of our investigation, we propose a new design for a secure crowdsourced tracking system (SECrow), which allows devices to leverage the benefits of the crowdsourced model without sacrificing security and privacy. Our preliminary evaluation shows that SECrow is a practical, secure, and effective crowdsourced tracking solution.

Despite the effort of software maintainers, patches to open-source repositories are propagated from the main codebase to all the related projects (e.g., forks) with a significant delay. Previous work shows that this is true also for security patches, which represents a critical problem. Vulnerability databases, such as the CVE database, were born to speed-up the application of critical patches; however, patches associated with CVE entries (i.e., CVE patches) are still applied with a delay, and some security fixes lack the corresponding CVE entries. Because of this, project maintainers could miss security patches when upgrading software.In this paper, we are the first to define safe patches (sps). An sp is a patch that does not disrupt the intended functionality of the program (on valid inputs), meaning that it can be applied with no testing; we argue that most security fixes fall into this category. Furthermore, we show a technique to identify sps, and implement SPIDER1, a tool based on such a technique that works by analyzing the source code of the original and patched versions of a file. We performed a large-scale evaluation on 341,767 patches from 32 large and popular source code repositories as well as on 809 CVE patches. Results show that SPIDER was able to identify 67,408 sps and that most of the CVE patches are sps. In addition, SPIDER identified 2,278 patches that fix vulnerabilities lacking a CVE; 229 of these are still unpatched in different vendor kernels, which can be considered as potential unfixed vulnerabilities.

Low-power, single-purpose embedded devices (e.g., routers and IoT devices) have become ubiquitous. While they automate and simplify many aspects of users' lives, recent large-scale attacks have shown that their sheer number poses a severe threat to the Internet infrastructure. Unfortunately, the software on these systems is hardware-dependent, and typically executes in unique, minimal environments with non-standard configurations, making security analysis particularly challenging. Many of the existing devices implement their functionality through the use of multiple binaries. This multi-binary service implementation renders current static and dynamic analysis techniques either ineffective or inefficient, as they are unable to identify and adequately model the communication between the various executables. In this paper, we present Karonte, a static analysis approach capable of analyzing embedded-device firmware by modeling and tracking multi-binary interactions. Our approach propagates taint information between binaries to detect insecure interactions and identify vulnerabilities. We first evaluated Karonte on 53 firmware samples from various vendors, showing that our prototype tool can successfully track and constrain multi-binary interactions. This led to the discovery of 46 zero-day bugs. Then, we performed a large-scale experiment on 899 different samples, showing that Karonte scales well with firmware samples of different size and complexity.

In the past few years, both the industry and the academic communities have developed several approaches to detect malicious Android apps. State-of-the-art research approaches achieve very high accuracy when performing malware detection on existing datasets. These approaches perform their malware classification tasks in an "offline" scenario, where malware authors cannot learn from and adapt their malicious apps to these systems. In real-world deployments, however, adversaries get feedback about whether their app was detected, and can react accordingly by transforming their code until they are able to influence the classification.

Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.

When performing program analysis, loops are one of the most important aspects that needs to be taken into account. In the past, many approaches have been proposed to analyze loops to perform different tasks, ranging from compiler optimizations to Worst-Case Execution Time (WCET) analysis. While these approaches are powerful, they focus on tackling very specific categories of loops and known loop patterns, such as the ones for which the number of iterations can be statically determined. In this work, we developed a static analysis framework to characterize and analyze generic loops, without relying on techniques based on pattern matching. For this work, we focus on the Android platform, and we implemented a prototype, called CLAPP, that we used to perform the first large-scale empirical study of the usage of loops in Android applications. In particular, we used our tool to analyze a total of 4,110,510 loops found in 11,823 Android applications. As part of our evaluation, we provide the detailed results of our empirical study, we show how our analysis was able to determine that the execution of 63.28% of the loops is bounded, and we discuss several interesting insights related to the performance issues and security aspects associated with loops.

We present a system Dynodroid for generating relevant inputs to unmodified Android apps. Dynodroid views an app as an event-driven program that interacts with its environment by means of a sequence of events through the Android framework. By instrumenting the framework once and for all, Dynodroid monitors the reaction of an app upon each event in a lightweight manner, using it to guide the generation of the next event to the app. Dynodroid also allows interleaving events from machines, which are better at generating a large number of simple inputs, with events from humans, who are better at providing intelligent inputs.