Containers have become the standard for deploying applications in many cloud systems due to its convenience. However, this convenience leads to significant container bloat, i.e., unused files that inflate container image sizes, increase provisioning times, waste resources and introduce security vulnerabilities. Bloat is particularly problematic in serverless and edge computing scenarios, where resources are constrained, and performance is critical, and for microservice applications where rapid scaling is key to meet performance targets. However, existing container debloating tools are often limited in both effectiveness and robustness. In this paper, we propose BLAFS, a bloat-aware container filesystem that removes bloat while guaranteeing the correct operation of the debloated containers. BLAFS addresses bloat at the filesystem level by introducing new layers in the filesystem to enable debloating. During runtime, accessed files are moved to the debloating layers, and then similar to garbage collection mechanisms, BLAFS removes files that are not accessed during runtime. An optional reloading layer fetches files from a remote cloud cache on-demand if the files are mistakenly removed. We discuss how BLAFS can be used in different deployment scenarios and for different use-cases including container security-hardened and a dynamic deployment mode where the target is improved provisioning performance. We evaluate BLAFS performance using the top 20 downloaded containers from DockerHub, four ML containers, and SEBS, a Serverless Benchmark containing 10 serverless functions and compare its performance against two state-of-the-art debloating tools. Our evaluation shows that BLAFS reduces container sizes by up to 95% and cold-starts by up to 68%. In the security-hardened mode, BLAFS removes up to 89% of CVEs while the two state-of-the-art debloating tools fail on most of the workloads. We identify their limitations, and show how BLAFS provides a more principled approach to debloating. Additionally, when combined with lazy-loading snapshotters, BLAFS improves provisioning efficiency, reducing conversion times by up to 93% and provisioning times by up to 19%.

Declarative specification languages like Alloy are critical for modeling and verifying complex software systems, yet repairing these specifications remains a significant challenge for ensuring software dependability. This study conducts the first comprehensive empirical evaluation comparing traditional systematic repair techniques with emerging Large Language Model (LLM)-based approaches across two established benchmarks, analyzing over 1,900 Alloy specifications. By systematically analyzing repair success rates, ground truth similarity, and repair generation strategies, we reveal nuanced performance characteristics of different repair methodologies. Our findings demonstrate that while traditional tools excel in systematic fault localization and achieving high ground truth similarity, LLM-based techniques—particularly multi-round prompting approaches—offer unique capabilities in addressing complex specification errors, with some hybrid approaches achieving repair rates of up to 85.5%. Critically, we show that integrating traditional fault localization techniques with LLM-based repair strategies can significantly enhance overall repair effectiveness and specification dependability. This research provides a large-scale empirical evaluation of how various Alloy repair techniques work in synergy, offering valuable insights that chart a promising path for future automated specification repair approaches and contribute to the development of more reliable and secure software systems.

Abstract Automatic Program Repair (APR) has garnered significant attention as a practical research domain focused on automatically fixing bugs in programs. While existing APR techniques primarily target imperative programming languages like C and Java, there is a growing need for effective solutions applicable to declarative software specification languages. This paper systematically investigates the capacity of Large Language Models (LLMs) to repair declarative specifications in Alloy, a declarative formal language used for software specification. We designed six different repair settings, encompassing single-agent and dual-agent paradigms, utilizing various LLMs. These configurations also incorporate different levels of feedback, including an auto-prompting mechanism for generating prompts autonomously using LLMs. Our study reveals that dual-agent with auto-prompting setup outperforms the other settings, albeit with a marginal increase in the number of iterations and token usage. This dual-agent setup demonstrated superior effectiveness compared to state-of-the-art Alloy APR techniques when evaluated on a comprehensive set of benchmarks. This work is the first to empirically evaluate LLM capabilities to repair declarative specifications, while taking into account recent trending LLM concepts such as LLM-based agents, feedback, auto-prompting, and tools, thus paving the way for future agent-based techniques in software engineering.

Multiple Perspective attack Investigation (MPI) is a technique to partition application dependencies based on high-level semantics. It facilitates provenance analysis by generating succinct causal graphs. It involves an annotation process that identifies variables and data structures corresponding to the partitions and the communication channels between them. Though the amount of annotation is small, this process requires a detailed understanding of the source code. In this work, autoMPI, we extend the capability ofMPIby automating the identifying annotation requirements. We leverage a hybrid analysis approach, performing a differential analysis based on crafted inputs. Static analysis is conducted to identify the annotation sites within the application code afterward automatically. Our evaluation shows the proposed approach can significantly facilitate the annotation process. It correctly identifies all required annotation sites within an average 16 seconds analysis time for the majority of analyzed programs with average precision and recall 72.5% and 100%, respectively.

Due to the growing presence of Internet of Things (IoT) apps and devices in smart homes and smart cities, there are more and more concerns about their security and privacy risks. IoT apps normally interact with each other and the physical world to offer utility to the users. In this paper, we investigate the safety and security risks brought by the interactive behaviors of IoT apps. Two major challenges ensue in identifying the interaction threats: i) how to discover the threats across both cyber and physical channels; and ii) how to ensure the scalability of the detection approach. To address these challenges, we first provide a taxonomy of interaction threats between IoT apps, which contains seven classes of coordination threats categorized based on their interaction behaviors. Then, we presentIoTCom, a compositional threat detection system capable of automatically detecting and verifying unsafe interactions between IoT apps and devices.IoTComapplies static analysis to automatically infer relevant apps’ behaviors, and uses a novel strategy to trim the extracted app's behaviors prior to translating them into analyzable formal specifications, mitigating the state explosion associated with formal analysis. Our experiments with numerous bundles of real-world IoT apps have corroboratedIoTCom's ability to effectively identify a broad spectrum of interaction threats triggered through cyber and physical channels, many of which were previously unknown. Finally,IoTComuses an automatic verifier to validate the discovered threats. Our experimental results show thatIoTComsignificantly outperforms the existing techniques in terms of the computational time, and maintains the capability to perform its analysis across different IoT platforms.

Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. These approaches are either based on unsound strategies or demanding techniques, leading to unsafe results or a high-overhead debloating process. In this paper, we address these limitations by applying partial-evaluation principles to generate specialized applications. Our approach relies on a simple observation that an application typically consists of configuration logic, followed by the main logic of the program. The configuration logic specifies what functionality in the main logic should be executed. LMCAS performs partial interpretation to capture a precise program state of the configuration logic based on the supplied inputs. LMCAS then applies partial-evaluation optimizations to generate a specialized program by propagating the constants in the captured partial state, eliminating unwanted code, and preserving the desired functionalities. Our evaluation of LMCAS-on commonly used benchmarks and real-world applications-shows that it successfully removes unwanted features while preserving the functionality and robustness of the debloated programs, runs faster than prior tools, and reduces the attack surface of specialized programs. LMCAS runs 1500x, 4.6x, and 1.2x faster than the state-of-the-art debloating tools CHISEL, RAZOR, and OCCAM, respectively; achieves 25% reduction in the binary size; demonstrates favorable gadgets elimination trade-off; and eliminates 87.5% of the known CVE vulnerabilities in our test corpus.

Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models, and in many settings the adversarial perturbation need not be bounded by small $\ell_p$-norms. Motivated by attacks in program analysis and security tasks, we investigate $\textit{relational adversaries}$, a broad class of attackers who create adversarial examples in a reflexive-transitive closure of a logical relation. We analyze the conditions for robustness against relational adversaries and investigate different levels of robustness-accuracy trade-off due to various patterns in a relation. Inspired by the insights, we propose $\textit{normalize-and-predict}$, a learning framework that leverages input normalization to achieve provable robustness. The framework solves the pain points of adversarial training against relational adversaries and can be combined with adversarial training for the benefits of both approaches. Guided by our theoretical findings, we apply our framework to source code authorship attribution and malware detection. Results of both tasks show our learning framework significantly improves the robustness of models against relational adversaries. In the process, it outperforms adversarial training, the most noteworthy defense mechanism, by a wide margin.

The ubiquity of Internet of Things (IoT) and our growing reliance on IoT apps are leaving us more vulnerable to safety and security threats than ever before. Many of these threats are manifested at the interaction level, where undesired or malicious coordinations between apps and physical devices can lead to intricate safety and security issues. This paper presents IoTCOM, an approach to automatically discover such hidden and unsafe interaction threats in a compositional and scalable fashion. It is backed with auto-mated program analysis and formally rigorous violation detection engines. IoTCOM relies on program analysis to automatically infer the relevant app’s behavior. Leveraging a novel strategy to trim the extracted app’s behavior prior to translating them to analyzable formal specifications,IoTCOM mitigates the state explosion associated with formal analysis. Our experiments with numerous bundles of real-world IoT apps have corroborated IoTCOM’s ability to effectively detect a broad spectrum of interaction threats triggered through cyber and physical channels, many of which were previously unknown, and to significantly outperform the existing techniques in terms of scalability.

Android inter-app communication (IAC) allows apps to request functionalities from other apps, which has been extensively used to provide a better user experience. However, IAC has also become an enticing target by attackers to launch malicious activities. Dynamic class loading (DCL) and reflection are effective features to enhance the functionality of the apps. In this paper, we expose a new attack that leverages these features in conjunction with inter-app communication to conceal malicious attacks with the ability to bypass existing security mechanisms. To counteract such attack, we present DINA, a novel hybrid analysis approach for identifying malicious IAC behaviors concealed within dynamically loaded code through reflective/DCL calls. DINA appends reflection and DCL invocations to control-flow graphs and continuously performs incremental dynamic analysis to detect the misuse of reflection and DCL that obfuscates malicious Intent communications. DINA utilizes string analysis and inter-procedural analysis to resolve hidden IAC and achieves superior detection performance. Our extensive evaluation on 49,000 real-world apps corroborates the prevalent usage of reflection and DCL, and reveals previously unknown and potentially harmful, hidden IAC behaviors in real-world apps.

Java reflection and dynamic class loading (DCL) are effective features for enhancing the functionalities of Android apps. However, these features can be abused by sophisticated malware to bypass detection schemes. Advanced malware can utilize reflection and DCL in conjunction with Android Inter-App Communication (IAC) to launch collusion attacks using two or more apps. Such dynamically revealed malicious behaviors enable a new type of stealthy, collusive attacks, bypassing all existing detection mechanisms. In this paper, we present DINA, a novel hybrid analysis approach for identifying malicious IAC behaviors concealed within dynamically loaded code through reflective/DCL calls. DINA continuously appends reflection and DCL invocations to control-flow graphs; it then performs incremental dynamic analysis on such augmented graphs to detect the misuse of reflection and DCL that may lead to malicious, yet concealed, IAC activities. Our extensive evaluation on 3,000 real-world Android apps and 14,000 malicious apps corroborates the prevalent usage of reflection and DCL, and reveals previously unknown and potentially harmful, hidden IAC behaviors in real-world apps.

Breast cancer is very popular between females all over the world. However, detecting this cancer in its first stages helps in saving lives. Radiologists can predict if the mammography images have cancer or not, but they may miss about 15% of them. In this paper, we propose a new method to detect the breast cancer with high accuracy. This method consists of two main parts, in the first part the image processing techniques are used to prepare the mammography images for feature and pattern extraction process. The second part is presented by utilizing the extracted features as an input for a two types of supervised learning models, which are Back Propagation Neural Network (BPNN) model and the Logistic Regression (LR) model. In this paper we examined the accuracy of these models. The results showed that the LR model utilized more features than the BPNN.

Security is considered a significant deficiency in cloud computing, and insider threats problem exacerbate security concerns in the cloud. In addition to that, cloud computing is very complex by itself, because it encompasses numerous technologies and concepts. Apparently, overcoming these challenges requires substantial efforts from information security researchers to develop powerful mitigation solutions for this emerging problem. This entails developing a taxonomy of insider threats in cloud environments encompassing all potential abnormal activities in the cloud and can be useful for conducting security assessment. This article describes the first phase of an ongoing research to develop a framework for mitigating insider threats in cloud computing environments. Primarily, it presents a multidimensional taxonomy of insider threats in cloud computing and demonstrates its viability. The taxonomy provides a fundamental understanding for this complicated problem by identifying five dimensions; it also supports security engineers in identifying hidden paths, thus determining proper countermeasures, and presents a guidance that covers all bounders of insiders’ threats issue in clouds; hence, it facilitates researchers’ endeavours in tackling this problem. For instance, according to the hierarchical taxonomy, clearly many significant issues exist in public cloud, while conventional insider mitigation solutions can be used in private clouds. Finally, the taxonomy assists in identifying future research directions in this emerging area.