@inproceedings{59412, author = {{Karakaya, Kadiray and Bodden, Eric}}, booktitle = {{2023 IEEE Conference on Software Testing, Verification and Validation (ICST)}}, publisher = {{IEEE}}, title = {{{Two Sparsification Strategies for Accelerating Demand-Driven Pointer Analysis}}}, doi = {{10.1109/icst57152.2023.00036}}, year = {{2023}}, } @inproceedings{41812, author = {{Luo, Linghui and Piskachev, Goran and Krishnamurthy, Ranjith and Dolby, Julian and Schäf, Martin and Bodden, Eric}}, booktitle = {{IEEE International Conference on Software Testing, Verification and Validation (ICST)}}, title = {{{Model Generation For Java Frameworks}}}, year = {{2023}}, } @inproceedings{35083, author = {{Dann, Andreas Peter and Hermann, Ben and Bodden, Eric}}, title = {{{UpCy: Safely Updating Outdated Dependencies}}}, year = {{2023}}, } @inproceedings{46500, abstract = {{The security of Industrial Control Systems is relevant both for reliable production system operations and for high-quality throughput in terms of manufactured products. Security measures are designed, operated and maintained by different roles along product and production system lifecycles. Defense-in-Depth as a paradigm builds upon the assumption that breaches are unavoidable. The paper at hand provides an analysis of roles, corresponding Human Factors and their relevance for data theft and sabotage attacks. The resulting taxonomy is reflected by an example related to Additive Manufacturing. The results assist in both designing and redesigning Industrial Control System as part of an entire production system so that Defense-in-Depth with regard to Human Factors is built in by design.}}, author = {{Pottebaum, Jens and Rossel, Jost and Somorovsky, Juraj and Acar, Yasemin and Fahr, René and Arias Cabarcos, Patricia and Bodden, Eric and Gräßler, Iris}}, booktitle = {{2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)}}, keywords = {{Defense-in-Depth, Human Factors, Production Engineering, Product Design, Systems Engineering}}, location = {{Delft, Netherlands}}, pages = {{379--385}}, publisher = {{IEEE}}, title = {{{Re-Envisioning Industrial Control Systems Security by Considering Human Factors as a Core Element of Defense-in-Depth}}}, doi = {{10.1109/eurospw59978.2023.00048}}, year = {{2023}}, } @article{31844, abstract = {{Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks. However, due to its high computational complexity, its applicability to general-purpose computing remains limited. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data. We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described previously. We implemented two flavors of DFAuth, a Java bytecode-to-bytecode compiler, and an SGX enclave running a small and program-independent trusted code base. We applied DFAuth to a neural network performing machine learning on sensitive medical data and a smart charging scheduler for electric vehicles. Our transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in \( 12.55 \,\mathrm{m}\mathrm{s} \) . Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds. }}, author = {{Fischer, Andreas and Fuhry, Benny and Kußmaul, Jörn and Janneck, Jonas and Kerschbaum, Florian and Bodden, Eric}}, issn = {{2471-2566}}, journal = {{ACM Transactions on Privacy and Security}}, keywords = {{Safety, Risk, Reliability and Quality, General Computer Science}}, number = {{3}}, pages = {{1--36}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Computation on Encrypted Data Using Dataflow Authentication}}}, doi = {{10.1145/3513005}}, volume = {{25}}, year = {{2022}}, } @misc{32409, abstract = {{Context: Cryptographic APIs are often misused in real-world applications. Therefore, many cryptographic API misuse detection tools have been introduced. However, there exists no established reference benchmark for a fair and comprehensive comparison and evaluation of these tools. While there are benchmarks, they often only address a subset of the domain or were only used to evaluate a subset of existing misuse detection tools. Objective: To fairly compare cryptographic API misuse detection tools and to drive future development in this domain, we will devise such a benchmark. Openness and transparency in the generation process are key factors to fairly generate and establish the needed benchmark. Method: We propose an approach where we derive the benchmark generation methodology from the literature which consists of general best practices in benchmarking and domain-specific benchmark generation. A part of this methodology is transparency and openness of the generation process, which is achieved by pre-registering this work. Based on our methodology we design CamBench, a fair "Cryptographic API Misuse Detection Tool Benchmark Suite". We will implement the first version of CamBench limiting the domain to Java, the JCA, and static analyses. Finally, we will use CamBench to compare current misuse detection tools and compare CamBench to related benchmarks of its domain.}}, author = {{Schlichtig, Michael and Wickert, Anna-Katharina and Krüger, Stefan and Bodden, Eric and Mezini, Mira}}, keywords = {{cryptography, benchmark, API misuse, static analysis}}, title = {{{CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite}}}, doi = {{10.48550/ARXIV.2204.06447}}, year = {{2022}}, } @inproceedings{32410, abstract = {{Static analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research on static analysis emphasizes its technical challenges but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and in some cases, user dissatisfaction even leads to tool abandonment. To comprehensively assess the current state of the art, this paper presents the first systematic usability evaluation in a wide range of static analysis tools. We derived a set of 36 relevant criteria from the scientific literature and gathered a collection of 46 static analysis tools complying with our inclusion and exclusion criteria - a representative set of mainly non-proprietary tools. Then, we evaluated how well these tools fulfill the aforementioned criteria. The evaluation shows that more than half of the considered tools offer poor warning messages, while about three-quarters of the tools provide hardly any fix support. Furthermore, the integration of user knowledge is strongly neglected, which could be used for improved handling of false positives and tuning the results for the corresponding developer. Finally, issues regarding workflow integration and specialized user interfaces are proved further. These findings should prove useful in guiding and focusing further research and development in the area of user experience for static code analyses.}}, author = {{Nachtigall, Marcus and Schlichtig, Michael and Bodden, Eric}}, booktitle = {{Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis}}, isbn = {{9781450393799}}, keywords = {{Automated static analysis, Software usability}}, pages = {{532 -- 543}}, publisher = {{ACM}}, title = {{{A Large-Scale Study of Usability Criteria Addressed by Static Analysis Tools}}}, doi = {{10.1145/3533767}}, year = {{2022}}, } @inproceedings{31133, abstract = {{Application Programming Interfaces (APIs) are the primary mechanism that developers use to obtain access to third-party algorithms and services. Unfortunately, APIs can be misused, which can have catastrophic consequences, especially if the APIs provide security-critical functionalities like cryptography. Understanding what API misuses are, and for what reasons they are caused, is important to prevent them, e.g., with API misuse detectors. However, definitions and nominations for API misuses and related terms in literature vary and are diverse. This paper addresses the problem of scattered knowledge and definitions of API misuses by presenting a systematic literature review on the subject and introducing FUM, a novel Framework for API Usage constraint and Misuse classification. The literature review revealed that API misuses are violations of API usage constraints. To capture this, we provide unified definitions and use them to derive FUM. To assess the extent to which FUM aids in determining and guiding the improvement of an API misuses detectors' capabilities, we performed a case study on CogniCrypt, a state-of-the-art misuse detector for cryptographic APIs. The study showed that FUM can be used to properly assess CogniCrypt's capabilities, identify weaknesses and assist in deriving mitigations and improvements. And it appears that also more generally FUM can aid the development and improvement of misuse detection tools.}}, author = {{Schlichtig, Michael and Sassalla, Steffen and Narasimhan, Krishna and Bodden, Eric}}, booktitle = {{2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)}}, keywords = {{API misuses, API usage constraints, classification framework, API misuse detection, static analysis}}, pages = {{673 -- 684}}, title = {{{FUM - A Framework for API Usage constraint and Misuse Classification}}}, doi = {{https://doi.org/10.1109/SANER53432.2022.00085}}, year = {{2022}}, } @inproceedings{34057, author = {{Pasic, Faruk and Becker, Matthias}}, booktitle = {{2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, publisher = {{IEEE}}, title = {{{Domain-specific Language for Condition Monitoring Software Development}}}, doi = {{10.1109/etfa52439.2022.9921730}}, year = {{2022}}, } @article{33835, abstract = {{ Nowadays, an increasing number of applications uses deserialization. This technique, based on rebuilding the instance of objects from serialized byte streams, can be dangerous since it can open the application to attacks such as remote code execution (RCE) if the data to deserialize is originating from an untrusted source. Deserialization vulnerabilities are so critical that they are in OWASP’s list of top 10 security risks for web applications. This is mainly caused by faults in the development process of applications and by flaws in their dependencies, i.e., flaws in the libraries used by these applications. No previous work has studied deserialization attacks in-depth: How are they performed? How are weaknesses introduced and patched? And for how long are vulnerabilities present in the codebase? To yield a deeper understanding of this important kind of vulnerability, we perform two main analyses: one on attack gadgets, i.e., exploitable pieces of code, present in Java libraries, and one on vulnerabilities present in Java applications. For the first analysis, we conduct an exploratory large-scale study by running 256 515 experiments in which we vary the versions of libraries for each of the 19 publicly available exploits. Such attacks rely on a combination of gadgets present in one or multiple Java libraries. A gadget is a method which is using objects or fields that can be attacker-controlled. Our goal is to precisely identify library versions containing gadgets and to understand how gadgets have been introduced and how they have been patched. We observe that the modification of one innocent-looking detail in a class – such as making it public – can already introduce a gadget. Furthermore, we noticed that among the studied libraries, 37.5% are not patched, leaving gadgets available for future attacks. For the second analysis, we manually analyze 104 deserialization vulnerabilities CVEs to understand how vulnerabilities are introduced and patched in real-life Java applications. Results indicate that the vulnerabilities are not always completely patched or that a workaround solution is proposed. With a workaround solution, applications are still vulnerable since the code itself is unchanged.}}, author = {{Sayar, Imen and Bartel, Alexandre and Bodden, Eric and Le Traon, Yves}}, issn = {{1049-331X}}, journal = {{ACM Transactions on Software Engineering and Methodology}}, keywords = {{Software}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{An In-depth Study of Java Deserialization Remote-Code Execution Exploits and Vulnerabilities}}}, doi = {{10.1145/3554732}}, year = {{2022}}, } @article{33836, author = {{Piskachev, Goran and Späth, Johannes and Budde, Ingo and Bodden, Eric}}, journal = {{Empirical Software Engineering}}, number = {{5}}, pages = {{1–33}}, publisher = {{Springer}}, title = {{{Fluently specifying taint-flow queries with fluentTQL}}}, volume = {{27}}, year = {{2022}}, } @inproceedings{33838, author = {{Krishnamurthy, Ranjith and Piskachev, Goran and Bodden, Eric}}, title = {{{To what extent can we analyze Kotlin programs using existing Java taint analysis tools?}}}, year = {{2022}}, } @inproceedings{33837, author = {{Piskachev, Goran and Dziwok, Stefan and Koch, Thorsten and Merschjohann, Sven and Bodden, Eric}}, title = {{{How far are German companies in improving security through static program analysis tools?}}}, year = {{2022}}, } @misc{33959, abstract = {{Recent studies have revealed that 87 % to 96 % of the Android apps using cryptographic APIs have a misuse which may cause security vulnerabilities. As previous studies did not conduct a qualitative examination of the validity and severity of the findings, our objective was to understand the findings in more depth. We analyzed a set of 936 open-source Java applications for cryptographic misuses. Our study reveals that 88.10 % of the analyzed applications fail to use cryptographic APIs securely. Through our manual analysis of a random sample, we gained new insights into effective false positives. For example, every fourth misuse of the frequently misused JCA class MessageDigest is an effective false positive due to its occurrence in a non-security context. As we wanted to gain deeper insights into the security implications of these misuses, we created an extensive vulnerability model for cryptographic API misuses. Our model includes previously undiscussed attacks in the context of cryptographic APIs such as DoS attacks. This model reveals that nearly half of the misuses are of high severity, e.g., hard-coded credentials and potential Man-in-the-Middle attacks.}}, author = {{Wickert, Anna-Katharina and Baumgärtner, Lars and Schlichtig, Michael and Mezini, Mira}}, title = {{{To Fix or Not to Fix: A Critical Study of Crypto-misuses in the Wild}}}, doi = {{10.48550/ARXIV.2209.11103}}, year = {{2022}}, } @article{53952, author = {{Massacci, Fabio and Sabetta, Antonino and Mirkovic, Jelena and Murray, Toby and Okhravi, Hamed and Mannan, Mohammad and Rocha, Anderson and Bodden, Eric and Geer, Daniel E.}}, issn = {{1540-7993}}, journal = {{IEEE Security & Privacy}}, number = {{5}}, pages = {{16--21}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{“Free” as in Freedom to Protest?}}}, doi = {{10.1109/msec.2022.3185845}}, volume = {{20}}, year = {{2022}}, } @article{30511, abstract = {{AbstractMany critical codebases are written in C, and most of them use preprocessor directives to encode variability, effectively encoding software product lines. These preprocessor directives, however, challenge any static code analysis. SPLlift, a previously presented approach for analyzing software product lines, is limited to Java programs that use a rather simple feature encoding and to analysis problems with a finite and ideally small domain. Other approaches that allow the analysis of real-world C software product lines use special-purpose analyses, preventing the reuse of existing analysis infrastructures and ignoring the progress made by the static analysis community. This work presents VarAlyzer, a novel static analysis approach for software product lines. VarAlyzer first transforms preprocessor constructs to plain C while preserving their variability and semantics. It then solves any given distributive analysis problem on transformed product lines in a variability-aware manner. VarAlyzer ’s analysis results are annotated with feature constraints that encode in which configurations each result holds. Our experiments with 95 compilation units of OpenSSL show that applying VarAlyzer enables one to conduct inter-procedural, flow-, field- and context-sensitive data-flow analyses on entire product lines for the first time, outperforming the product-based approach for highly-configurable systems.}}, author = {{Schubert, Philipp and Gazzillo, Paul and Patterson, Zach and Braha, Julian and Schiebel, Fabian Benedikt and Hermann, Ben and Wei, Shiyi and Bodden, Eric}}, issn = {{0928-8910}}, journal = {{Automated Software Engineering}}, keywords = {{inter-procedural static analysis, software product lines, preprocessor, LLVM, C/C++}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Static data-flow analysis for software product lines in C}}}, doi = {{10.1007/s10515-022-00333-1}}, volume = {{29}}, year = {{2022}}, } @article{27045, abstract = {{Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors.}}, author = {{Luo, Linghui and Pauck, Felix and Piskachev, Goran and Benz, Manuel and Pashchenko, Ivan and Mory, Martin and Bodden, Eric and Hermann, Ben and Massacci, Fabio}}, issn = {{1382-3256}}, journal = {{Empirical Software Engineering}}, title = {{{TaintBench: Automatic real-world malware benchmarking of Android taint analyses}}}, doi = {{10.1007/s10664-021-10013-5}}, year = {{2021}}, } @phdthesis{27158, author = {{Luo, Linghui}}, publisher = {{Universität Paderborn}}, title = {{{Improving Real-World Applicability of Static Taint Analysis}}}, year = {{2021}}, } @article{21595, author = {{Stockmann, Lars and Laux, Sven and Bodden, Eric}}, issn = {{2589-2258}}, journal = {{Journal of Automotive Software Engineering}}, title = {{{Using Architectural Runtime Verification for Offline Data Analysis}}}, doi = {{10.2991/jase.d.210205.001}}, year = {{2021}}, } @phdthesis{21596, author = {{Fischer, Andreas}}, publisher = {{Universität Paderborn}}, title = {{{Computing on Encrypted Data using Trusted Execution Environments}}}, year = {{2021}}, }