Learning-based malware detection for Android is sensitive to multiple forms of distribution drift. Temporal drift includes (i) the emergence of new families and (ii) variant-level evolution within existing families, while spatial drift manifests as (iii) population-level shifts in the overall app distribution. Although recent work applies active learning to mitigate the resulting performance degradation, it commonly relies on margin-based sampling, which prioritizes samples near the decision boundary and lacks theoretical grounding for improving adaptation to test distribution.

Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse engineering tasks, such as binary code search, vulnerability detection, or malware classification. While many models only take the function in question as contextual input, there have been successful attempts to improve function embeddings by leveraging information from the call graph. In this study, we dissect the implications of these embedding refinements. We conduct experiments using a range of graph-based models on the embeddings generated by two state-of-the-art binary function embedding models. Integrating inter-procedural context, we show that improvements on binary code similarity detection (BCSD) will not necessarily generalize to downstream tasks, neither of semantic nor of syntactic nature. More generally, we find that optimizing for semantic similarity tasks correlates with worse performance on syntactic tasks. By conducting an explanatory analysis on the dataset, we find that the call graph-based enhancements significantly enhance the robustness of embeddings, particularly in scenarios where the initial models struggle. Furthermore, we observe that the added context is more beneficial for namespace-related functions than for those focused on individual logic, confirming that the call graph can be leveraged most effectively in context-dependent scenarios.

Browser extensions put millions of users at risk when misusing their elevated privileges. Despite the current practices of semi-automated code vetting, privacy-violating extensions still thrive in the official stores. We propose an approach for tracking contextual flows from browser-specific sensitive sources like cookies, browsing history, bookmarks, and search terms to suspicious network sinks through network requests. We demonstrate the effectiveness of the approach by a prototype called CodeX that leverages the power of CodeQL while breaking away from the conservativeness of bug-finding flavors of the traditional CodeQL taint analysis. Applying CodeX to the extensions published on the Chrome Web Store between March 2021 and March 2024 identified 1,588 extensions with risky flows. Manual verification of 339 of those extensions resulted in flagging 212 as privacy-violating, impacting up to 3.6M users.

Malicious packages are a problem on npm, but like other malware, they are rarely completely novel and share large semantic similarities. We propose to leverage the existing static analysis framework CodeQL to find malware on npm; but instead of detecting variants of vulnerabilities, we use it to detect variants of malware. We present a methodology for writing queries from recently reported packages, as a way of defining semantic signature for specific malicious behavior, where a single one can then be used to match entire families of malware. An iteration of our approach resulted in the discovery of 125 malicious packages from the registry, without producing a single false alarm.

Android applications record and process personal user data, and they can share it among each other throughcontent providers. While the access is protected through multiple mechanisms, unintentional misconfigurations can allow an attacker to access or modify private application data. In this work, we study how content providers protect private data in a systematic study on 14.4 million Android apps. We identify potentially vulnerable apps by using static analysis to successively reduce the set of target apps. Using a custom attack app, we can confirm data leakage in practice and successfully access privacy-sensitive information. We conclude that this points to an inherent problem in designing secure Android applications and discuss possible mitigations.

Reverse engineers benefit from the presence of identifiers such as function names in a binary, but usually these are removed for release. Training a machine learning model to predict function names automatically is promising but fundamentally hard: unlike words in natural language, most function names occur only once. In this paper, we address this problem by introducing eXtreme Function Labeling (XFL), an extreme multi-label learning approach to selecting appropriate labels for binary functions. XFL splits function names into tokens, treating each as an informative label akin to the problem of tagging texts in natural language. We relate the semantics of binary code to labels through Dexter, a novel function embedding that combines static analysis-based features with local context from the call graph and global context from the entire binary. We demonstrate that XFL/Dexter outperforms the state of the art in function labeling on a dataset of 10,047 binaries from the Debian project, achieving a precision of 83.5%. We also study combinations of XFL with alternative binary embeddings from the literature and show that Dexter consistently performs best for this task. As a result, we demonstrate that binary function labeling can be effectively phrased in terms of multi-label learning, and that binary function embeddings benefit from including explicit semantic features.

The SPECTRE family of speculative execution attacks has required a rethinking of formal methods for security. Approaches based on operational speculative semantics have made initial inroads towards finding vulnerable code and validating defenses. However, with each new attack grows the amount of microarchitectural detail that has to be integrated into the underlying semantics. We propose an alternative, lightweight and axiomatic approach to specifying speculative semantics that relies on insights from memory models for concurrency. We use the CAT modeling language for memory consistency to specify execution models that capture speculative control flow, store-to-load forwarding, predictive store forwarding, and memory ordering machine clears. We present a bounded model checking framework parameterized by our speculative CAT models and evaluate its implementation against the state of the art. Due to the axiomatic approach, our models can be rapidly extended to allow our framework to detect new types of attacks and validate defenses against them.

Debugging symbols in binary executables carry the names of functions and global variables. When present, they greatly simplify the process of reverse engineering, but they are almost always removed (stripped) for deployment. We present the design and implementation of punstrip, a tool which combines a probabilistic fingerprint of binary code based on high-level features with a probabilistic graphical model to learn the relationship between function names and program structure. As there are many naming conventions and developer styles, functions from different applications do not necessarily have the exact same name, even if they implement the exact same functionality. We therefore evaluate punstrip across three levels of name matching: exact; an approach based on natural language processing of name components; and using Symbol2Vec, a new embedding of function names based on random walks of function call graphs. We show that our approach is able to recognize functions compiled across different compilers and optimization levels and then demonstrate that punstrip can predict semantically similar function names based on code structure. We evaluate our approach over open source C binaries from the Debian Linux distribution and compare against the state of the art.

Support for regular expressions in symbolic execution-based tools for test generation and bug finding is insufficient. Common aspects of mainstream regular expression engines, such as backreferences or greedy matching, are ignored or imprecisely approximated, leading to poor test coverage or missed bugs. In this paper, we present a model for the complete regular expression language of ECMAScript 2015 (ES6), which is sound for dynamic symbolic execution of the test and exec functions. We model regular expression operations using string constraints and classical regular expressions and use a refinement scheme to address the problem of matching precedence and greediness. We implemented our model in ExpoSE, a dynamic symbolic execution engine for JavaScript, and evaluated it on over 1,000 Node.js packages containing regular expressions, demonstrating that the strategy is effective and can significantly increase the number of successful regular expression queries and therefore boost coverage.

Machine learning is widely used in security research to classify malicious activity, ranging from malware to malicious URLs and network traffic. However, published performance numbers often seem to leave little room for improvement and, due to a wide range of datasets and configurations, cannot be used to directly compare alternative approaches; moreover, most evaluations have been found to suffer from experimental bias which positively inflates results. In this manuscript we discuss the implementation of Tesseract, an open-source tool to evaluate the performance of machine learning classifiers in a security setting mimicking a deployment with typical data feeds over an extended period of time. In particular, Tesseract allows for a fair comparison of different classifiers in a realistic scenario, without disadvantaging any given classifier. Tesseract is available as open-source to provide the academic community with a way to report sound and comparable performance results, but also to help practitioners decide which system to deploy under specific budget constraints.

Developers of applications relying on cryptographic libraries can easily make mistakes in their use. Popular dynamic languages such as JavaScript make testing or verifying such applications particularly challenging. In this paper, we present our ongoing work toward a methodology for automatically checking security properties in JavaScript code. Our main idea is to attach security annotations to values that encode properties of interest. We illustrate our idea using examples and, as an initial step in our line of work, we present a formalization of security annotations in a statically typed lambda calculus. As next steps, we will translate our annotations to a dynamically typed formalization of JavaScript such as λJS and implement a runtime checked type extension using code instrumentation for full JavaScript.

With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely--on their own--be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware.

JavaScript has evolved into a versatile ecosystem for not just the web, but also a wide range of server-side and client-side applications. With this increased scope, the potential impact of bugs increases. We introduce ExpoSE, a dynamic symbolic execution engine for Node.js applications. ExpoSE automatically generates test cases to find bugs and cover as many paths in the target program as possible. We discuss the specific challenges for symbolic execution arising from the widespread use of regular expressions in such applications. In particular, we make explicit the issues of capture groups, backreferences, and greediness in JavaScript's flavor of regular expressions, and our models improve over previous work that only partially addressed these. We evaluate ExpoSE on three popular JavaScript libraries that make heavy use of regular expressions, and we report a previously unknown bug in the Minimist library.

Security vulnerabilities plague modern systems because writing secure systems code is hard. Promising approaches can retrofit security automatically via runtime checks that implement the desired security policy, these checks guard critical operations, like memory accesses. Alas, the induced slowdown usually exceeds by a wide margin what system users are willing to tolerate in production, so these tools are hardly ever used. As a result, the insecurity of real-world systems persists. We present an approach in which developers/operators can specify what level of overhead they find acceptable for a given workload (e.g., 5%), our proposed tool ASAP then automatically instruments the program to maximize its security while staying within the specified "overhead budget." Two insights make this approach effective: most overhead in existing tools is due to only a few "hot" checks, whereas the checks most useful to security are typically "cold" and cheap. We evaluate ASAP on programs from the Phoronix and SPEC benchmark suites. It can precisely select the best points in the security-performance spectrum. Moreover, we analyzed existing bugs and security vulnerabilities in RIPE, Open SSL, and the Python interpreter, and found that the protection level offered by the ASAP approach is sufficient to protect against all of them.

Symbolic execution is being successfully used to automatically test statically compiled code. However, increasingly more systems and applications are written in dynamic interpreted languages like Python. Building a new symbolic execution engine is a monumental effort, and so is keeping it up-to-date as the target language evolves. Furthermore, ambiguous language specifications lead to their implementation in a symbolic execution engine potentially differing from the production interpreter in subtle ways.

Summary form only given. Symbolic execution has proven to be a practical technique for building automated test case generation and bug finding tools. While the basic technique had been introduced already in the 70s, the advent of modern SAT and SMT solvers has lead to a surge of tools and techniques in the area over the last decade. This tutorial will introduce and compare the different approaches to using symbolic execution for testing and discuss the specific challenges and trade-offs. A main challenge in symbolic execution is path explosion, and various proposals have been made to combat it. I will discuss how these techniques affect the number and type of solver queries that have to be made, and how this can lead to surprising effects on the efficiency of a symbolic execution engine. Going further, we will look at developments to increase the scope of symbolic execution to larger software systems. Specific topics covered include state merging, procedure summaries, abstraction, search strategies, and parallelization.

Summary form only given. Symbolic execution has proven to be a practical technique for building automated test case generation and bug finding tools. While the basic technique had been introduced already in the 70s, the advent of modern SAT and SMT solvers has lead to a surge of tools and techniques in the area over the last decade. This tutorial will introduce and compare the different approaches to using symbolic execution for testing and discuss the specific challenges and trade-offs. A main challenge in symbolic execution is path explosion, and various proposals have been made to either combat it. I will discuss how these techniques affect the number and type of solver queries that have to be made, and how this can lead to surprising effects on the efficiency of a symbolic execution engine. Going further, we will look at developments to increase the scope of symbolic execution to larger software systems. Specific topics covered include state merging, procedure summaries, abstraction, search strategies, and parallelization.

Symbolic execution has proven to be a practical technique for building automated test case generation and bug finding tools. Nevertheless, due to state explosion, these tools still struggle to achieve scalability. Given a program, one way to reduce the number of states that the tools need to explore is to merge states obtained on different paths. Alas, doing so increases the size of symbolic path conditions (thereby stressing the underlying constraint solver) and interferes with optimizations of the exploration process (also referred to as search strategies). The net effect is that state merging may actually lower performance rather than increase it.

To aid the formal verification of fault-tolerant distributed protocols, we propose an approach that significantly reduces the costs of their model checking. These protocols often specify atomic, process-local events that consume a set of messages, change the state of a process, and send zero or more messages. We call such events quorum transitions and leverage them to optimize state exploration in two ways. First, we generate fewer states compared to models where quorum transitions are expressed by single-message transitions. Second, we refine transitions into a set of equivalent, finer-grained transitions that allow partial-order algorithms to achieve better reduction. We implement the MP-Basset model checker, which supports refined quorum transitions. We model check protocols representing core primitives of deployed reliable distributed systems, namely: Paxos consensus, regular storage, and Byzantine-tolerant multicast. We achieve up to 92% memory and 85% time reduction compared to model checking with standard unrefined single-message transitions.

Model checkers offer to automatically prove safety and liveness properties of complex concurrent software systems, but they are limited by state space explosion. Partial-Order Reduction (POR) is an effective technique to mitigate this burden. However, applying existing notions of POR requires to verify conditions based on execution paths of unbounded length, a difficult task in general. To enable a more intuitive and still flexible application of POR, we propose local POR (LPOR). LPOR is based on the existing notion of statically computed stubborn sets, but its locality allows to verify conditions in single states rather than over long paths. As a case study, we apply LPOR to message-passing systems. We implement it within the Java Pathfinder model checker using our general Java-based LPOR library. Our experiments show significant reductions achieved by LPOR for model checking representative message-passing protocols and, maybe surprisingly, that LPOR can outperform dynamic POR.

Whitebox fuzzing is a novel form of security testing based on dynamic symbolic execution and constraint solving. Over the last couple of years, whitebox fuzzers have found many new security vulnerabilities (buffer overflows) in Windows and Linux applications, including codecs, image viewers and media players. Those types of applications tend to use floating-point instructions available on modern processors, yet existing whitebox fuzzers and SMT constraint solvers do not handle floating-point arithmetic. Are there new security vulnerabilities lurking in floating-point code?

Although recent estimates are speaking of 200,000 different viruses, worms, and Trojan horses, the majority of them are variants of previously existing malware. As these variants mostly differ in their binary representation rather than their functionality, they can be recognized by analyzing the program behavior, even though they are not covered by the signature databases of current antivirus tools. Proactive malware detectors mitigate this risk by detection procedures that use a single signature to detect whole classes of functionally related malware without signature updates. It is evident that the quality of proactive detection procedures depends on their ability to analyze the semantics of the binary. In this paper, we propose the use of model checking—a well-established software verification technique—for proactive malware detection. We describe a tool that extracts an annotated control flow graph from the binary and automatically verifies it against a formal malware specification. To this end, we introduce the new specification language CTPL, which balances the high expressive power needed for malware signatures with efficient model checking algorithms. Our experiments demonstrate that our technique indeed is able to recognize variants of existing malware with a low risk of false positives.