As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produce many false positives. While machine learning-based approaches are promising, they typically perform well only in scenarios where training and test data are closely related. A key challenge for ML-based vulnerability detection is providing suitable and concise code context, as excessively long contexts negatively affect the code comprehension capabilities of machine learning models, particularly smaller ones. This work introduces Trace Gadgets, a novel code representation that minimizes code context by removing non-related code. Trace Gadgets precisely capture the statements that cover the path to the vulnerability. As input for ML models, Trace Gadgets provide a minimal but complete context, thereby improving the detection performance. Moreover, we collect a large-scale dataset generated from real-world applications with manually curated labels to further improve the performance of ML-based vulnerability detectors. Our results show that state-of-the-art machine learning models perform best when using Trace Gadgets compared to previous code representations, surpassing the detection capabilities of industry-standard static scanners such as GitHub's CodeQL by at least 4% on a fully unseen dataset. By applying our framework to real-world applications, we identify and report previously unknown vulnerabilities in widely deployed software.

Trusted Execution Environments are a promising solution for solving the data privacy and trust issues introduced by cloud computing. As a result, all major CPU vendors integrated Trusted Execution Environments (TEEs) into their CPUs. The biggest threat to TEE security are side-channel attacks, of which single-stepping attacks turned out to be the most powerful ones. Enabled by the TEE attacker model, single-stepping attacks allow the attacker to execute the TEE one instruction at a time, enabling numerous controlled- and side-channel based security issues. Intel recently launched Intel TDX, its second generation TEE, which protects whole virtual machines (VMs). To minimize the attack surface to side-channels, TDX comes with a dedicated single-stepping attack countermeasure. In this paper, we systematically analyze the single-stepping countermeasure of Intel TDX and show, for the first time, that both, the built-in detection heuristic as well as the prevention mechanism, can be circumvented. We reliably single-step TDX-protected VMs by deluding the TDX security monitor about the elapsed processing time used as part of the detection heuristic. Moreover, our study reveals a design flaw in the single-stepping countermeasure that turns the prevention mechanism against itself: An inherent side-channel within the prevention mechanism leaks the number of instructions executed by the TDX-protected VM, enabling a novel attack we refer to as StumbleStepping. Both attacks, single-stepping and StumbleStepping, work on the most recent Intel TDX enabled Xeon Scalable CPUs. Using StumbleStepping, we demonstrate a novel end-to-end attack against wolfSSL's ECDSA implementation, exploiting a control flow side-channel in its truncation-based nonce generation algorithm. We provide a systematic study of nonce-truncation implementations, revealing similar leakages in OpenSSL, which we exploit with our single-stepping primitive. Finally, we propose design changes to TDX to mitigate our attacks.

Abstract SWAT is a novel dynamic symbolic execution engine for Java applications utilizing dynamic instrumentation. SWAT’s unique modular design facilitates flexible communication between its symbolic explorer and executor using HTTP endpoints, thus enhancing adaptability to diverse application scenarios. The symbolic executor’s ability to attach to Java applications enables efficient constraint generation and path exploration. SWAT employs JavaSMT for constraint generation and ASM for bytecode instrumentation, ensuring robust performance. SWAT’s efficacy is evaluated in the Java Track of SV-COMP 2024, achieving fourth place.

Secret-dependent timing behavior in cryptographic implementations has resulted in exploitable vulnerabilities, undermining their security. Over the years, numerous tools to automatically detect timing leakage or even to prove their absence have been proposed. However, a recent study at IEEE S&P 2022 showed that, while many developers are aware of one or more analysis tools, they have major difficulties integrating these into their workflow, as existing tools are tedious to use and mapping discovered leakages to their originating code segments requires expert knowledge. In addition, existing tools focus on compiled languages like C, or analyze binaries, while the industry and open-source community moved to interpreted languages, most notably JavaScript.

Implementations of cryptographic libraries have been scrutinized for secret-dependent execution behavior exploitable by microarchitectural side-channel attacks. To prevent unintended leakages, most libraries moved to constant-time implementations of cryptographic primitives. There have also been efforts to certify libraries for use in sensitive areas, like Microsoft CNG and Botan, with specific attention to leakage behavior.