Florian Sieck

Felix Mächtle, Nils Loose, Tim Schulz et al.

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.

Florian Sieck, Sebastian Berndt, Jan Wichelmann et al.

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. In this work, we show that a common oversight in these libraries is the existence of \emph{utility functions}, which handle and thus possibly leak confidential information. We analyze the exploitability of base64 decoding functions across several widely used cryptographic libraries. Base64 decoding is used when loading keys stored in PEM format. We show that these functions by themselves leak sufficient information even if libraries are executed in trusted execution environments. In fact, we show that recent countermeasures to transient execution attacks such as LVI \emph{ease} the exploitability of the observed faint leakages, allowing us to robustly infer sufficient information about RSA private keys \emph{with a single trace}. We present a complete attack, including a broad library analysis, a high-resolution last level cache attack on SGX enclaves, and a fully parallelized implementation of the extend-and-prune approach that allows a complete key recovery at medium costs.

Luca Wilke, Jan Wichelmann, Florian Sieck et al.

The ongoing trend of moving data and computation to the cloud is met with concerns regarding privacy and protection of intellectual property. Cloud Service Providers (CSP) must be fully trusted to not tamper with or disclose processed data, hampering adoption of cloud services for many sensitive or critical applications. As a result, CSPs and CPU manufacturers are rushing to find solutions for secure outsourced computation in the Cloud. While enclaves, like Intel SGX, are strongly limited in terms of throughput and size, AMD's Secure Encrypted Virtualization (SEV) offers hardware support for transparently protecting code and data of entire VMs, thus removing the performance, memory and software adaption barriers of enclaves. Through attestation of boot code integrity and means for securely transferring secrets into an encrypted VM, CSPs are effectively removed from the list of trusted entities. There have been several attacks on the security of SEV, by abusing I/O channels to encrypt and decrypt data, or by moving encrypted code blocks at runtime. Yet, none of these attacks have targeted the attestation protocol, the core of the secure computing environment created by SEV. We show that the current attestation mechanism of Zen 1 and Zen 2 architectures has a significant flaw, allowing us to manipulate the loaded code without affecting the attestation outcome. An attacker may abuse this weakness to inject arbitrary code at startup -- and thus take control over the entire VM execution, without any indication to the VM's owner. Our attack primitives allow the attacker to do extensive modifications to the bootloader and the operating system, like injecting spy code or extracting secret data. We present a full end-to-end attack, from the initial exploit to leaking the key of the encrypted disk image during boot, giving the attacker unthrottled access to all of the VM's persistent data.