arXiv subjects

Thorsten Holz

Publications and source records attributed to Thorsten Holz.

At least 55 records · Page 3Linked to original sources

arXiv2021

[RE] CNN-generated images are surprisingly easy to spot...for now

This work evaluates the reproducibility of the paper "CNN-generated images are surprisingly easy to spot... for now" by Wang et al. published at CVPR 2020. The paper addresses the challenge of detecting CNN-generated imagery, which has reached the potential to even fool humans. The authors propose two methods which help an image classifier to generalize from being trained on one specific CNN to detecting imagery produced by unseen architectures, training methods, or data sets. The paper proposes two methods to help a classifier generalize: (i) utilizing different kinds of data augmentations and (ii) using a diverse data set. This report focuses on assessing if these techniques indeed help the generalization process. Furthermore, we perform additional experiments to study the limitations of the proposed techniques.

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arXiv2020

Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems

Automatic speech recognition (ASR) systems can be fooled via targeted adversarial examples, which induce the ASR to produce arbitrary transcriptions in response to altered audio signals. However, state-of-the-art adversarial examples typically have to be fed into the ASR system directly, and are not successful when played in a room. The few published over-the-air adversarial examples fall into one of three categories: they are either handcrafted examples, they are so conspicuous that human listeners can easily recognize the target transcription once they are alerted to its content, or they require precise information about the room where the attack takes place, and are hence not transferable to other rooms. In this paper, we demonstrate the first algorithm that produces generic adversarial examples, which remain robust in an over-the-air attack that is not adapted to the specific environment. Hence, no prior knowledge of the room characteristics is required. Instead, we use room impulse responses (RIRs) to compute robust adversarial examples for arbitrary room characteristics and employ the ASR system Kaldi to demonstrate the attack. Further, our algorithm can utilize psychoacoustic methods to hide changes of the original audio signal below the human thresholds of hearing. In practical experiments, we show that the adversarial examples work for varying room setups, and that no direct line-of-sight between speaker and microphone is necessary. As a result, an attacker can create inconspicuous adversarial examples for any target transcription and apply these to arbitrary room setups without any prior knowledge.

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arXiv2020

Unacceptable, where is my privacy? Exploring Accidental Triggers of Smart Speakers

Voice assistants like Amazon's Alexa, Google's Assistant, or Apple's Siri, have become the primary (voice) interface in smart speakers that can be found in millions of households. For privacy reasons, these speakers analyze every sound in their environment for their respective wake word like ''Alexa'' or ''Hey Siri,'' before uploading the audio stream to the cloud for further processing. Previous work reported on the inaccurate wake word detection, which can be tricked using similar words or sounds like ''cocaine noodles'' instead of ''OK Google.'' In this paper, we perform a comprehensive analysis of such accidental triggers, i.,e., sounds that should not have triggered the voice assistant, but did. More specifically, we automate the process of finding accidental triggers and measure their prevalence across 11 smart speakers from 8 different manufacturers using everyday media such as TV shows, news, and other kinds of audio datasets. To systematically detect accidental triggers, we describe a method to artificially craft such triggers using a pronouncing dictionary and a weighted, phone-based Levenshtein distance. In total, we have found hundreds of accidental triggers. Moreover, we explore potential gender and language biases and analyze the reproducibility. Finally, we discuss the resulting privacy implications of accidental triggers and explore countermeasures to reduce and limit their impact on users' privacy. To foster additional research on these sounds that mislead machine learning models, we publish a dataset of more than 1000 verified triggers as a research artifact.

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arXiv2020

VPS: Excavating High-Level C++ Constructs from Low-Level Binaries to Protect Dynamic Dispatching

Polymorphism and inheritance make C++ suitable for writing complex software, but significantly increase the attack surface because the implementation relies on virtual function tables (vtables). These vtables contain function pointers that attackers can potentially hijack and in practice, vtable hijacking is one of the most important attack vector for C++ binaries. In this paper, we present VTable Pointer Separation (VPS), a practical binary-level defense against vtable hijacking in C++ applications. Unlike previous binary-level defenses, which rely on unsound static analyses to match classes to virtual callsites, VPS achieves a more accurate protection by restricting virtual callsites to validly created objects. More specifically, VPS ensures that virtual callsites can only use objects created at valid object construction sites, and only if those objects can reach the callsite. Moreover, VPS explicitly prevents false positives (falsely identified virtual callsites) from breaking the binary, an issue existing work does not handle correctly or at all. We evaluate the prototype implementation of VPS on a diverse set of complex, real-world applications (MongoDB, MySQL server, Node.js, SPEC CPU2017/CPU2006), showing that our approach protects on average 97.8% of all virtual callsites in SPEC CPU2006 and 97.4% in SPEC CPU2017 (all C++ benchmarks), with a moderate performance overhead of 11% and 9% geomean, respectively. Furthermore, our evaluation reveals 86 false negatives in VTV, a popular source-based defense which is part of GCC.

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arXiv2020

An Exploratory Analysis of Microcode as a Building Block for System Defenses

Microcode is an abstraction layer used by modern x86 processors that interprets user-visible CISC instructions to hardware-internal RISC instructions. The capability to update x86 microcode enables a vendor to modify CPU behavior in-field, and thus patch erroneous microarchitectural processes or even implement new features. Most prominently, the recent Spectre and Meltdown vulnerabilities were mitigated by Intel via microcode updates. Unfortunately, microcode is proprietary and closed source, and there is little publicly available information on its inner workings. In this paper, we present new reverse engineering results that extend and complement the public knowledge of proprietary microcode. Based on these novel insights, we show how modern system defenses and tools can be realized in microcode on a commercial, off-the-shelf AMD x86 CPU. We demonstrate how well-established system security defenses such as timing attack mitigations, hardware-assisted address sanitization, and instruction set randomization can be realized in microcode. We also present a proof-of-concept implementation of a microcode-assisted instrumentation framework. Finally, we show how a secure microcode update mechanism and enclave functionality can be implemented in microcode to realize a small trusted execution environment. All microcode programs and the whole infrastructure needed to reproduce and extend our results are publicly available.

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arXiv2020

Detile: Fine-Grained Information Leak Detection in Script Engines

Memory disclosure attacks play an important role in the exploitation of memory corruption vulnerabilities. By analyzing recent research, we observe that bypasses of defensive solutions that enforce control-flow integrity or attempt to detect return-oriented programming require memory disclosure attacks as a fundamental first step. However, research lags behind in detecting such information leaks. In this paper, we tackle this problem and present a system for fine-grained, automated detection of memory disclosure attacks against scripting engines. The basic insight is as follows: scripting languages, such as JavaScript in web browsers, are strictly sandboxed. They must not provide any insights about the memory layout in their contexts. In fact, any such information potentially represents an ongoing memory disclosure attack. Hence, to detect information leaks, our system creates a clone of the scripting engine process with a re-randomized memory layout. The clone is instrumented to be synchronized with the original process. Any inconsistency in the script contexts of both processes appears when a memory disclosure was conducted to leak information about the memory layout. Based on this detection approach, we have designed and implemented Detile (\underline{det}ection of \underline{i}nformation \underline{le}aks), a prototype for the JavaScript engine in Microsoft's Internet Explorer 10/11 on Windows 8.0/8.1. An empirical evaluation shows that our tool can successfully detect memory disclosure attacks even against this proprietary software.

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arXiv2020

Automated Multi-Architectural Discovery of CFI-Resistant Code Gadgets

Memory corruption vulnerabilities are still a severe threat for software systems. To thwart the exploitation of such vulnerabilities, many different kinds of defenses have been proposed in the past. Most prominently, Control-Flow Integrity (CFI) has received a lot of attention recently. Several proposals were published that apply coarse-grained policies with a low performance overhead. However, their security remains questionable as recent attacks have shown. To ease the assessment of a given CFI implementation, we introduce a framework to discover code gadgets for code-reuse attacks that conform to coarse-grained CFI policies. For this purpose, binary code is extracted and transformed to a symbolic representation in an architecture-independent manner. Additionally, code gadgets are verified to provide the needed functionality for a security researcher. We show that our framework finds more CFI-compatible gadgets compared to other code gadget discovery tools. Furthermore, we demonstrate that code gadgets needed to bypass CFI solutions on the ARM architecture can be discovered by our framework as well.

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arXiv2020

Challenges in Designing Exploit Mitigations for Deeply Embedded Systems

Memory corruption vulnerabilities have been around for decades and rank among the most prevalent vulnerabilities in embedded systems. Yet this constrained environment poses unique design and implementation challenges that significantly complicate the adoption of common hardening techniques. Combined with the irregular and involved nature of embedded patch management, this results in prolonged vulnerability exposure windows and vulnerabilities that are relatively easy to exploit. Considering the sensitive and critical nature of many embedded systems, this situation merits significant improvement. In this work, we present the first quantitative study of exploit mitigation adoption in 42 embedded operating systems, showing the embedded world to significantly lag behind the general-purpose world. To improve the security of deeply embedded systems, we subsequently present μArmor, an approach to address some of the key gaps identified in our quantitative analysis. μArmor raises the bar for exploitation of embedded memory corruption vulnerabilities, while being adoptable on the short term without incurring prohibitive extra performance or storage costs.

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arXiv2020

Steroids for DOPed Applications: A Compiler for Automated Data-Oriented Programming

The wide-spread adoption of system defenses such as the randomization of code, stack, and heap raises the bar for code-reuse attacks. Thus, attackers utilize a scripting engine in target programs like a web browser to prepare the code-reuse chain, e.g., relocate gadget addresses or perform a just-in-time gadget search. However, many types of programs do not provide such an execution context that an attacker can use. Recent advances in data-oriented programming (DOP) explored an orthogonal way to abuse memory corruption vulnerabilities and demonstrated that an attacker can achieve Turing-complete computations without modifying code pointers in applications. As of now, constructing DOP exploits requires a lot of manual work. In this paper, we present novel techniques to automate the process of generating DOP exploits. We implemented a compiler called Steroids that compiles our high-level language SLANG into low-level DOP data structures driving malicious computations at run time. This enables an attacker to specify her intent in an application- and vulnerability-independent manner to maximize reusability. We demonstrate the effectiveness of our techniques and prototype implementation by specifying four programs of varying complexity in SLANG that calculate the Levenshtein distance, traverse a pointer chain to steal a private key, relocate a ROP chain, and perform a JIT-ROP attack. Steroids compiles each of those programs to low-level DOP data structures targeted at five different applications including GStreamer, Wireshark, and ProFTPd, which have vastly different vulnerabilities and DOP instances. Ultimately, this shows that our compiler is versatile, can be used for both 32- and 64-bit applications, works across bug classes, and enables highly expressive attacks without conventional code-injection or code-reuse techniques in applications lacking a scripting engine.

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arXiv2020

Static Detection of Uninitialized Stack Variables in Binary Code

More than two decades after the first stack smashing attacks, memory corruption vulnerabilities utilizing stack anomalies are still prevalent and play an important role in practice. Among such vulnerabilities, uninitialized variables play an exceptional role due to their unpleasant property of unpredictability: as compilers are tailored to operate fast, costly interprocedural analysis procedures are not used in practice to detect such vulnerabilities. As a result, complex relationships that expose uninitialized memory reads remain undiscovered in binary code. Recent vulnerability reports show the versatility on how uninitialized memory reads are utilized in practice, especially for memory disclosure and code execution. Research in recent years proposed detection and prevention techniques tailored to source code. To date, however, there has not been much attention for these types of software bugs within binary executables. In this paper, we present a static analysis framework to find uninitialized variables in binary executables. We developed methods to lift the binaries into a knowledge representation which builds the base for specifically crafted algorithms to detect uninitialized reads. Our prototype implementation is capable of detecting uninitialized memory errors in complex binaries such as web browsers and OS kernels, and we detected 7 novel bugs.

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arXiv2020

EvilCoder: Automated Bug Insertion

The art of finding software vulnerabilities has been covered extensively in the literature and there is a huge body of work on this topic. In contrast, the intentional insertion of exploitable, security-critical bugs has received little (public) attention yet. Wanting more bugs seems to be counterproductive at first sight, but the comprehensive evaluation of bug-finding techniques suffers from a lack of ground truth and the scarcity of bugs. In this paper, we propose EvilCoder, a system to automatically find potentially vulnerable source code locations and modify the source code to be actually vulnerable. More specifically, we leverage automated program analysis techniques to find sensitive sinks which match typical bug patterns (e.g., a sensitive API function with a preceding sanity check), and try to find data-flow connections to user-controlled sources. We then transform the source code such that exploitation becomes possible, for example by removing or modifying input sanitization or other types of security checks. Our tool is designed to randomly pick vulnerable locations and possible modifications, such that it can generate numerous different vulnerabilities on the same software corpus. We evaluated our tool on several open-source projects such as for example libpng and vsftpd, where we found between 22 and 158 unique connected source-sink pairs per project. This translates to hundreds of potentially vulnerable data-flow paths and hundreds of bugs we can insert. We hope to support future bug-finding techniques by supplying freshly generated, bug-ridden test corpora so that such techniques can (finally) be evaluated and compared in a comprehensive and statistically meaningful way.

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arXiv2020

Breaking and Fixing Destructive Code Read Defenses

Just-in-time return-oriented programming (JIT-ROP) is a powerful memory corruption attack that bypasses various forms of code randomization. Execute-only memory (XOM) can potentially prevent these attacks, but requires source code. In contrast, destructive code reads (DCR) provide a trade-off between security and legacy compatibility. The common belief is that DCR provides strong protection if combined with a high-entropy code randomization. The contribution of this paper is twofold: first, we demonstrate that DCR can be bypassed regardless of the underlying code randomization scheme. To this end, we show novel, generic attacks that infer the code layout for highly randomized program code. Second, we present the design and implementation of BGDX (Byte-Granular DCR and XOM), a novel mitigation technique that protects legacy binaries against code inference attacks. BGDX enforces memory permissions on a byte-granular level allowing us to combine DCR and XOM for legacy, off-the-shelf binaries. Our evaluation shows that BGDX is not only effective, but highly efficient, imposing only a geometric mean performance overhead of 3.95% on SPEC.

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arXiv2020

Leveraging Frequency Analysis for Deep Fake Image Recognition

Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements have been largely made possible by Generative Adversarial Networks (GANs). While deep fake images have been thoroughly investigated in the image domain - a classical approach from the area of image forensics - an analysis in the frequency domain has been missing so far. In this paper, we address this shortcoming and our results reveal that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified. We perform a comprehensive analysis, showing that these artifacts are consistent across different neural network architectures, data sets, and resolutions. In a further investigation, we demonstrate that these artifacts are caused by upsampling operations found in all current GAN architectures, indicating a structural and fundamental problem in the way images are generated via GANs. Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.

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arXiv2020

CORSICA: Cross-Origin Web Service Identification

Vulnerabilities in private networks are difficult to detect for attackers outside of the network. While there are known methods for port scanning internal hosts that work by luring unwitting internal users to an external web page that hosts malicious JavaScript code, no such method for detailed and precise service identification is known. The reason is that the Same Origin Policy (SOP) prevents access to HTTP responses of other origins by default. We perform a structured analysis of loopholes in the SOP that can be used to identify web applications across network boundaries. For this, we analyze HTML5, CSS, and JavaScript features of standard-compliant web browsers that may leak sensitive information about cross-origin content. The results reveal several novel techniques, including leaking JavaScript function names or styles of cross-origin requests that are available in all common browsers. We implement and test these techniques in a tool called CORSICA. It can successfully identify 31 of 42 (74%) of web services running on different IoT devices as well as the version numbers of the four most widely used content management systems WordPress, Drupal, Joomla, and TYPO3. CORSICA can also determine the patch level on average down to three versions (WordPress), six versions (Drupal), two versions (Joomla), and four versions (TYPO3) with only ten requests on average. Furthermore, CORSICA is able to identify 48 WordPress plugins containing 65 vulnerabilities. Finally, we analyze mitigation strategies and show that the proposed but not yet implemented strategies Cross-Origin Resource Policy (CORP)} and Sec-Metadata would prevent our identification techniques.

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arXiv2020

Beyond the Front Page: Measuring Third Party Dynamics in the Field

In the modern Web, service providers often rely heavily on third parties to run their services. For example, they make use of ad networks to finance their services, externally hosted libraries to develop features quickly, and analytics providers to gain insights into visitor behavior. For security and privacy, website owners need to be aware of the content they provide their users. However, in reality, they often do not know which third parties are embedded, for example, when these third parties request additional content as it is common in real-time ad auctions. In this paper, we present a large-scale measurement study to analyze the magnitude of these new challenges. To better reflect the connectedness of third parties, we measured their relations in a model we call third party trees, which reflects an approximation of the loading dependencies of all third parties embedded into a given website. Using this concept, we show that including a single third party can lead to subsequent requests from up to eight additional services. Furthermore, our findings indicate that the third parties embedded on a page load are not always deterministic, as 50% of the branches in the third party trees change between repeated visits. In addition, we found that 93% of the analyzed websites embedded third parties that are located in regions that might not be in line with the current legal framework. Our study also replicates previous work that mostly focused on landing pages of websites. We show that this method is only able to measure a lower bound as subsites show a significant increase of privacy-invasive techniques. For example, our results show an increase of used cookies by about 36% when crawling websites more deeply.

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arXiv2019

(Un)informed Consent: Studying GDPR Consent Notices in the Field

Since the adoption of the General Data Protection Regulation (GDPR) in May 2018 more than 60 % of popular websites in Europe display cookie consent notices to their visitors. This has quickly led to users becoming fatigued with privacy notifications and contributed to the rise of both browser extensions that block these banners and demands for a solution that bundles consent across multiple websites or in the browser. In this work, we identify common properties of the graphical user interface of consent notices and conduct three experiments with more than 80,000 unique users on a German website to investigate the influence of notice position, type of choice, and content framing on consent. We find that users are more likely to interact with a notice shown in the lower (left) part of the screen. Given a binary choice, more users are willing to accept tracking compared to mechanisms that require them to allow cookie use for each category or company individually. We also show that the wide-spread practice of nudging has a large effect on the choices users make. Our experiments show that seemingly small implementation decisions can substantially impact whether and how people interact with consent notices. Our findings demonstrate the importance for regulation to not just require consent, but also provide clear requirements or guidance for how this consent has to be obtained in order to ensure that users can make free and informed choices.

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arXiv2019

Reverse Engineering x86 Processor Microcode

Microcode is an abstraction layer on top of the physical components of a CPU and present in most general-purpose CPUs today. In addition to facilitate complex and vast instruction sets, it also provides an update mechanism that allows CPUs to be patched in-place without requiring any special hardware. While it is well-known that CPUs are regularly updated with this mechanism, very little is known about its inner workings given that microcode and the update mechanism are proprietary and have not been throughly analyzed yet. In this paper, we reverse engineer the microcode semantics and inner workings of its update mechanism of conventional COTS CPUs on the example of AMD's K8 and K10 microarchitectures. Furthermore, we demonstrate how to develop custom microcode updates. We describe the microcode semantics and additionally present a set of microprograms that demonstrate the possibilities offered by this technology. To this end, our microprograms range from CPU-assisted instrumentation to microcoded Trojans that can even be reached from within a web browser and enable remote code execution and cryptographic implementation attacks.

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arXiv2019

Towards Automated Application-Specific Software Stacks

Software complexity has increased over the years. One common way to tackle this complexity during development is to encapsulate features into a shared library. This allows developers to reuse already implemented features instead of reimplementing them over and over again. However, not all features provided by a shared library are actually used by an application. As a result, an application using shared libraries loads unused code into memory, which an attacker can use to perform code-reuse and similar types of attacks. The same holds for applications written in a scripting language such as PHP or Ruby: The interpreter typically offers much more functionality than is actually required by the application and hence provides a larger overall attack surface. In this paper, we tackle this problem and propose a first step towards automated application-specific software stacks. We present a compiler extension capable of removing unneeded code from shared libraries and---with the help of domain knowledge---also capable of removing unused functionalities from an interpreter's code base during the compilation process. Our evaluation against a diverse set of real-world applications, among others Nginx, Lighttpd, and the PHP interpreter, removes on average 71.3% of the code in musl-libc, a popular libc implementation. The evaluation on web applications show that a tailored PHP interpreter can mitigate entire vulnerability classes, as is the case for OpenConf. We demonstrate the applicability of our debloating approach by creating an application-specific software stack for a Wordpress web application: we tailor the libc library to the Nginx web server and PHP interpreter, whereas the PHP interpreter is tailored to the Wordpress web application. In this real-world scenario, the code of the libc is decreased by 65.1% in total, thereby reducing the available code for code-reuse attacks.

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