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    3406 research outputs found

    DoubleX: Statically Detecting Vulnerable Data Flows in Browser Extensions at Scale

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    Browser extensions are popular to enhance users' browsing experience. By design, they have access to security- and privacy-critical APIs to perform tasks that web applications cannot traditionally do. Even though web pages and extensions are isolated, they can communicate through messages. Specifically, a vulnerable extension can receive messages from another extension or web page, under the control of an attacker. Thus, these communication channels are a way for a malicious actor to elevate their privileges to the capabilities of an extension, which can lead to, e.g., universal cross-site scripting or sensitive user data exfiltration. To automatically detect such security and privacy threats in benign-but-buggy extensions, we propose our static analyzer DoubleX. DoubleX defines an Extension Dependence Graph (EDG), which abstracts extension code with control and data flows, pointer analysis, and models the message interactions within and outside of an extension. This way, we can leverage this graph to track and detect suspicious data flows between external actors and sensitive APIs in browser extensions. We evaluated DoubleX on 154,484 Chrome extensions, where it flags 278 extensions as having a suspicious data flow. Overall, we could verify that 89% of these flows can be influenced by external actors (i.e., an attacker). Based on our threat model, we subsequently demonstrate exploitability for 184 extensions. Finally, we evaluated DoubleX on a labeled vulnerable extension set, where it accurately detects almost 93% of known flaws

    Preventing Dynamic Library Compromise on Node.js via RWX-Based Privilege Reduction

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    Third-party libraries ease the development of large-scale software systems. However, libraries often execute with significantly more privilege than needed to complete their task. Such additional privilege is sometimes exploited at runtime via inputs passed to a library, even when the library itself is not actively malicious. We present Mir, a system addressing dynamic compromise by introducing a fine-grained read-write-execute (RWX) permission model at the boundaries of libraries: every field of every free variable name in the context of an imported library is governed by a permission set. To help specify the permissions given to existing code, Mir’s automated inference generates default permissions by analyzing how libraries are used by their clients. Applied to over 1,000 JavaScript libraries for Node.js, Mir shows practical security (61/63 attacks mitigated), performance (2.1s for static analysis and +1.93% for dynamic enforcement), and compatibility (99.09%) characteristics and enables a novel quantification of privilege reduction

    vBump: Securing Ethernet-based Industrial Control System Networks with VLAN-based Traffic Aggregation

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    Bump-in-the-wire (bump) devices can be used to protect critical endpoints in Industrial Control System (ICS) networks. However, bump devices cannot be used to authenticate incoming broadcast traffic, are complex to manage, and one bump is needed per host. In this work, we propose a virtual bump-like solution called vBump, which allows to insert virtual bumps in front of Ethernet- based legacy ICS devices. The vBumps can be used to limit traffic to whitelisted destinations, inspect all traffic on or above Link- layer like a centralized intrusion detection systems (or monitoring systems), or even police the traffic like a centralized intrusion pre- vention systems. In particular, this also allows the network to apply fine-grained control on traffic between nodes that need to be in the same Link-layer broadcast domain. Compared to traditional bumps, vBumps do not require any changes in physical network topology, and the central server’s global view allows for more informed deci- sion, with less computational constraints. We implement the system in a high-fidelity ICS testbed, and demonstrate its capabilities to support even time-critical protection control traffic in smart grids. Our system can handle traffic rates of 150Mbps with one-way delay of ≈ 1ms

    Visual Analysis of Hyperproperties for Understanding Model Checking Results

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    Model checkers provide algorithms for proving that a mathematical model of a system satisfies a given specification. In case of a violation, a counterexample that shows the erroneous behavior is returned. Understanding these counterexamples is challenging, especially for hyperproperty specifications, i.e., specifications that relate multiple executions of a system to each other. We aim to facilitate the visual analysis of such counterexamples through our HYPERVIS tool, which provides interactive visualizations of the given model, specification, and counterexample. Within an iterative and interdisciplinary design process, we developed visualization solutions that can effectively communicate the core aspects of the model checking result. Specifically, we introduce graphical representations of binary values for improving pattern recognition, color encoding for better indicating related aspects, visually enhanced textual descriptions, as well as extensive cross-view highlighting mechanisms. Further, through an underlying causal analysis of the counterexample, we are also able to identify values that contributed to the violation and use this knowledge for both improved encoding and highlighting. Finally, the analyst can modify both the specification of the hyperproperty and the system directly within HYPERVIS and initiate the model checking of the new version. In combination, these features notably support the analyst in understanding the error leading to the counterexample as well as iterating the provided system and specification. We ran multiple case studies with HYPERVIS and tested it with domain experts in qualitative feedback sessions. The participants’ positive feedback confirms the considerable improvement over the manual, text-based status quo and the value of the tool for explaining hyperproperties

    Model Checking Algorithms for Hyperproperties (Invited Paper)

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    Hyperproperties generalize trace properties by expressing relations between multiple computations. Hyperpropertes include policies from information-flow security, like observational determinism or noninterference, and many other system properties including promptness and knowledge. In this paper, we give an overview on the model checking problem for temporal hyperlogics. Our starting point is the model checking algorithm for HyperLTL, a reduction to Büchi automata emptiness. This basic construction can be extended with propositional quantification, resulting in an algorithm for HyperQPTL. It can also be extended with branching time, resulting in an algorithm for HyperCTL∗. However, it is not possible to have both extensions at the same time: the model checking problem of HyperQCTL∗ is undecidable. An attractive compromise is offered by MPL[E], i.e., monadic path logic extended with the equal-level predicate. The expressiveness of MPL[E] falls strictly between that of HyperCTL∗ and HyperQCTL∗. MPL[E] subsumes both HyperCTL∗ and HyperKCTL∗, the extension of HyperCTL∗ with the knowledge operator. We show that the model checking problem for MPL[E] is still decidable

    Bringing Balance to the Force: Dynamic Analysis of the Android Application Framework

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    Android's application framework plays a crucial part in protecting users' private data and the system integrity. Consequently, it has been the target of various prior works that analyzed its security policy and enforcement. Those works uncovered different security problems, including incomplete documentation, permission re-delegation within the framework, and inconsistencies in access control. However, all but one of those prior works were based on static code analysis. Thus, their results provide a one-sided view that inherits the limitations and drawbacks of applying static analysis to the vast, complex code base of the application framework. Even more, the performances of different security applications---including malware classification and least-privileged apps---depend on those analysis results, but those applications are currently tarnished by imprecise and incomplete results as a consequence of this imbalanced analysis methodology. To complement and refine this methodology and consequently improve the applications that are dependent on it, we add dynamic analysis of the application framework to the current research landscape and demonstrate the necessity of this move for improving the quality of prior results and advancing the field. Applying our solution, called Dynamo, to four prominent use-cases from the literature and taking a synoptical view on the results, we verify but also refute and extend the existing results of prior static analysis solutions. From the manual investigation of the root causes of discrepancies between results, we draw new insights and expert knowledge that can be valuable in improving both static and dynamic testing of the application framework

    PLATYPUS: Software-based Power Side-Channel Attacks on x86

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    Power side-channel attacks exploit variations in power consumption to extract secrets from a device, e.g., cryptographic keys. Prior attacks typically required physical access to the target device and specialized equipment such as probes and a high-resolution oscilloscope. In this paper, we present PLATYPUS attacks, which are novel software-based power side-channel attacks on Intel server, desktop, and laptop CPUs. We exploit unprivileged access to the Intel Running Average Power Limit (RAPL) interface that exposes values directly correlated with power consumption, forming a low-resolution side channel. We show that with sufficient statistical evaluation, we can observe variations in power consumption, which distinguish different instructions and different Hamming weights of operands and memory loads. This enables us to not only monitor the control flow of applications but also to infer data and extract cryptographic keys. We demonstrate how an unprivileged attacker can leak AES-NI keys from Intel SGX and the Linux kernel, break kernel address-space layout randomization (KASLR), infer secret instruction streams, and establish a timing-independent covert channel. We also present a privileged attack on mbedTLS, utilizing precise execution control to recover RSA keys from an SGX enclave. We discuss countermeasures and show that mitigating these attacks in a privileged context is not trivial

    Data Plane Cooperative Caching with Dependencies

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    Caching is at the core of most modern communication systems, where caches are used to store content and traffic classification rules. While network components can leverage caching in a cooperative manner, one important aspect of such systems concerns possible dependencies among stored items. A major use case of such dependencies appears in rule placement across software-defined networks (SDNs). Despite the tremendous success of SDNs in datacenters, their wide adoption still poses a key challenge: the packet-forwarding rules in switches require fast and power-hungry memories. Rule tables, which serve as caches, are of limited size in cheap and energy-constrained devices, motivating novel solutions to achieve high hit rates. We leverage device connectivity in the fast data plane, where delays are in the order of few milliseconds, and propose multiple switches to work together to avoid accessing the control plane, where delays are orders of magnitude greater. As a low priority rule in a cache entails caching higher priority rules, we pose the problem of cooperative caching with dependencies. We provide models and algorithms accounting for dependencies among rules implied by existing switch memory types, and lay the foundations of cooperative caching with dependencies

    RTLola on Board: Testing Real Driving Emissions on your Phone

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    This paper is about shipping runtime verification to the masses. It presents the crucial technology enabling everyday car owners to monitor the behaviour of their cars in-the-wild. Concretely, we present an Android app that deploys RTLola runtime monitors for the purpose of diagnosing automotive exhaust emissions. For this, it harvests the availability of cheap bluetooth adapters to the On-Board-Diagnostics (OBD) ports, which are ubiquitous in cars nowadays. We detail its use in the context of Real Driving Emissions (RDE) tests and report on sample runs that helped identify violations of the regulatory framework currently valid in the European Union

    Never ever or no matter what: Investigating Adoption Intentions and Misconceptions about the Corona-Warn-App in Germany

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    To help tackle the COVID-19 pandemic, the tech community has put forward proximity detection apps to help warn people who might have been exposed to the coronavirus. The privacy implications of such apps have been discussed both in academic circles and the general population. The discussion in Germany focused on the trade-off between a centralized or decentralized approach for data collection and processing and their implications. Specifically, privacy dominated the public debate about the proposed “Corona-Warn-App”. This paper presents a study with a quota sample of the German population (n = 744) to assess what the population knew about the soon-to-be-released app and their willingness to use it. We also presented participants potential properties the app could have and asked them how these would affect their usage intention. Based on our findings, we discuss our participants’ views on privacy and functionality, including their perception of selected centralized and decentralized features. We also examine a wide range of false beliefs and information that was not communicated successfully. Especially technical details, such as that the app would use Bluetooth, as opposed to location services, were unknown to many participants. Our results give insights on the complicated relationship of trust in the government and public communication on the population’s willingness to adopt the app

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