In June 2014, Google announced Android One, a set of "hardware reference models" that would "allow [device makers] to easily create high-quality phones at low costs", designed for consumers in developing countries.[52][53][54] In September, Google announced the first set of Android One phones for release in India.[55][56] However, Recode reported in June 2015 that the project was "a disappointment", citing "reluctant consumers and manufacturing partners" and "misfires from the search company that has never quite cracked hardware".[57] Plans to relaunch Android One surfaced in August 2015,[58] with Africa announced as the next location for the program a week later.[59][60] A report from The Information in January 2017 stated that Google is expanding its low-cost Android One program into the United States, although The Verge notes that the company will presumably not produce the actual devices itself.[61][62] Google introduced the Pixel and Pixel XL smartphones in October 2016, marketed as being the first phones made by Google,[63][64] and exclusively featured certain software features, such as the Google Assistant, before wider rollout.[65][66] The Pixel phones replaced the Nexus series,[67] with a new generation of Pixel phones launched in October 2017.[68]
Modern Combat 4 Ipa Cracked 21
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On October 8, 2018, Google announced new Google Play store requirements to combat over-sharing of potentially sensitive information, including call and text logs. The issue stems from the fact that many apps request permissions to access users' personal information (even if this information is not needed for the app to function) and some users unquestionably grant these permissions. Alternatively, a permission might be listed in the app manifest as required (as opposed to optional) and the app would not install unless user grants the permission; users can withdraw any, even required, permissions from any app in the device settings after app installation, but few users do this. Google promised to work with developers and create exceptions if their apps require Phone or SMS permissions for "core app functionality". The new policies enforcement started on January 6, 2019, 90 days after policy announcement on October 8, 2018. Furthermore, Google announced a new "target API level requirement" (targetSdkVersion in manifest) at least Android 8.0 (API level 26) for all new apps and app updates. The API level requirement might combat the practice of app developers bypassing some permission screens by specifying early Android versions that had a coarser permission model.[299][300]
The ability to execute code in an emulator is a fundamental part of modern vulnerability testing. Unfortunately, this poses a challenge for many embedded systems, where firmware expects to interact with hardware devices specific to the target. Getting embedded system firmware to run outside its native environment, termed rehosting, requires emulating these hardware devices with enough accuracy to convince the firmware that it is executing on the target hardware. However, full fidelity emulation of target devices (which requires considerable engineering effort) may not be necessary to boot the firmware to a point of interest for an analyst (for example, a point where fuzzer input can be injected). We hypothesized that, for the firmware to boot successfully, it is sufficient to emulate only the behavior expected by the firmware, and that this behavior could be inferred automatically.
We introduce a new timing side-channel attack on Intel CPU processors. Our Frontal attack exploits timing differences that arise from how the CPU frontend fetches and processes instructions while being interrupted. In particular, we observe that in modern Intel CPUs, some instructions' execution times will depend on which operations precede and succeed them, and on their virtual addresses. Unlike previous attacks that could only profile branches if they contained different code or had known branch targets, the Frontal attack allows the adversary to distinguish between instruction-wise identical branches. As the attack requires OS capabilities to set the interrupts, we use it to exploit SGX enclaves. Our attack further demonstrates that secret-dependent branches should not be used even alongside defenses to current controlled-channel attacks. We show that the adversary can use the Frontal attack to extract a secret from an SGX enclave if that secret was used as a branching condition for two instruction-wise identical branches. We successfully tested the attack on all the available Intel CPUs with SGX (until 10th gen) and used it to leak information from two commonly used cryptographic libraries.
Using a number of novel insights, we overcome these challenges to build SMASH (Synchronized MAny-Sided Hammering), a technique to succesfully trigger Rowhammer bit flips from JavaScript on modern DDR4 systems. To mount effective attacks, SMASH exploits high-level knowledge of cache replacement policies to generate optimal access patterns for eviction-based many-sided Rowhammer. To lift the requirement for large physically-contiguous memory regions, SMASH decomposes n-sided Rowhammer into multiple double-sided pairs, which we can identify using slice coloring. Finally, to bypass the in-DRAM TRR mitigations, SMASH carefully schedules cache hits and misses to successfully trigger synchronized many-sided Rowhammer bit flips. We showcase SMASH with an end-to-end JavaScript exploit which can fully compromise the Firefox browser in 15 minutes on average.
Code autocompletion is an integral feature of modern code editors and IDEs. The latest generation of autocompleters uses neural language models, trained on public open-source code repositories, to suggest likely (not just statically feasible) completions given the current context.
To combat concept drift, we present a novel system CADE aiming to 1) detect drifting samples that deviate from existing classes, and 2) provide explanations to reason the detected drift. Unlike traditional approaches (that require a large number of new labels to determine concept drift statistically), we aim to identify individual drifting samples as they arrive. Recognizing the challenges introduced by the high-dimensional outlier space, we propose to map the data samples into a low-dimensional space and automatically learn a distance function to measure the dissimilarity between samples. Using contrastive learning, we can take full advantage of existing labels in the training dataset to learn how to compare and contrast pairs of samples. To reason the meaning of the detected drift, we develop a distance-based explanation method. We show that explaining "distance" is much more effective than traditional methods that focus on explaining a "decision boundary" in this problem context. We evaluate CADE with two case studies: Android malware classification and network intrusion detection. We further work with a security company to test CADE on its malware database. Our results show that CADE can effectively detect drifting samples and provide semantically meaningful explanations.
Client-side CSRF is a new type of CSRF vulnerability where the adversary can trick the client-side JavaScript program to send a forged HTTP request to a vulnerable target site by modifying the program's input parameters. We have little-to-no knowledge of this new vulnerability, and exploratory security evaluations of JavaScript-based web applications are impeded by the scarcity of reliable and scalable testing techniques. This paper presents JAW, a framework that enables the analysis of modern web applications against client-side CSRF leveraging declarative traversals on hybrid property graphs, a canonical, hybrid model for JavaScript programs. We use JAW to evaluate the prevalence of client-side CSRF vulnerabilities among all (i.e., 106) web applications from the Bitnami catalog, covering over 228M lines of JavaScript code. Our approach uncovers 12,701 forgeable client-side requests affecting 87 web applications in total. For 203 forgeable requests, we successfully created client-side CSRF exploits against seven web applications that can execute arbitrary server-side state-changing operations or enable cross-site scripting and SQL injection, that are not reachable via the classical attack vectors. Finally, we analyzed the forgeable requests and identified 25 request templates, highlighting the fields that can be manipulated and the type of manipulation.
A hypervisor (also know as virtual machine monitor, VMM) enforces the security boundaries between different virtual machines (VMs) running on the same physical machine. A malicious user who is able to run her own kernel on a cloud VM can interact with a large variety of attack surfaces. Exploiting a software fault in any of these surfaces leads to full access to all other VMs that are co-located on the same host. Hence, the efficient detection of hypervisor vulnerabilities is crucial for the security of the modern cloud infrastructure. Recent work showed that blind fuzzing is the most efficient approach to identify security issues in hypervisors, mainly due to an outstandingly high test throughput.
Log is a key enabler of many security applications including but not limited to security auditing and forensic analysis. Due to the rapid growth of modern computing infrastructure size, software systems are generating more and more logs every day. Moreover, the duration of recent cyber attacks like Advanced Persistent Threats (APTs) is becoming longer, and their targets consist of many connected organizations instead of a single one. This requires the analysis on logs from different sources and long time periods. Storing such large sized log files is becoming more important and also challenging than ever. Existing logging systems are either inefficient (i.e., high storage overhead) or designed for limited security applications (i.e., no support for general security analysis). In this paper, we propose ELISE, a storage efficient logging system built on top of a novel lossless data compression technique, which naturally supports all types of security analysis. It features lossless log compression using a novel log file preprocessing and Deep Neural Network (DNN) based method to learn optimal character encoding. On average, ELISE can achieve 3 and 2 times better compression results compared with existing state-of-the-art methods Gzip and DeepZip, respectively, showing a promising future research direction. 2ff7e9595c
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