enSilo to Discover Turning Tables Windows Exploit

Researchers claim that using this exploit any Windows version can be hacked
21 August 2018   1811

Specialists from enSilo Omri Misgav and Udi Yavo said they developed a new Windows hacking technique called Turning Tables. The exploit uses the page table of the system and allows to bypass all kernel protection mechanisms.

The system page table contains data that matches the contents of the virtual and physical memory. Some programs can use the same parts of the code, and in order to optimize the load, different programms can use the same page. The essence of the Turning Tables technique is to change the code on these pages in such a way as to change the behavior of processes with high priority.

Turning Tables
Turning Tables

Using the discovered exploit, an attacker can increase the priority of his own process. Moreover, the vulnerability can be used to attack the processes running in the sandbox.

Since the principle of shared pages, in addition to Windows, is also used in macOS and Linux, in theory, these operating systems may be vulnerable.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   170

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.