An anonymous reader writes: SafeBrowse, a Chrome extension with more than 140, 000 users, contains an embedded JavaScript library in the extension’s code that mines for the Monero cryptocurrency using users’ computers and without getting their consent. The additional code drives CPU usage through the roof, making users’ computers sluggish and hard to use. Looking at the SafeBrowse extension’s source code, anyone can easily spot the embedded Coinhive JavaScript Miner, an in-browser implementation of the CryptoNight mining algorithm used by CryptoNote-based currencies, such as Monero, Dashcoin, DarkNetCoin, and others. This is the same technology that The Pirate Bay experimented with as an alternative to showing ads on its site. The extension’s author claims he was “hacked” and the code added without his knowledge. Read more of this story at Slashdot.
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Popular Chrome Extension Embedded A CPU-Draining Cryptocurrency Miner
sciencehabit shares a report from Science Magazine: The Equifax breach is reason for concern, of course, but if a hacker wants to access your online data by simply guessing your password, you’re probably toast in less than an hour. Now, there’s more bad news: Scientists have harnessed the power of artificial intelligence (AI) to create a program that, combined with existing tools, figured more than a quarter of the passwords from a set of more than 43 million LinkedIn profiles. Researchers at Stevens Institute of Technology in Hoboken, New Jersey, started with a so-called generative adversarial network, or GAN, which comprises two artificial neural networks. A “generator” attempts to produce artificial outputs (like images) that resemble real examples (actual photos), while a “discriminator” tries to detect real from fake. They help refine each other until the generator becomes a skilled counterfeiter. The Stevens team created a GAN it called PassGAN and compared it with two versions of hashCat and one version of John the Ripper. The scientists fed each tool tens of millions of leaked passwords from a gaming site called RockYou, and asked them to generate hundreds of millions of new passwords on their own. Then they counted how many of these new passwords matched a set of leaked passwords from LinkedIn, as a measure of how successful they’d be at cracking them. On its own, PassGAN generated 12% of the passwords in the LinkedIn set, whereas its three competitors generated between 6% and 23%. But the best performance came from combining PassGAN and hashCat. Together, they were able to crack 27% of passwords in the LinkedIn set, the researchers reported this month in a draft paper posted on arXiv. Even failed passwords from PassGAN seemed pretty realistic: saddracula, santazone, coolarse18. Read more of this story at Slashdot.