FBI, CISA and NSA have disclosed information on how multiple nation-state hacker groups targeted the network of a Defense Industrial Base.
Avast has released a decryptor for variants of the Hades ransomware known as ‘MafiaWare666’, ‘Jcrypt’, ‘RIP Lmao’, and ‘BrutusptCrypt,’ allowing victims to recover their files for free.
The security company says it discovered a flaw in the encryption scheme of the Hades strain, allowing some of the variants to be unlocked. However, this may not apply to newer or unknown samples that use a different encryption system.
Utilizing Avast’s tool, victims of the supported ransomware variants can decrypt and access their files again without paying a ransom to the attackers, which ranges between $50 and $300. However, ransom demands reached tens of thousands in some cases.
Security researchers have found a new piece of malware targeting Microsoft SQL servers. Named Maggie, the backdoor has already infected hundreds of machines all over the world.
Maggie is controlled through SQL queries that instruct it to run commands and interact with files. Its capabilities extend to brute-forcing administrator logins to other Microsoft SQL servers and doubling as a bridge head into the server’s network environment.
The backdoor was discovered by German analysts Johann Aydinbas and Axel Wauer of the DCSO CyTec. Telemetry data shows that Maggie is more prevalent in South Korea, India, Vietnam, China, Russia, Thailand, Germany, and the United States.
Microsoft has updated the mitigations for the latest Exchange zero-day vulnerabilities tracked as CVE-2022–41040 and CVE-2022–41082, also referred to ProxyNotShell.
The initial recommendations were insufficient as researchers showed that they can be easily bypassed to allow new attacks exploiting the two bugs.
Unfortunately, the current recommendations are still not enough and the proposed mitigation can still allow ProxyNotShell attacks.
Companies creating lab-grown steak, chicken, and fish see a recent White House announcement as a signal that meat grown without animal slaughter is on the cusp of being legally sold and eaten in the US.
“We are laser focused on commercial-scale production, and for us, that means moving into competing with conventional meat products in scale,” said Eric Schulze, vice president of product and regulation at Upside Foods, a cultivated meat company, as the industry calls itself. The goal is to be selling its meat on the US market within the year.
The traditional meat and poultry industry reacted strongly to President Joe Biden’s executive order last month on biotechnology and biomanufacturing, which observers say could push federal agencies to allow commercial sales of meat grown from an animal’s cells.
Algorithms have helped mathematicians perform fundamental operations for thousands of years. The ancient Egyptians created an algorithm to multiply two numbers without requiring a multiplication table, and Greek mathematician Euclid described an algorithm to compute the greatest common divisor, which is still in use today.
During the Islamic Golden Age, Persian mathematician Muhammad ibn Musa al-Khwarizmi designed new algorithms to solve linear and quadratic equations. In fact, al-Khwarizmi’s name, translated into Latin as Algoritmi, led to the term algorithm. But, despite the familiarity with algorithms today – used throughout society from classroom algebra to cutting edge scientific research – the process of discovering new algorithms is incredibly difficult, and an example of the amazing reasoning abilities of the human mind.
In our paper, published today in Nature, we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication. This sheds light on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices.
Today, Google announced the development of Imagen Video, a text-to-video AI mode capable of producing 1280×768 videos at 24 frames per second from a written prompt. Currently, it’s in a research phase, but its appearance five months after Google Imagen points to the rapid development of video synthesis models.
According to Google’s research paper, Imagen Video includes several notable stylistic abilities, such as generating videos based on the work of famous painters (the paintings of Vincent van Gogh, for example), generating 3D rotating objects while preserving object structure, and rendering text in a variety of animation styles. Google is hopeful that general-purpose video synthesis models can “significantly decrease the difficulty of high-quality content generation.”
Quantum computing and communication often rely on the entanglement of several photons together. But obtaining these multiphoton states is a bit like playing the lottery, as generating entanglement between photons only succeeds a small fraction of the time. A new experiment shows how to improve one’s odds in this quantum game of chance. The method works like an entanglement assembly line, in which entangled pairs of photons are created in successive order and combined with stored photons.
The traditional method for obtaining multiphoton entanglement requires a large set of photon sources. Each source simultaneously generates an entangled photon pair, and those photons are subsequently interfered with each other. The process is probabilistic in that each step only succeeds in producing pair entanglement, say, once in every 20 tries. The odds become exponentially worse as entanglement of more and more photons is attempted.
Christine Silberhorn from Paderborn University, Germany, and her colleagues have developed a new method that offers a relatively high success rate [1]. They use a single source that generates pairs of polarization-entangled photons in succession. After the first pair is created, one of these photons is stored in an optical loop. When the source creates a new pair (which can take several tries), one of these photons is interfered with the stored photon. If successful, this interference creates a four-photon entangled state. The process can continue—with new pairs being generated and one photon being stored—until the desired multiphoton state is reached.
The rise of quantum computing and its implications for current encryption standards are well known. But why exactly should quantum computers be especially adept at breaking encryption? The answer is a nifty bit of mathematical juggling called Shor’s algorithm. The question that still leaves is: What is it that this algorithm does that causes quantum computers to be so much better at cracking encryption? In this video, YouTuber minutephysics explains it in his traditional whiteboard cartoon style.
“Quantum computation has the potential to make it super, super easy to access encrypted data — like having a lightsaber you can use to cut through any lock or barrier, no matter how strong,” minutephysics says. “Shor’s algorithm is that lightsaber.”
According to the video, Shor’s algorithm works off the understanding that for any pair of numbers, eventually multiplying one of them by itself will reach a factor of the other number plus or minus 1. Thus you take a guess at the first number and factor it out, adding and subtracting 1, until you arrive at the second number. That would unlock the encryption (specifically RSA here, but it works on some other types) because we would then have both factors.
Three scientists who laid the groundwork for the understanding of the odd “entangling” behavior of quantum particles have received the 2022 Nobel Prize in Physics.
French physicist Alain Aspect, Austria’s Anton Zeilinger and American John Clauser were honored for their experiments exploring the nature of entangled quantum particles.