Unlimited, or so-called helpful-only language models are trained without safety alignment constraints and never refuse user queries. They are widely used by leading AI companies as internal tools for red teaming and alignment evaluation. For example, if a safety-aligned model produces harmful outputs similar to an unlimited model, this indicates alignment failures that require further attention. Despite their essential role in assessing alignment, such models are not available to the research community. We introduce Jinx, a helpful-only variant of popular open-weight LLMs. Jinx responds to all queries without refusals or safety filtering, while preserving the base model's capabilities in reasoning and instruction following. It provides researchers with an accessible tool for probing alignment failures, evaluating safety boundaries, and systematically studying failure modes in language model safety.

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days.

Digital investigators often get involved with cases which seemingly point the responsibility to the person to which the computer belongs, but after a thorough examination malware is proven to be the cause, causing loss of precious time. Whilst Anti-Virus (AV) software can assist the investigator in identifying the presence of malware, with the increase in zero-day attacks and errors that exist in AV tools, this is something that cannot be relied upon. The aim of this paper is to investigate the behavior of malware upon various Windows operating system versions in order to determine and correlate the relationship between malicious software and OS artifacts. This will enable an investigator to be more efficient in identifying the presence of new malware and provide a starting point for further investigation. The study analyzed several versions of the Windows operating systems (Windows 7, 8.1 and 10) and monitored the interaction of 90 samples of malware across three categories of the most prevalent (Trojan, Worm, and Bot) and 90 benign samples through the Windows Registry.

Imagine getting to a courthouse and seeing paper signs stuck to the doors with the message "Systems down." What about police officers in the field unable to access information on laptops in their vehicles, or surgeries delayed in hospitals? That's what can happen to a city, police department, or hospital in a ransomware attack. Ransomware is malicious software that can encrypt or control computer systems. Criminals who launch these attacks can then refuse to return access until they get paid.

You shouldn't let anyone see you enter your phone's login password -- but there could also be a danger from hackers hearing it over your smartphone's microphone. Experts from England and Sweden have shown how hacked microphones can be used to decode the sound of typing on a smartphone screen into the keys pressed. In a test, their algorithm could correctly guess 31 out of 50 four-digit login pins in just 10 attempts based on recordings made of the participants as they typed. These potential attacks would likely begin with the accidental download of malicious software -- so users should keep themselves safe by only using trusted apps. Limiting microphone access to only those apps that need it will also help to make your smartphone more secure.

A study by Trend Micro researchers showed that more than 83 percent of all downloaded software files are unknown or unclassified, even two years after they were first observed in the wild. And because most malware threats come from software download events, they subsequently developed a human-readable machine learning system that successfully classifies unknown files into either benign or malicious in nature. The study involved a dataset of 3 million anonymized web-based software download events gathered in a seven-month period. These events were studied and analyzed using multiple sources of ground truth both from internal and proprietary Trend Micro systems and publicly available ones. However, less than 17 percent of the dataset were labeled using traditional means.

Secure is a weekly column that dives into the rapidly escalating topic of cybersecurity. Regardless of how much money is poured into increasing cybersecurity, the situation doesn't seem to improve. Could machine learning that adapts to new methods be the solution? For some companies, such machine learning techniques go hand in hand with more traditional signature based detection and others employ behavioral learning to watch out for other, unspecified threats. For threat prevention company Cylance, however, machine learning is all it needs to offer what it claims is the most effective anti-malware solution available today.

Parents are outraged after their children were forced to view offensive ads as they played what they thought were harmless puzzle game apps. The ads were promoting products for e-commerce platform Wish.com on popular game apps like Crazy Cake Swap and 2040. One bizarre ad shows a penis extender strap, while another displays a woman with an unzipped cat suit where her butt is partially exposed. UK ad watchdog Advertising Standards Authority (ASA) has reprimanded US e-commerce site Wish.com UK ad watchdog Advertising Standards Authority (ASA) has contacted Wish.com about the ads, but the firm has yet to respond or remove the ads.

Aided by machine learning, we analyzed data on 3 million software downloads from hundreds of thousands of internet-connected machines. We looked into the major domains from where different malware categories were downloaded and discussed which client applications were mostly targeted by malware infection. We also looked at code signing abuse and examined certain certification authorities that were found with certificates that were used for signing malicious code. In this blog post, we will discuss how we developed a human-readable machine learning system that is able to determine whether a downloaded file is benign or malicious in nature. The development of this actionable intelligent system stemmed from the question: How can we make our knowledge about global software download events actionable?

Robots could mimic writing styles and habits of millions of people to launch devastating scams, cyber security experts have warned. Hackers could use AI programmes to impersonate individuals after malicious software harvests records and emails from their computers. Such a scam could'explode' as colleagues and contacts are tricked into opening files and infecting their own systems. A House of Lords committee has also been told that criminal gangs could deploy AI to sift masses of material collected from hacked devices such as smart TVs at companies - and work out what can intelligence can be used to make money. The potential for organised gangs to'scale up' their activities using developments in artificial intelligence was spelled out in evidence to peers by Cambridge-based cyber security experts Darktrace The potential for organised gangs to'scale up' their activities using developments in artificial intelligence was spelled out in evidence to peers by respected Cambridge-based cyber security experts Darktrace.