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This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Hospital cyberattacks like the University of Mississippi Medical Center ransomware incident disrupt patient care. Ricardo Amper explains why healthcare systems are targets.

The Online Services Accord Against Scams was signed by major tech companies including Google, Microsoft and OpenAI. A coalition of Big Tech companies is working on a more comprehensive solution to combat online scams . As first reported by, Google, Microsoft, LinkedIn, Meta, Amazon, OpenAI, Adobe and Match Group announced the signing of the Online Services Accord Against Scams. The new agreement is meant to put up a united industry-wide front against online fraud and scams, particularly those from sophisticated criminal networks that use multiple platforms. According to the report, the measures will include adding fraud detection tools, introducing new user security features, and requiring more robust verification for financial transactions.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Iran-linked hackers claimed responsibility for a cyberattack on Stryker Corporation, disrupting the medical technology company's Microsoft environment and wiping devices.

The rogue AI agents appeared to act together to smuggle sensitive information out of supposedly secure cyber-systems. The rogue AI agents appeared to act together to smuggle sensitive information out of supposedly secure cyber-systems. 'Exploit every vulnerability': rogue AI agents published passwords and overrode anti-virus software Exclusive: Lab tests discover'new form of insider risk' with artificial intelligence agents engaging in autonomous, even'aggressive' behaviours Rogue artificial intelligence agents have worked together to smuggle sensitive information out of supposedly secure systems, in the latest sign cyber-defences may be overwhelmed by unforeseen scheming by AIs. With companies increasingly asking AI agents to carry out complex tasks in internal systems, the behaviour has sparked concerns that supposedly helpful technology could pose a serious inside threat. Under tests carried out by Irregular, an AI security lab that works with OpenAI and Anthropic, AIs given a simple task to create LinkedIn posts from material in a company's database dodged conventional anti-hack systems to publish sensitive password information in public without being asked to do so.

Claude AI: Why are there so many internet outages? AI chatbot Claude going down is just one example of a recent IT outage. Anthropic's Claude chatbot recently had service troubles This week, AI chatbot Claude went down, leaving users unable to access the service via its maker Anthropic's website, but barely a week goes by without a similar incident at a technology giant, government website or hospital . One of the main vulnerabilities of the modern internet is the shift to cloud computing, meaning a huge range of websites and services now rely on just a handful of companies, such as Amazon and Microsoft. In the early days of the commercial internet in the 1990s, companies used to operate their own hardware and software, a bit like individual shops in a street.

Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations.

For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? The dataset was created to evaluate the effectiveness of large language models (LLMs) in solving Capture the Flag (CTF) challenges within the domain of offensive security. There was a specific need to thoroughly assess the capabilities of LLMs in this context, as their potential for handling such tasks had not been systematically evaluated. The goal was to develop a scalable, open-source benchmark database specifically designed for these applications. This dataset includes diverse CTF challenges from popular competitions, with metadata to support LLM testing and adaptive learning. The dataset addresses a critical gap by providing a comprehensive resource for the systematic evaluation of LLMs' performance in real-world cybersecurity tasks. The development of this dataset and the accompanying automated framework allows for the continuous improvement and refinement of LLM-based approaches to vulnerability detection and resolution. By making the dataset open-source, the project aims to foster further research and development in this area, providing an ideal platform for developing, testing, and refining LLM-based approaches to cybersecurity challenges. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The students listed above compiled and validated these challenges from all previous global CSAW competitions by manually checking their setup and ensuring they remain solvable despite software changes. This work was conducted in collaboration with the OSIRIS Lab and the Center for Cybersecurity at NYU, which organize CSAW and attract global participation[1].