The question was deceptively simple. Could the light that is used to form an image on a display also be converted into something that can be felt? At the University of California - Santa Barbara, a team of researchers spent nearly a year exploring this idea, working through theoretical models, conducting simulations, and eventually building prototypes. Their work, described in the paper Tactile Displays Driven by Projected Light and explored in TechXplore, has led to a significant breakthroug
machinelearning (25)
Artificial intelligence has become the most disruptive technology in cybersecurity. It is transforming how defenders detect threats, how attackers build new tools, and how organizations must redesign their entire security strategy. In 2025, AI is no longer an enhancement to security systems. It has become the core engine behind both cyber defense and cyber offense. This shift brings opportunities, challenges, and new responsibilities for every security leader.[1]
AI is revolutionizing how def
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
After years of quiet escalation, business leaders are finally beginning to grasp just how serious the threat of fraud has become. Today, nearly half of all fraud attempts (41%) now involve artificial intelligence. Nowhere is this more evident than in the payments industry. Fraudsters can use AI to generate convincing fake invoices, purchase orders, and payment instructions that mirror legitimate business documents. I’ve seen examples that are indistinguishable from the real thing, which is a
After years of quiet escalation, business leaders are finally beginning to grasp just how serious the threat of fraud has become. Today, almost half of all fraud attempts (41%) involve artificial intelligence. Nowhere is this more evident than in the payments industry. Fraudsters can use AI to generate convincing fake invoices, purchase orders, and payment instructions that mirror legitimate business documents. I’ve seen examples that are indistinguishable from the real thing, which is a tre
For over ten years, computer scientist Randy Goebel and his colleagues in Japan have been quietly conducting one of the most revealing experiments in artificial intelligence —a legal reasoning competition based on the Japanese bar exam. The challenge is to have AI systems retrieve relevant laws and then answer the core question at the heart of every legal case of whether the law was broken or not. That yes/no decision, it turns out, is where AI stumbles hardest. This struggle has profound impl
Our colleagues at Sentinel Labs have provided yet another great research and analysis. As Large Language Models (LLMs) are increasingly incorporated into software‑development workflows, they also have the potential to become powerful new tools for adversaries; as defenders, it is important that we understand the implications of their use and how that use affects the dynamics of the security space.
In Sentinel’s research, they wanted to understand how LLMs are being used and how analysts could s
The cybersecurity company ESET has disclosed that it discovered an artificial intelligence (AI)-powered ransomware variant codenamed PromptLock. Written in Golang, the newly identified strain uses the gpt-oss:20b model from OpenAI locally via the Ollama API to generate malicious Lua scripts in real-time. The open-weight language model was released by OpenAI earlier this month. "PromptLock leverages Lua scripts generated from hard-coded prompts to enumerate the local filesystem, inspect target
A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request. Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions. Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning. Rather than directly asking the model to generate in
A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request. Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions. Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning. Rather than directly asking the model to generate in
A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request. Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions. Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning. Rather than directly asking the model to generate in
A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request. Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions. Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning. Rather than directly asking the model to generate in
