Explore the intersection of AI and cybersecurity. Stay informed on AI-driven security trends, tools, and threats in the ever-evolving digital landscape.
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Artificial intelligence (AI) describes computer systems that perform tasks such as recognizing patterns, making predictions, understanding language, or generating content. In security reporting, the term commonly includes machine-learning models used for detection and analysis, as well as generative AI applications that produce text, code, images, or other outputs.
AI can help analyze security telemetry, prioritize vulnerabilities, and support investigations, but its outputs can be wrong or manipulated. Important attack surfaces include prompt injection that steers an application into unintended actions, sensitive data being exposed through prompts or model outputs, and excessive permissions granted to AI systems that use external tools. Models can also be degraded by poisoned training data or evaded with carefully crafted inputs. Practitioners should protect training and operational data, limit model access and tool permissions, test for adversarial behavior, and require appropriate human validation before high-impact decisions.
The findings come from a new report released by Microsoft Threat Analysis Center on Thursday
Sometimes using AI to make hilariously wrong images that still drive social media engagement Microsoft, which earlier this week admitted not being able to detect a Chinese attack on its own infrastructure, has published a report [PDF] titled "Digital threats from East Asia increase in breadth and effectiveness." In the report, Redmond's Threat Intelligence group expounds on its fresh insight into evolving online aggressions from both China and North Korea.…
Deepfake videos and audio of NFL players and phishing communications via ChatGPT-like tools are a worry, the NFL's CISO says.
The Traceable report is based on insights from 1629 cybersecurity experts across the US, UK and EU
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Putting the top 10 generative AI tools to the ethical test reveals more about humanity than artificial intelligence.
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We analyze an information stealer written in Node.js, packaged into an executable, exfiltrated stolen data via both Telegram bot API and a C&C server, and employed GraphQL as a channel for C&C communication.
Realistic expectations and caution began to replace wonder and confusion for generative AI at the recent security industry gatherings.
There’s been a great deal of AI hype recently, but that doesn’t mean the robots are here to replace us. This article sets the record straight and explains how businesses should approach AI