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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Background for this topic.
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.
YouTube warns that scammers are using an AI-generated video featuring the company's CEO in phishing attacks to steal creators' credentials. [...]
Google has announced an increased rollout of new AI-powered scam detection features on Android to help protect users from increasingly sophisticated phone and text social engineering scams. [...]
Close to 12,000 valid secrets that include API keys and passwords have been found in the Common Crawl dataset used for training multiple artificial intelligence models. [...]