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.
Pacific Northwest National Labs' expert cybersecurity system, ALOHA, can recreate attacks and test them against organizations' infrastructure to bolster defense.
ServiceNow tacked agentic AI onto a largely unguarded legacy chatbot, exposing customers' data and connected systems.
Researchers detailed a souped-up version of the GoBruteforcer botnet that preys on servers with weak credentials and AI-generated configurations.
A total of 91,403 sessions targeted public LLM endpoints to find leaks in organizations' use of AI and map an expanding attack surface.