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.
OpenAI has reported a surge in performance as GPT-5.1-Codex-Max reaching 76% in capability assessments, and warned of upcoming cyber-risks
The flaw, dubbed ‘GeminiJack,’ exploits the trust boundary between user-controlled content in data sources and the AI model’s instruction processing
Two malicious Visual Studio Code extensions, Bitcoin Black and Codo AI, have been observed harvesting sensitive user data
Gartner has called for organizations to block today’s AI browsers on security concerns