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.
Google's AI advancement is not slowing down, and we might be getting yet another powerful model codenamed "Gemini Kingfall." [...]
OpenAI is planning to ship an update to ChatGPT that will turn on the new o3 Pro model, which has more compute to think harder. [...]
OpenAI's next big foundational model is GPT-5, and the AI startup is hoping that the model will compete a little more with rivals. [...]
ChatGPT's memory feature is now better and capable of referencing past conversations for free accounts. [...]
Two malicious RubyGems packages posing as popular Fastlane CI/CD plugins redirect Telegram API requests to attacker-controlled servers to intercept and steal data. [...]