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Latest coverage for Artificial Intelligence

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.

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A high-severity security flaw has been disclosed in Meta's Llama large language model (LLM) framework that, if successfully exploited, could allow an attacker to execute arbitrary code on the llama-stack inference server.  The vulnerability, tracked as CVE-2024-50050, has been assigned a CVSS score of 6.3 out of 10.0. Supply chain security firm Snyk, on the other hand, has assigned it a

Every week seems to bring news of another data breach, and it’s no surprise why: securing sensitive data has become harder than ever. And it’s not just because companies are dealing with orders of magnitude more data. Data flows and user roles are constantly shifting, and data is stored across multiple technologies and cloud environments. Not to mention, compliance requirements are only getting