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 newly disclosed Linux kernel flaw called Bad Epoll (CVE-2026-46242) lets an ordinary user with no special access take full control of a machine as root. It affects Linux desktops, servers, and Android, and a fix is out
A high-severity security flaw in Langflow, an open-source low-code platform to build artificial intelligence (AI) applications, has come under active exploitation in the wild, according to findings from VulnCheck
Redis has patched a use-after-free in its blocking-client code that lets an authenticated user run arbitrary OS commands on the machine hosting the database. The flaw was found by an autonomous AI tool built to hunt bugs in large codebases
A newly disclosed security flaw impacting NGINX Plus and NGINX Open has come under active exploitation in the wild, days after its public disclosure, according to VulnCheck
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