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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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Google’s highest security setting for its agents runs command operations through a sandbox and throttles network access, but is still vulnerable to prompt injection. The post Vuln in Google’s Antigravity AI agent manager could escape sandbox, give attackers remote code execution appeared first on CyberScoop.

Two weeks ago, a suspected North Korean threat actor slipped malicious code into a package within Axios, a widely used JavaScript library. The immediate concern was the blast radius: roughly 100 million weekly downloads spanning enterprises, startups, and government systems. But beyond the sheer scale, the attack’s speed was just as worrisome – a stark […] The post Why the Axios attack proves AI is mandatory for supply chain security appeared first on CyberScoop.