AI Security Vulnerability

An AI security vulnerability refers to weaknesses in AI systems that can be exploited to cause unauthorized access, data breaches, model manipulation, or system failures. These vulnerabilities span multiple layers including training data poisoning, model extraction attacks, adversarial inputs, and infrastructure misconfigurations. For enterprises, identifying and patching AI security vulnerabilities is critical to protecting sensitive data, ensuring model integrity, and maintaining regulatory compliance with data protection frameworks.

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ResearchGlobal2026-08-18

CoSnitch Vulnerability Turns Microsoft Copilot Into a Silent Data Exfiltration Tool

Varonis Threat Labs disclosed a vulnerability in Microsoft Copilot Personal, dubbed CoSnitch, that allows attackers to craft a malicious URL triggering silent prompt execution inside an authenticated user session. Exploiting undocumented URL parameters revealed through a technique called meta-hacking, the attack can exfiltrate emails, files, and credentials through OAuth connectors to Gmail, Google Drive, and other integrated services. The finding directly implicates enterprise controls around third-party AI tool vetting, prompt injection defense, and the governance of AI systems with access to sensitive data through connected integrations.

Microsoft Copilot prompt injection data exfiltration OAuth security AI security vulnerability Microsoft third-party AI risk