Google Chrome to use on-device AI to detect tech support scams
Google is implementing a new Chrome security feature that uses the built-in 'Gemini Nano' large-language model (LLM) to detect and block tech support scams while browsing the web. [...]
Scams use deception to steal money, credentials, or sensitive data, making them a cybersecurity risk for individuals and organizations.
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Background for this topic.
Scams are deceptive schemes intended to make people surrender money, credentials, sensitive information, or access. In information security, they commonly use phishing messages, impersonation, fraudulent websites, business-email compromise, fake technical support, or malicious attachments. Their defining feature is manipulation: the attacker creates a credible pretext and pressures the target to act before verifying the request.
Security teams should treat scams as an attack surface spanning email, messaging, telephone calls, social media, and payment workflows. Material risks include account takeover through stolen credentials, unauthorized payments, disclosure of personal or company data, and malware execution from deceptive content. Useful controls include phishing-resistant authentication, secure payment-change procedures with independent verification, filtering and domain protections, user training focused on reporting, and rapid review of suspicious messages or transactions. Incident handling may require revoking sessions, resetting credentials, contacting financial institutions, preserving evidence, and notifying affected parties where applicable.
Google is implementing a new Chrome security feature that uses the built-in 'Gemini Nano' large-language model (LLM) to detect and block tech support scams while browsing the web. [...]