Smartphones now run powerful on-device AI that learns from your behavior—and that means new risk. In this intermediate course, you’ll learn how AI turns phones into active attack surfaces and how adversaries weaponize deepfakes, side-channel inference, and mobile LLM agents. Through short, focused videos and scenario-based discussions, you’ll see exactly how zero-permission sensors and cache traces reveal activity, how overlays and prompt injection hijack agents, and why “permissions” alone don’t ensure privacy. Then you’ll turn knowledge into action: baseline telemetry, write simple detection rules, verify links and intents, quarantine devices, rotate tokens, and draft a one-page SOP. AI-graded labs provide hands-on practice, and a capstone project ties everything together. By the end, you can detect, respond, and harden against AI-driven mobile threats—skills you can apply immediately at home or in an enterprise.

Detect & Respond to Mobile AI Threats
访问权限由 New York State Department of Labor 提供
您将学到什么
Analyze how AI features like sensors, models, and agents make phones attack surfaces and enable deepfake-based scams.
Evaluate technical attack paths—zero-permission inference and multi-layer agent attacks—using real research cases.
Design a mobile-focused detection and response plan with simple rules, containment steps, and key resilience controls.
您将获得的技能
要了解的详细信息

添加到您的领英档案
1 项作业
December 2025
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- 通过实践项目培养工作相关技能
- 获得可共享的职业证书

该课程共有3个模块
This module sets the mental model for how AI embeds across the phone—keyboards, cameras, sensors, and agents—and why that expands risk. Learners examine deepfake social engineering and zero-permission inference attacks that leak behavior. They connect people/model/GUI/system layers to real incidents. A short intro activity builds intuition before deeper technical work.
涵盖的内容
4个视频2篇阅读材料1次同伴评审
This module examines the mechanics of AI-powered mobile exploits, from zero-permission sensor inference to multi-layer AI agent hijacking. Learners study real research cases, explore how deep learning amplifies attacks, and analyze adversarial examples and AI-enabled malware. The focus is on understanding how technical threats operate in practice, preparing learners for hands-on detection in the next module.
涵盖的内容
3个视频1篇阅读材料1次同伴评审
This module converts theory into practice by focusing on detection signals, response steps, and resilience controls. Learners design telemetry rules, run an incident response simulation, and propose hardening measures such as allow-lists, verified links, and attestation. The goal is to build practical readiness against mobile AI-driven threats.
涵盖的内容
4个视频1篇阅读材料1个作业2次同伴评审
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