AI › Module 1 › Lesson 1
Why AI Sec
AI security literacy means named risks, defender vocabulary, and lab ethics on YOUR $AI_LAB — not jailbreak weaponization against stranger chatbots or malware-generation cookbooks.
Visual · t32_why_ai_sec
AI scope literacy. $AI_LAB only. Original Cyberlium.
Opening
LLMs change the threat surface — Cyberlium teaches defender vocabulary and lab ethics on toys you own, not jailbreak scripts against production apps or AI malware recipe drops.
AI security spans prompt injection, data poisoning, model supply chain, output handling, and AI-powered social engineering. Analysts need vocabulary to read OWASP LLM findings, red-team reports, and vendor advisories — not to publish jailbreak kits against stranger chatbots or deepfake fraud playbooks. Cyberlium Topic 32 teaches on $AI_LAB — YOUR personal toy chatbot, courseware notes, and self-authored risk cards under $HOME/cyberlium-lab/t32-ai/. You will name AI risk classes and lab boundaries — never stranger app jailbreaks or weaponized AI cookbooks. Next: Lab Toys Only.
1. What AI security covers (named)
AI security includes prompt injection, training-data poisoning, adversarial inputs, model supply chain integrity, secrets in prompts, unsafe tool use, and AI-assisted phishing or deepfake detection. One misconfigured RAG pipeline or unfiltered tool call can leak data or execute unintended actions.
Literacy means you can name these domains when reading an OWASP LLM report or job description — not that you can jailbreak any production chatbot you find online.
Command guide
Try these commands — What AI security covers (named)
═══ TOOLS & WEBSITES ═══ Browse / read these (authorized learning only — stay in YOUR lab / program scope)
OWASP LLM Top 10 — https://owasp.org/www-project-top-10-for-large-language-model-applications/ (AI risk literacy) NIST AI RMF — https://www.nist.gov/itl/ai-risk-management-framework (governance context) MITRE ATLAS — https://atlas.mitre.org/ (AI threat taxonomy literacy) CISA AI — https://www.cisa.gov/ai (national AI security guidance)
═══ INSTALL ═══
Linux (Debian/Ubuntu):
Command — copy this
sudo apt install python3 sudo apt install curl
macOS:
Command — copy this
brew install python3
Windows: Download https://python.org/downloads/ Built-in (PowerShell: Invoke-WebRequest)
═══ LINUX / macOS ═══
Command — copy this
python3 -c "print('AI Security literacy: YOUR toys + $HOME/cyberlium-lab/t32-ai/ only')"
curl -sS https://owasp.org/www-project-top-10-for-large-language-model-applications/ | head -10
curl -sS https://www.nist.gov/itl/ai-risk-management-framework | head -8Primary tools to practice this lesson: python3, curl. Reference sites: OWASP LLM Top 10 (https://owasp.org/www-project-top-10-for-large-language-model-applications/); NIST AI RMF (https://www.nist.gov/itl/ai-risk-management-framework); MITRE ATLAS (https://atlas.mitre.org/); CISA AI (https://www.cisa.gov/ai). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.
2. Who needs AI security vocabulary
App developers harden LLM integrations before production. Security analysts triage AI abuse alerts and model-risk findings. Students practice scope and ethics on personal lab toys before touching employer AI stacks.
Cyberlium assumes YOU practice on $AI_LAB — personal toy chatbot, labeled courseware samples, self-authored risk notes — not employer production without ticket scope or stranger chatbot jailbreak targets.
3. What this topic will never call practice
Jailbreaking stranger production chatbots, sharing live jailbreak prompts in public chat, malware-generation recipes, deepfake fraud kits, poisoning stranger training pipelines, or weaponized red-team playbooks without written scope.
Ship a sentence: Topic 32 here means AI risk literacy and defenses on MY $AI_LAB toys only. Next lesson: Lab Toys Only.
4. What you ship: AI topic scope scoped to $AI_LAB literacy
Write literacy vs unauthorized AI abuse in one paragraph. Dest = $AI_LAB toy chatbot and notes. NEVER stranger app jailbreaks. Notes chmod 600.
5. What you record before the next lesson
Date (UTC). Topic scope. Lab = $AI_LAB. NEVER stranger jailbreaks or weaponized AI cookbooks. Path: $HOME/cyberlium-lab/t32-m01-l01-why-ai-sec.txt chmod 600.
6. Wrong vs right: stranger SaaS vs YOUR toy LLM
Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.
Wrong
Jailbreak a stranger chatbot 'for AI learning.' Treat Topic 32 as a free pass to share malware-generation prompts.
Right
Define AI security literacy and name $AI_LAB as the only practice surface. Next: Lab Toys Only.
Mission: define Topic 32 for YOUR AI lab
1) Write literacy vs unauthorized AI abuse in one paragraph each. 2) Write a NEVER list (stranger jailbreaks, malware recipes, deepfake fraud kits). 3) Name $AI_LAB as your placeholder. Never aim AI abuse tools at apps outside your scoped lab.
Stuck? Ask Cyberlium AI Mentor
Ask Mentor: 'Hint only: what is prompt injection?' — not how to jailbreak a production chatbot you do not own.
Knowledge Check
APPLY: AI security on Cyberlium means:
Multiple choice
Knowledge Check
APPLY: True or False: Topic 32 includes jailbreak weaponization against stranger production apps.
True or False
Knowledge Check
APPLY: Primary output of this topic supports:
Multiple choice