Lab Path

AI Security literacy path on `$AI_LAB` — lab setup to mentor handoff with ethics gates at every station.

Lab Path• Audio Walkthrough

00:00/02:56

Visual · t32_lab_path

Lab path = visual AI security chain on YOUR $AI_LAB. Original Cyberlium.

Draw the path from lab scope to mentor handoff — with stop signs at every jailbreak kit shortcut and stranger app test lane.

Document literacy path: `$AI_LAB` setup → LLM basics → injection → poisoning/threats → LLM app security → secure dev → governance/red team → findings/disclosure → capstone. Ethics gates: jailbreak weaponization, stranger app testing, fraud deepfake kits, prod LLM without RoE, public bypass publication — refused paths labeled red. UTC milestones and artifact IDs per station. Mermaid or sketch chmod 600. Honest N/A if brief skipped live eval — labeled. Defender breakpoint: where RoE or YOUR apps discipline prevented out-of-scope test.

1. Path stations

Setup, LLM basics, injection, poisoning, threats, LLM apps, secure dev, governance, findings, handoff — ten minimum stations.

Each station links evidence index row and module lesson ID.

Command guide

Try these commands — Path stations

═══ LINUX / macOS (Terminal Practice) ═══

Check system state and user context

Command — copy this

id
whoami
uname -a

Inspect network sockets listening for connections

Command — copy this

ss -tuln 2>/dev/null || netstat -tuln

Audit active processes

Command — copy this

ps aux | grep -v "\[" | head -15

═══ WINDOWS (POWERSHELL) ═══ Query user identity and system information

Command — copy this

whoami /all
Get-ComputerInfo | Select-Object CsName, OsName, OsVersion

Primary tools to practice this lesson: curl, grep. 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); OpenAI safety (https://openai.com/safety). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.