Lab — Poisoning
Pack data poisoning, adversarial ML, and supply chain cards — poisoning literacy file from $AI_LAB only.
Visual · t32_poisoning_lab
Lab: poisoning pack. $AI_LAB only. Original Cyberlium.
Poisoning pack merges data poison to adversarial to supply chain — paperwork before AI threats module.
Lessons 4-1–4-3 named data poisoning, adversarial ML, and model supply chain. This lab merges three sections into one $AI_LAB poisoning artifact with M1 ethics cross-reference. No stranger pipeline sabotage — poisoning literacy paperwork only. Next: Quiz — Poisoning and Adversarial.
1. Lab contract: poisoning and adversarial pack
Create $HOME/cyberlium-lab/t32-m04-l04-poisoning-lab.txt merging data poisoning, adversarial ML, and supply chain sections with $AI_LAB toy stack name and M1 ethics cross-reference.
Optional: one fictional poisoned sample description — paper only, no upload to public hubs.
Command guide
Try these commands — Lab contract: poisoning and adversarial pack
═══ 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: grep, python3. Reference sites: OWASP LLM03 (https://owasp.org/www-project-top-10-for-large-language-model-applications/); MITRE ATLAS (https://atlas.mitre.org/); NIST AI RMF (https://www.nist.gov/itl/ai-risk-management-framework). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.
2. Cross-check ethics
Grep for steps targeting stranger training pipelines, public hub upload commands, adversarial fuzz scripts against prod APIs — remove. Pack labels all work as $AI_LAB training only.
Confirm no actionable sabotage recipes against third-party models.
3. Lock the proof
chmod 600 on the pack. Quiz next — then AI Phishing Named.
Poisoning literacy feeds AI-powered threat lessons in M5.
4. What you ship: poisoning and adversarial pack for $AI_LAB
Merged poisoning, adversarial, supply chain sections. $AI_LAB named. NO pipeline sabotage. chmod 600.
5. What you record before the next lesson
Date. Poisoning pack path. $AI_LAB named. File t32-m04-l04-poisoning-lab.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: Include stranger hub poison upload steps. Skip supply chain section.
- Right: Write YOUR poisoning pack for $AI_LAB. chmod 600. Next: Quiz — Poisoning and Adversarial.
Mission: freeze YOUR poisoning pack on disk
1) Merge M4 literacy sections. 2) Confirm zero stranger sabotage refs. 3) Link M1 ethics cross-ref. 4) chmod 600.
Stuck? Ask Cyberlium AI Mentor
One governed poisoning pack beats ten public hub experiment logs without scope.
APPLY: This lab requires:
APPLY: True or False: Poisoning pack must label scenarios as $AI_LAB training only.
APPLY: Data poisoning section feeds: