Challenge 1: Security audit of an LLM with garak
Tool: garak (open source, Apache-2.0) · Framework: OWASP Top 10 for LLM Apps · Time: 3–4 h
A vulnerability report for an LLM: you launch automated probes (prompt injection, data leakage, toxicity, jailbreaks) and document findings mapped to the OWASP LLM Top 10. It is the equivalent of a "pentest report," but for AI.
Where to run this: on your machine, inside the Step 0 environment. The test target is a local model with Ollama (free) or your own key with a spending limit.
Why it matters for employment
AI Security Engineer and detection platform support roles (e.g., Falcon, Sentinel) ask for "interpreting AI-generated alerts" and "reducing false positives while maintaining detection effectiveness." This challenge gives you vocabulary and concrete evidence for both.
Steps
# 1. Instala garak en tu entorno virtual
python -m pip install -U garak
# 2. (Opción gratis) Levanta un modelo local con Ollama en otra terminal
# https://ollama.com → ollama run llama3.2
# 3. Explora las sondas disponibles
garak --list_probes
# 4. Corre una auditoría enfocada (ejemplo: prompt injection)
garak --model_type ollama --model_name llama3.2 --probes promptinject
# 5. Corre una segunda familia (ejemplo: fuga de datos / toxicidad)
garak --model_type ollama --model_name llama3.2 --probes leakreplay,realtoxicityprompts
# 6. garak genera un reporte .jsonl y un resumen en consola → guárdalo en reports/
Mapping: garak probes → OWASP LLM Top 10
| garak probe (example) | OWASP LLM risk (v2025) |
|---|---|
promptinject, dan (jailbreaks) | LLM01: Prompt Injection |
leakreplay | LLM02: Sensitive Information Disclosure |
realtoxicityprompts, lmrc | LLM05: Improper Output Handling (harmful content) |
malwaregen, xss | LLM05 / LLM02 (unsafe output) |
glitch, encoding | LLM01 (obfuscated injection) |
Check the current official risk list at genai.owasp.org/llm-top-10 and probes with
garak --list_probes.
📦 Deliverable
A llm-security-audit/ repository with:
README.md— what model you audited, which probes, and 3–5 findings in business language.reports/— garak.jsonloutput (raw evidence).findings.md— table: finding → OWASP risk → severity → proposed mitigation.- A simple diagram of the audit flow.
✅ Success criteria
- Ran at least 2 distinct probe families.
- Each finding is mapped to an OWASP LLM Top 10 ID.
- Proposed one mitigation per finding (not just a description).
- The README is understandable by a non-technical hiring manager in 2 minutes.
Audit only your own models or models where you have explicit permission. Never run garak against a third-party production system without written authorization.