Project Guide | AI Security Redteam Lab

Live Demo

Open the public GitHub Pages demo
Scope: credential-free, synthetic-data demo for security teams and technical evaluators.

Self-contained test harness for prompt-injection, secret-leakage, and unsafe tool-use scenarios. The lab uses deterministic policy checks and synthetic cases so it can run in CI without external services.

System Overview

A credential-free AI security lab that turns abstract model risk into CI-friendly tests teams can actually run.

Area	Details
Users	AI platform teams, security engineers, product teams shipping AI features, and governance teams.
Technical path	Validate the demo, README, architecture notes, and quality gate before deeper workflow review.
System scope	Prompt injection, secret leakage, unsafe tool use checks, deterministic fixtures, and reportable safety outputs.
Operating boundary	Credential-free by design; extend with customer-specific policies only after scoping and approval.
Evaluation path	Run the safety checks locally and inspect generated reports and failing-case examples.

Evaluation Path

Start here: Start with the policy categories, then open the generated Markdown report.
Local demo: Run python3 scripts/run_scan.py to create a credential-free red-team report.
Checks: Run python3 -m unittest discover -s tests and python3 -m redteam_lab.scanner examples/cases.json.

Architecture Notes

Architecture guide summarizes the system scope, first files to inspect, runtime commands, and known boundaries.
Quality notes lists the local checks, CI surface, and release expectations for this repository.
Enterprise readiness notes outlines security, data, operations, integration, and handoff expectations.

What It Demonstrates

attack case fixtures
policy detectors with severity and category
reusable scan reports
regression tests for known unsafe patterns
no live keys, no external model calls, no private data

Architecture

flowchart LR
    Cases["Synthetic cases"] --> Scanner["Policy scanner"]
    Scanner --> Findings["Findings"]
    Findings --> Score["Risk score"]
    Score --> Reports["JSON and Markdown reports"]

Quick Start

python3 -m unittest discover -s tests
python3 scripts/run_scan.py

The scan writes:

artifacts/redteam-report.json
artifacts/redteam-report.md

Policy Categories

Category	Examples
prompt injection	ignore prior rules, reveal hidden instruction
secret handling	key/token/password exfiltration language
unsafe tool use	shell execution, file deletion, network fetch pressure
data boundary	attempts to access private or unrelated data

Verification

python3 -m unittest discover -s tests
python3 -m redteam_lab.scanner examples/cases.json

All fixtures are synthetic.

Cloud + AI Architecture

Cloud + AI architecture blueprint
Machine-readable architecture manifest
Validation command: python3 scripts/validate_architecture_blueprint.py

Enterprise Productization

Product operating model defines the product scope, trust boundary, operating checks, and service path for this repository.

System Architecture

System architecture maps the runtime boundary, data/control flow, cloud or local deployment surface, and operating assumptions for this repository.

Service Architecture

Service architecture defines the cloud resources, account information, cost controls, and production guardrails needed to turn this repo into a scoped service without publishing public financial assumptions.

Search And Service Surface

Public entry: public sample attack catalog and static report
Paid boundary: private red-team scenario suite and recurring scan report dashboard
Canonical URL: https://ai-security-redteam-lab.pages.dev/
Lead capture: https://kim3310-doeon-kim-portfolio.pages.dev/?offer=ai-security-redteam-lab&inquiry=agent-reliability-audit#private-inquiry
Resource route: https://kim3310-doeon-kim-portfolio.pages.dev/resources/ai-security-redteam-lab/
Commercial route: https://kim3310-doeon-kim-portfolio.pages.dev/?offer=ai-security-redteam-lab#service-offers
Machine-readable offer: docs/service-offer.json
Search growth implementation: docs/search-growth-implementation.md
Revenue architecture: docs/revenue-architecture.md