LLM Red Team Lab In Progress
Interactive AI security lab with animated attack visualization, TechCorp scenario data, and measurable defense scorecards against live or mock LLMs.
- Role
- Project Lead / AI Security Engineer
- Year
- 2026
- Category
- Software Dev
About This Project
Built a standalone Python red-team lab that compares vulnerable and hardened LLM chat pipelines side by side. Curated 32 attack prompts plus 6 benign regression cases across OWASP LLM Top 10 categories against a realistic TechCorp employee and infrastructure dataset. Added multi-layer input and output guardrails, redesigned the UI with step-by-step attack animation and side-by-side chat simulation, and shipped a scorecard that reports block rate, benign pass rate, and latency overhead. Supports mock LLM for CI and OpenRouter live models for real-world testing. Deployable via Railway or Docker and linked from the portfolio as an AI security case study.
Technologies Used
Engineering Challenges
Attack Coverage vs False Positives: Tuned regex-based input scanning so obvious prompt-injection and exfiltration patterns were blocked while benign recruiter questions about APIs, skills, and experience still passed through.
Realistic Scenario Data: Embedded employee PII, salary bands, financial metrics, and AWS credentials in the system prompt so attacks target believable business data instead of abstract strings.
Dual Pipeline Parity: Ensured both vulnerable and hardened paths use the same LLM provider (mock or OpenRouter) so comparison results reflect defense layers rather than model variance.
Visual Security Story: Redesigned the lab UI with attack card grids, animated defense-layer breakdowns, and side-by-side chat panels so non-specialists can follow what each guardrail blocked.
Technical Highlights
Layered Guardrails: Implemented input scanning, server-side system prompt isolation, PII filtering, and credential blocking as separate defense layers with explicit audit results per request.
YAML Attack Catalog: Structured 32 attacks and 6 benign prompts across extraction, injection, and jailbreak files with OWASP categories and expected outcomes to drive automated regression testing.
Live Model Support: Added OpenRouter integration with a selectable model dropdown for testing defenses against real LLMs while keeping mock mode for local dev and CI.
Production-Ready Scaffold: FastAPI routes, Jinja lab UI with animated JavaScript visualization, pytest suite (unit and property-based), Docker image, Railway deploy config, and GitHub Actions CI with ruff, mypy, and bandit.
Business Logic
Recruiter-Friendly Demo: Visitors pick an attack card, watch the unprotected pipeline fail, see the protected pipeline block the same payload, and review a scorecard in under a minute.
Portfolio Separation: Kept Python runtime and demo hosting outside the Cloudflare Workers portfolio site while still linking the case study back to gssimao.com.
Interview Artifacts: Documented threat model, defense tradeoffs, sensitive-data scenario, and benchmark metrics recruiters and security engineers can discuss concretely.