llama-guard

The course provides guidance on best practices for prompting and building applications with the powerful open commercial license models of Llama 2.

LLM Security Project with Llama Guard

Open-source red-team harness for LLMs. Jailbreaks, prompt injection, agentic tool-abuse and image attacks, scored with validated judges (Llama Guard 3, StrongREJECT).

A local, zero-dependency fuzzer for open-weight LLM guardrails (Llama Guard, Prompt Guard). Measures per-family bypass rate and over-blocking - and verifies each bypass still works on the model behind the guard.

Enterprise RAG chatbot with namespace-scoped RBAC, hybrid BM25 + vector retrieval, JWT auth, PII redaction, and Llama Guard safety — RAGAS faithfulness 0.91 across 77 automated tests.

SentinelShield: Advanced AI content moderation combining Llama Prompt Guard 2, rule-based filtering, and real-time analysis. Protect your applications from harmful content, prompt injection attacks, and inappropriate material with sub-second response times.

Brazilian LLM guardrails layer, detects and masks BR PII (CPF, CNPJ, RG, CNH, título de eleitor, PIS) with checksum validation, classifies PT-BR prompt injection via Llama Guard 3, and maps every guardrail to its LGPD article. Python library + Docker REST API sidecar.

Defense-in-depth input safety for LLMs — perplexity gate + FAISS + ModernBERT + LoRA + Llama Guard 3, behind a deterministic policy gate. 99.88% accuracy, 99.47% jailbreak recall, calibrated confidence, ONNX-optimized. Live demo on HF Spaces.

Evaluating PAIR, GCG, and Prompt-RS jailbreak attacks against LLaMA-3.1, LLaMA-4, and Qwen3-32B. Two-stage defense pipeline (prompt sanitization + LlamaGuard) achieving 95% Defense Block Rate.

6-stage LLM safety middleware (rate-limit, input sanitization, injection detection, system-prompt hardening, output validation, audit) — Python + TypeScript, benchmarked against PurpleLlama/AdvBench

Nutrition Disorder Specialist Chatbot using Agentic RAG System

Defensa en capas para LLMs en producción: guardrails, observabilidad y regresión adversarial en CI. Charla de DojoConf Panamá 2026.