Cybersecurity, learned like a practitioner.

AI / LLM Security — Beginner to Expert Intermediate Members

Trending AI Stack 2026 — Tools, Frameworks, Architecture Patterns

A practitioner's tour of what is actually being deployed in production AI systems in 2026: model providers, agent frameworks, vector databases, observability, evaluation, deployment platforms. Skip the hype, focus on what teams shipping code use.

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

AI Code Generation Security — Copilot, Cursor, Cline Risks

Copilot, Cursor, Cline, and Claude Code generate millions of lines per day. They also leak code via context window, suggest insecure patterns, are vulnerable to prompt injection in source files, and act as data-exfiltration channels. This module covers the threats and the enginee

Apr 29, 2026 45 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

Self-Hosting Llama / Mistral / Qwen — vLLM vs Ollama vs llama.cpp Benchmarks

Three serious LLM runtimes, three different sweet spots. Ollama for developers and single-user. llama.cpp for edge and embedded. vLLM for production multi-user serving. This module benchmarks them on identical hardware, explains the architectural differences, and shows when to pi

Apr 29, 2026 55 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

Build Your Own ChatGPT Wrapper Safely — Architecture, Auth, Rate Limit, Logging

Half the SaaS launches in 2024-2025 were "ChatGPT for X." Most shipped with embarrassing security gaps: hardcoded API keys, no rate limiting, no abuse logging, prompt injection that leaks system prompts. This module is the production architecture for a chat wrapper that does not

Apr 29, 2026 60 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

RAG Security — Vector Store Leaks, Retrieval Hijacks, Embedding Inversion

Retrieval-Augmented Generation looks like a clean architecture: store docs as vectors, retrieve relevant ones at query time, feed to LLM. The security failure modes are subtle: cross-tenant data leakage via shared vector indexes, prompt injection planted in indexed documents, and

Apr 29, 2026 55 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

Fine-tuning Safety — LoRA, SFT, and RLHF Explained for Security Teams

Fine-tuning sounds like configuration. It is not — it is a destructive operation that can degrade safety properties of the base model. This module explains the three tuning methods (SFT, LoRA, RLHF/DPO), what each step exposes from a security perspective, and a practical safe-tun

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

Data Poisoning and AI Supply Chain — Attacks Before Deployment

Most AI defenders worry about runtime attacks. Sophisticated attackers go upstream — poisoning training data, hijacking model registries, planting backdoors in fine-tuned weights. Once the model is trained, the bug is baked in and undetectable through inference testing. This modu

Apr 29, 2026 55 min Open

Attacker Mindset — Cloud Intermediate Free

Cloud Audit Trail Forensics

Cloud audit logs are richer than on-prem. Every API call. Identity, source, resource, action. With CloudTrail Lake or BigQuery, queryable for years. Forensic discipline: log to a separate logging account. Object Lock on the bucket. Cross-region replication. Otherwise: attacker disables logging early in attack. The mindset: cloud audit logs deserve their own account, their own […]

Apr 27, 2026 15 min Open

Attacker Mindset — Cloud Intermediate Free

Cost as Security Signal

Cost anomaly: 10x normal compute spend overnight. Could be: new feature launched. Could be: crypto mining instance spun up by attacker. The cost-anomaly alert is a security signal in disguise. AWS Cost Anomaly Detection, Azure Cost Anomaly, GCP recommendations all available. The mindset: integrate billing alerts with security ops. Unusual cost = investigate, don’t just […]

Apr 27, 2026 15 min Open

Attacker Mindset — Network Intermediate Free

The Network Forensics Mindset

Network logs are evidentiary in regulator inquiries and lawsuits. They have weight when properly preserved. The discipline: timestamps in UTC, defined retention, chain of custody, immutable archive. Without these, “we have logs” doesn’t answer “can the regulator rely on them?” The mindset: every log is a future court exhibit. Build retention and integrity for that […]

Apr 27, 2026 15 min Open