AI Security · 85 articles

AI Security

AI / LLM security — prompt injection, data poisoning, agent threat models, building trustworthy AI, optimisation, and the architecture of trending AI products.

AI Security

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,…

Apr 29, 2026 · 10 min read
AI Security

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…

Apr 29, 2026 · 9 min read
AI Security

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,…

Apr 29, 2026 · 9 min read
AI Security

AI Red Teaming — Methodology, PyRIT, garak, llm-guard

Red teaming an LLM is not penetration testing. There is no shell to pop, no service to enumerate. Instead you systematically probe…

Apr 29, 2026 · 9 min read
AI Security

Model Extraction Attacks — Stealing LLMs by Querying

You can clone a closed-source LLM by querying it many times and training your own model on the input-output pairs. Researchers showed…

Apr 29, 2026 · 9 min read
AI Security

Adversarial Examples — FGSM, PGD, Transfer Attacks (Image and Text)

A 0.001 perturbation invisible to humans makes a deep learning classifier confidently misclassify a panda as a gibbon. This 2014 demonstration started…

Apr 29, 2026 · 9 min read
AI Security

Backdooring LLMs — Trigger Phrases in Fine-tuning Data

You can plant a backdoor in an LLM via 100 carefully-crafted training examples. Normal queries work normally; the trigger phrase activates malicious…

Apr 29, 2026 · 9 min read
AI Security

Building a Production AI Stack — Vector DB, LLM, Auth, Observability

A real production AI application has 6-8 components: LLM (own or API), embedding model, vector DB, prompt cache, auth, rate limit, content…

Apr 29, 2026 · 9 min read
AI Security

Defending AI Endpoints — Rate Limit, Content Filters, NeMo Guardrails, Llama Guard

Once your AI endpoint is public, attackers will probe it within hours — for free LLM access, prompt injection, content-policy violations, and…

Apr 29, 2026 · 8 min read
AI Security

AI Security 101 — Why ML Systems Break Differently

Traditional software is deterministic. ML systems are probabilistic, learn from data, and respond to natural language. That changes the entire threat model…

Apr 29, 2026 · 10 min read