AI Briefing

AI Briefing — 2026-08-22

4 articles · Generated in 488s

Security / Risk

Ultimate Guide to Prompt Injection: Step by Step Tutorial

Aikido Security · 2026-08-13 · 1,406 views · 🔥 156/day

Prompt injection isn’t a quirky chatbot bug; it’s a structural failure mode in how LLMs obey instructions. The sharp bit: system prompts, tools, and agent workflows can be manipulated to leak secrets or misuse automation, and current architectures can’t fully prevent it. That matters because one poisoned prompt can turn AI pipelines, CI/CD, and copilots into an attacker’s foothold.

Threat-model every AI tool and workflow.
Isolate secrets from model-accessible contexts.
Treat agent tools as high-risk interfaces.

New Udemy Course-AI Security Bootcamp-Guardrails,LLM Gateways,Observability

Krish Naik · 2026-07-25 · 9,697 views · 🔥 346/day

Building AI apps safely takes more than clever prompts; the hard part is guardrails, gateways, and observability that hold up in production. This bootcamp focuses on defending LLM systems against prompt injection, jailbreaks, leakage, and unsafe outputs while adding monitoring and governance. That matters because insecure AI demos are easy; reliable, enterprise-ready systems are not.

Add guardrails before exposing LLMs publicly.
Instrument outputs, failures, and policy violations.
Test jailbreaks and leakage continuously.

Build / Deploy

End to End Production-Grade LLM Serving with vLLM on Azure AKS | Terraform + NVIDIA GPU Operator

Sunny Savita · 2026-08-07 · 5,426 views · 🔥 361/day

Skip hosted APIs and learn the real work: standing up a self-hosted LLM stack on Azure AKS with vLLM, Terraform, and NVIDIA’s GPU Operator. The useful bit is not deployment alone, but how GPU scheduling, memory tuning, KV cache, and OpenAI-compatible serving fit together in production. That matters if you need lower inference costs, tighter control, or credible LLMOps skills beyond prompt-wrapping.

Provision AKS GPU nodes with Terraform first.
Tune vLLM memory and KV cache.
Verify GPU utilization before scaling.

Llama.cpp vs vLLM: Which Local LLM Engine Actually Scales?

IBM Technology · 2026-07-28 · 55,915 views · 🔥 2,236/day

Choosing the wrong local LLM engine burns performance fast: Llama.cpp wins on laptops and edge boxes, while vLLM is built to squeeze throughput from serious GPU setups. The real question isn’t which is better, but which fits your hardware, concurrency needs, and agent workload before you waste weeks tuning the wrong stack.

Match engine to hardware first.
Use vLLM for multi-user throughput.
Pick Llama.cpp for lightweight local runs.