Prompt injection is now the dominant attack vector against LLM-powered applications — and most teams shipping AI features don’t have a defensive playbook. We map the attack taxonomy, walk through real exploit patterns, and lay out the controls that actually contain the blast radius.

A step-by-step tutorial for wiring an MCP server into a WordPress site — using the AI Engine MCP adapter — so Claude, Cursor, or any MCP-compatible client can read posts, run admin tasks, and edit content. With auth, scope, and security hardening you actually need.

Model Context Protocol (MCP) is the emerging open standard for connecting AI assistants to tools, data, and live systems. This guide explains how MCP servers work, the architecture behind them, and how to build your first one — with security caveats security teams need to know.

A 2026 workflow for telling AI-generated phishing apart from real correspondence — combining email-header forensics, public LLM-detection classifiers, and DKIM/SPF replay analysis.

A practitioner’s tutorial for running Llama 3 70B locally with Ollama, Open WebUI, and the right hardware. Privacy-sensitive AI work without sending a byte to OpenAI or Anthropic.

A 2026 tutorial for running structured prompt-injection and jailbreak red-team tests against your own LLM application using NVIDIA Garak, Microsoft PyRIT, and Promptfoo. Open-source, repeatable, CI-friendly.

The EU AI Act’s enforcement window is open in 2026. Here’s what US companies actually need to do, ranked by risk tier and deadline, in plain English.

A practitioner’s look at how threat actors are wiring open-source LLMs and agent frameworks into their reconnaissance pipelines, what that means for defender visibility, and the detection signals that still work.

A practitioner’s field manual on prompt injection in 2026 — the four attack patterns that work against production LLM apps, the controls that actually mitigate them, and the test cases your red team should be running this quarter.

Shadow AI — the AI tools your employees use without IT’s blessing — is the 2026 version of shadow IT, and it’s leaking proprietary code, customer data, and internal strategy at a pace most security teams aren’t measuring.

Every successful jailbreak prompt has a price. A look at the underground market for AI agent bypasses in 2026 — who builds them, who buys them, and how the profit motive shapes the threat landscape.

Running models locally feels safer than sending data to OpenAI. Sometimes it is. Sometimes it isn’t. A practitioner’s breakdown of the actual security trade-offs between local and cloud AI deployments.

AI in security operations has graduated from vendor demoware to production reality — but only in three specific use cases. Here’s where AI is genuinely changing SOC work in 2026, and where it still doesn’t.

A practitioner’s account of building a local AI stack with OpenClaw — and discovering that out-of-the-box defaults turn it into a wide-open data exposure surface for prompt injection and remote compromise.

The EU AI Act, in force since August 2024 and phasing in through 2027, is the first comprehensive AI regulation in any major jurisdiction. Here is what it actually requires, who it applies to, and what organisations should be doing now.

Red teaming for traditional software is well-defined. Red teaming for AI systems borrows the term but operates differently. Here is what AI red teaming actually involves, the documented methodologies, and how to structure an effective exercise.

Should you build on a closed API like GPT-5 or Claude, or run an open-weight model like Llama 4 or Mistral on your own infrastructure? The choice has real security implications that go beyond cost and performance.

A trained model represents enormous investment in compute, data, and expertise. The threat of model theft — through extraction, distillation, or outright weight exfiltration — is real and increasingly operationalised. Here is the threat landscape and the realistic protections.

Add a small, carefully chosen perturbation to an image and a state-of-the-art classifier sees a school bus instead of a panda. Adversarial examples are the longest-running unresolved problem in machine-learning security and increasingly relevant to deployed systems.

An AIBOM lists everything that went into producing an AI model — base model, training data, fine-tuning corpora, dependencies, evaluation results. The concept is borrowed from software supply-chain security and increasingly required by regulators. Here is what an AIBOM actually contains and why it matters.

Pre-LLM phishing was constrained by language. Post-LLM phishing is not. The result is a measurable upgrade in lure quality, a wider reach into non-English-speaking markets, and an emerging class of personalised attacks that were previously economically unviable.

Deepfake video and AI voice cloning have moved from research demos to operational tools used in fraud, fraud-driven CEO scams, election interference, and harassment. Here is the actual state of the technology, the real incidents, and what defences exist.

AI is now in nearly every security product’s marketing copy. Some of it has changed the game; some of it has not changed anything. Here is a category-by-category honest assessment of where machine learning has actually moved the security needle and where the marketing has run ahead of the technology.

Prompt injection is the SQL injection of the LLM era — easy to demonstrate, hard to fully defend against, and present in essentially every commercial LLM application. Here is what it is, why it persists, and the realistic mitigation playbook.