📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
Every serious security topic in 2026 eventually requires understanding what a large language model actually is. Prompt injection, jailbreaking, model theft, adversarial inputs, hallucination exploitation — all of these attack categories only make sense once you understand the underlying architecture. My goal in this guide is to explain LLMs the way I explain them in security briefings: technically accurate, practically focused, and without the machine learning PhD prerequisites. If you understand how LLMs work, you understand why they’re vulnerable in the specific ways they are.
What You’ll Learn
What an LLM actually is — the plain English technical explanation
How LLMs are trained and why training creates security risks
Why LLMs hallucinate and how that creates exploitable behaviour
The attack surface specific to LLMs — what makes them different from traditional software
How to think about LLM security as a practitioner
⏱️ 14 min read ### What Is an LLM? — Security Guide 2026 1. What an LLM Actually Is 2. How LLMs Are Trained — and Why Training Matters for Security 3. Why LLMs Hallucinate 4. The LLM Attack Surface — What’s Different 5. How to Think About LLM Security Once you understand the LLM architecture, the OWASP AI Security Top 10 and the prompt injection explainer will make significantly more sense. The AI Red Teaming Guide applies this understanding to formal security assessments.
What an LLM Actually Is
A large language model is a statistical prediction engine trained on text — the most important technical concept for any security practitioner to understand before engaging with AI security work in 2026. Given a sequence of words, it predicts the most probable next word — then the next, then the next — to produce a response. That’s it at the core. The “large” part refers to the number of parameters: GPT-4 is estimated at around 1.7 trillion parameters. Each parameter is a number that was adjusted during training to make the model better at predicting text.
What makes this security-relevant is what “predicting text” means in practice — and this is the concept that unlocks every LLM vulnerability class. The model doesn’t have a database of facts. It doesn’t look things up. It produces text that is statistically similar to text it was trained on. When it produces a correct answer, it’s because that pattern appeared reliably in training data. When it produces a confident wrong answer, it’s because the wrong pattern was more statistically likely given the input.
LLM ARCHITECTURE — SECURITY PRACTITIONER’S VIEWCopy
The core components
Tokeniser: converts input text into numerical tokens (roughly words/subwords)
Transformer: the neural network architecture — processes tokens in parallel via attention
Parameters: the billions of numbers that encode learned patterns from training
Context window: the amount of text the model can “see” at once (4K to 2M tokens)
Output sampler: selects the next token probabilistically — explains non-determinism
What the model “knows”
Nothing — LLMs don’t have knowledge in the way humans do
They have statistical patterns learned from text corpora
This distinction is critical for understanding hallucination and injection attacks
What the context window contains (security relevant)
System prompt: developer’s instructions defining the AI’s role and rules
Conversation: all previous messages in the current session
Retrieved data: RAG content, tool outputs, documents processed
User input: the current message — potentially attacker-controlled
Key insight: the model processes ALL of this as undifferentiated text
How LLMs Are Trained — and Why Training Matters for Security
Understanding LLM training is essential for understanding data poisoning, backdoor attacks, and why model provenance matters. Training happens in stages, and each stage creates a different security risk profile.
LLM TRAINING STAGES — SECURITY IMPLICATIONSCopy
Stage 1: Pre-training
Data: massive text corpus — web crawl, books, code, academic papers
Process: predict next token across billions of examples → parameters updated
Risk: poisoned web content influences what the model learns as “true”
Risk: private data in the corpus can be memorised and later extracted
Risk: backdoors can be injected via coordinated corpus poisoning
Stage 2: Fine-tuning / Instruction Tuning
Data: curated examples of desired input-output behaviour
Process: further adjusts parameters to follow instructions helpfully
Risk: malicious fine-tuning datasets introduce backdoors or remove safety
Risk: third-party fine-tuning services can modify model behaviour
Stage 3: RLHF (Reinforcement Learning from Human Feedback)
Data: human ratings of model outputs (good/bad)
Process: adjusts model to produce outputs humans rate highly
Risk: manipulated rater pool could shift model values/behaviour
Benefit: this stage also installs safety guidelines and refusal behaviour
Why training provenance matters
A model from an unknown source could have any of these attacks embedded
Supply chain: downloading a model from Hugging Face ≠downloading safe weights
Best practice: use models from verified sources with published model cards
Why LLMs Hallucinate
Hallucination is one of the most security-relevant LLM behaviours and the one that’s most commonly misunderstood. My explanation in security briefings: the model isn’t lying and it isn’t broken. It’s doing exactly what it was designed to do — produce statistically probable text — in a situation where the probable text happens to be wrong.
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