LLM SECURITY 101 — PART 1
Understanding the Basics & Key Early Risks
As we prepare for 2026, AI tools — especially large language models (LLMs) — are becoming part of everyday work. December is a good moment to focus on the fundamentals, explained in a simple and practical way before we move into deeper technical topics in January.
This article introduces how LLMs work, what they can and cannot do, and three of the most common early security risks to be aware of when teams use AI tools in their day-to-day tasks.
1) How LLMs actually work
LLMs don’t “think” or “understand” information the way people do.
They generate text by recognizing patterns and predicting what is most likely to come next.
They can:
- summarize
- assist with writing
- organize information
- generate ideas
But they do not verify facts, have awareness, or act as real-world specialists.
2) What LLMs are — and what they are not
LLMs ARE:
- powerful text-generation models
- pattern-based predictors
- helpful assistants for everyday tasks
LLMs are NOT:
❌ search engines
❌ factual authorities
❌ medical, legal, financial or technical experts
❌ secure storage solutions
❌ replacements for human judgment
3) Prompt Injection (LLM01)
Prompt Injection occurs when hidden instructions inside user-provided text cause the model to behave differently than intended.
This can happen when users paste text from emails, documents or websites without checking it first.
How to reduce the risk:
✔ review text before pasting it into AI
✔ use clear instructions
✔ reset the session if responses look unusual
4) Sensitive Information Exposure (LLM02)
A common mistake is placing sensitive or non-public information into public AI tools.
Avoid entering:
❌ personal data
❌ sensitive documents
❌ non-public business information
❌ financial details
❌ proprietary code
Simple principle:
If you wouldn’t share it publicly, don’t paste it into an AI tool.
5) Hallucinations – incorrect but confident output (LLM04)
LLMs sometimes produce information that sounds confident but is factually incorrect.
This happens because they generate text based on likelihood, not accuracy.
To manage this risk:
✔ verify important facts
✔ check numbers and citations
✔ treat AI output as a draft, not a final answer
6) How information may leave the organization unintentionally
When using public AI tools, information can move outside the organization’s environment in ways that are not always obvious.
This may happen through:
✔ cloud processing on external servers
✔ temporary storage or diagnostic logs
✔ human mistakes (copying sensitive text into AI)
✔ browser extensions or integrations
✔ sharing responses that include copied content
Awareness and careful prompting reduce these scenarios significantly.
7) Practical AI hygiene for 2026
✔ verify accuracy
✔ avoid entering sensitive information
✔ use approved corporate AI platforms
✔ follow internal security guidelines
✔ treat AI output as a draft
What’s next (January 2026)
Part 2 of the series will cover additional risks and more advanced topics, including:
- training data manipulation
• execution-related risks
• unsafe external resource calls
• supply chain vulnerabilities
Our goal is to help teams use AI tools with clarity, confidence, and safety.
Part 2: Advanced Risks & Safe Usage
Source (reference only):
This article references the OWASP LLM Top 10 categories conceptually.
No text has been copied. Source https://owasp.org/
