The OWASP Top 10 for LLM Applications is the closest thing the industry has to a shared checklist of the security risks specific to systems built on large language models. You do not need to read it as an engineer. As an investor or an enterprise buyer, you need it for one purpose: to know which risks to verify before you commit, and which are operational details to leave with the people running the system.
The categories worth verifying during diligence
A handful of the OWASP risks are the ones that should shape an investment or procurement decision, because getting them wrong is expensive and hard to fix later.
Training data poisoning and provenance. Where did the training data come from, was it licensed for commercial use, and could it have been tampered with. This is a due diligence question because the answer affects both legal exposure and model integrity, and it is very hard to remediate after the fact.
Sensitive information disclosure. Can the system leak data it should not, either from its training data or from one user's session into another's. For any system handling regulated or personal data, this is a must-verify.
Supply chain risk. Most LLM applications are built on third-party models and APIs. What is the system actually depending on, and what happens if that dependency changes its terms, its pricing, or its behaviour. A startup whose entire product is a thin layer on one provider's API has a supply chain risk sitting at the centre of its business.
The categories that are operational, not deal-breaking
Risks like insecure output handling, excessive agency, and rate-limiting are real, but they are the kind of thing a competent team fixes during normal operation. You want to confirm the team understands them. You do not usually kill a deal over them.
The prompt injection question
Every investor now asks about prompt injection, because they have heard the term. It is on the OWASP list, and it is a genuine risk where an LLM can be manipulated by crafted input. But "are you protected against prompt injection" is close to unanswerable, because no one is fully protected and any honest team will say so.
The better question is: what can your system actually do if it is successfully manipulated. A model that can only return text is a contained problem. A model wired up to take actions, send emails, move money, change records, is a serious one. The risk is not the injection. It is the blast radius. Ask what the system is permitted to do, not whether it can be tricked, and you will learn far more about how seriously the team has thought about security.
If you are weighing an AI investment, acquisition, vendor selection, or training programme, our team is happy to start with a conversation about scope and approach.
The views and findings in this article are shared for general information only. They are high-level perspectives, not legal, financial, regulatory, or other professional advice, and should not be relied upon for any specific decision or circumstance. For guidance tailored to your situation, please consult a qualified adviser.