Key insight
The OWASP Top 10 for LLM applications is the industry’s shared, ranked checklist of the biggest risks in AI apps — and you already know most of it, because it is essentially the topics in this series. Its value is a common language, a ready-made checklist, and shared priorities, so a whole industry can defend together. The ten group naturally into attacks on the input, the data, and the system. Use it as a floor every AI team should clear — never as a box to tick that lets you stop thinking.
Having covered many separate threats, it helps to zoom out and see them as one recognised whole. This page hands you the official names and numbers for what you already understand, so you can connect your intuition to the framework the whole industry uses — and use it as a practical audit.
1 · Who is OWASP, and why a list?
OWASP is a long-respected, community-driven security organisation — volunteers and experts who, for decades, have published free guidance on how software gets attacked. Their most famous format is the top-ten list: a ranked collection of the most important risks in a given area, agreed on by many practitioners. The value of such a list is that it turns scattered, tribal knowledge into a shared map. Instead of every team discovering these dangers the hard way and inventing their own names, everyone can point to the same numbered list and immediately understand each other. As AI applications exploded, OWASP created exactly this for them: a top-ten of the biggest risks in LLM and agent apps.
2 · You already know the list
Here is the good news, and the reason this topic comes near the end: you already know the list. The OWASP Top Ten for LLM applications is, almost item for item, the very set of topics we have walked through. Prompt injection sits at number one, exactly where our attention has been. Insecure output handling, training-data poisoning, sensitive information disclosure, excessive agency, the supply chain, model theft — they are all on it. We did not follow the list slavishly, but we covered its substance, in plain language, one idea at a time. So this topic is not teaching you ten new things; it is handing you the official names and numbers for what you already understand.
3 · Group one: attacks on the input
It helps to see the ten as a few natural families. The first is attacks on the flow of text through the model — the front lines. Prompt injection, the headline risk: hiding instructions in the data an agent reads. Its close cousin, jailbreaks and guardrail bypass: wording a request to slip past the safety rules. And insecure output handling: blindly trusting what the model produces and feeding it into something powerful. These three share a root, the one we established early on — the model treats instructions and data as the same text on one shared desk. Master that single idea and you understand nearly half the list at once.
4 · Group two: attacks on the data
The second family is about data — both the data that shapes the model and the data trapped inside it. Training-data poisoning: corrupting what the model learns from, so a flaw or backdoor is baked in before it ships. Sensitive information disclosure: the model revealing secrets it should not — from its hidden instructions, connected systems, or other users. And the leakage risks: model theft, extraction, inversion, and membership inference, all ways the model’s value or its private training data flows out. Where the first family attacks the conversation, this one attacks the model’s memory. The common defense thread is discipline about data: know what you feed the model, and be careful what secrets it can ever hold.
5 · Group three: attacks on the system
The third family looks past the model to the system built around it. Excessive agency: giving an agent more tools, permissions, and autonomy than its job needs, so a single trick has a huge blast radius. Supply-chain risk: inheriting weaknesses from every borrowed model, dataset, library, and plugin. And the resource risks — sometimes called unbounded consumption — where an attacker floods your model with expensive queries to run up your bill or take the service down, which also covers the extraction attacks that clone your model through sheer volume. This family is where AI security meets classic engineering discipline: least privilege, knowing your dependencies, and rate-limiting.
6 · Why a shared list matters
Why does having this official list matter, beyond tidiness? Three reasons. First, a common language: when someone says “we need to handle LLM01,” everyone knows they mean prompt injection — no confusion, faster conversations across teams and companies. Second, a ready-made checklist: instead of hoping you remembered every category of risk, you can walk your system against ten agreed items and see where you are exposed. Third, shared priorities: because the list is ranked by real-world impact, it tells a team where to spend limited security effort first — you harden against injection before you worry about rarer risks. A shared list turns AI security from something each team fumbles alone into something an entire industry improves together.
7 · A checklist, not a guarantee
One honest caution, in the spirit of the guardrails topic: the list is a checklist, not a guarantee. Covering all ten items does not make you safe — it means you have addressed the most common, best-understood risks, which is a strong floor, not a finish line. New attack techniques appear constantly, the list itself gets revised as the field learns, and your specific application will have risks no general list can name. So use it the right way: as a baseline you should absolutely clear, a shared starting point for thinking about threats — never as a box to tick that lets you stop thinking. The teams that stay secure treat the Top Ten as the floor everyone must reach, then keep going, watching for the risks unique to what they built.
8 · A simple test you can run this week
1. Pull up the OWASP Top 10 for LLM Apps — it is free and public.
2. For each item, ask: does this apply to our system?
3. Mark each: handled, partly handled, or not at all.
4. Pick the highest-risk gap and plan one fix.
The lesson: the list is a shared floor to clear — use it as a checklist, then keep going past it.
9 · Glossary — every term, spelled out
- OWASP
- A community-driven security organisation known for its ranked “top ten” lists of the most important risks in an area.
- OWASP Top 10 for LLM Applications
- A ranked checklist of the biggest risks in AI/LLM apps — essentially the topics in this series, with official names.
- LLM01, LLM02…
- The numbered identifiers for each risk (e.g., LLM01 is prompt injection) — a shared shorthand across teams.
- Unbounded consumption
- Flooding a model with expensive queries to run up costs or degrade service — the resource-risk family.
- Baseline / floor
- A minimum standard every team should clear — how the Top Ten should be used, not as a finish line.
- Threat modelling
- Systematically thinking through how a system could be attacked — which the checklist supports but does not replace.
The OWASP Top 10 for LLM apps is the industry’s shared, ranked checklist — and it is essentially this series.
The ten group into attacks on the input, the data, and the system.
A shared list gives a common language, a ready checklist, and priorities, so the whole industry defends together.
Use it as a floor to clear, not a guarantee — cover the ten, then keep watching for risks unique to your app.
References
- OWASP, Top 10 for Large Language Model Applications — the list itself. owasp.org
- OWASP Foundation — about the organisation and its top-ten methodology. owasp.org
- This guide’s Prompt Injection, Explained From Zero — LLM01, the headline risk.
- This guide’s MITRE ATLAS, Explained From Zero — the playbook of how these risks are actually attacked.