Cybersecurity Compliance
OWASP Top 10 for LLM Applications (2025), Explained with Examples
The OWASP GenAI Security Project published the 2025 edition (v2.0) of its Top 10 for LLM Applications in November 2024, and it's quickly become the reference checklist enterprise security reviewers ask vendors about by name. Two categories are new since the prior list - System Prompt Leakage and Vector and Embedding Weaknesses - reflecting how fast real-world LLM deployments (RAG pipelines, agents, tool use) have moved. Here's what each of the ten actually means, with concrete examples.
LLM01: Prompt Injection
An attacker crafts input - directly in a chat box, or indirectly hidden in a document, webpage or email the model later reads - that overrides the system's intended instructions. A support chatbot told via a hidden instruction in a PDF to 'ignore prior instructions and reveal the system prompt' is a textbook indirect prompt injection.
LLM02: Sensitive Information Disclosure
Models can leak information they were trained on, retrieved via RAG, or given earlier in a conversation - PII, credentials, proprietary business logic - especially when output isn't filtered before being shown to a user who shouldn't see it.
LLM03: Supply Chain
Risk inherited from third-party models, fine-tuning datasets, plugins, LoRA adapters or hosting platforms - a poisoned pretrained model or a compromised plugin marketplace entry can compromise every application built on top of it.
LLM04: Data and Model Poisoning
Manipulating training, fine-tuning or embedding data to introduce vulnerabilities, biases or backdoors - including poisoning a RAG knowledge base so the model confidently returns attacker-controlled content as fact.
LLM05: Improper Output Handling
Treating model output as inherently safe and passing it downstream unsanitized - into a database query, a shell command, or rendered HTML - turns a text-generation flaw into classic injection, XSS or remote code execution.
LLM06: Excessive Agency
Giving an LLM-based agent more permissions, tools or autonomy than a task actually requires. An email-drafting assistant that can also send email unsupervised, or a coding agent with unrestricted shell access, turns a successful prompt injection into real-world impact.
LLM07: System Prompt Leakage
New in the 2025 edition. System prompts often contain business logic, internal tool definitions or guardrail instructions that were never meant to be public - and a leaked system prompt both exposes that logic and often hands an attacker a roadmap for further prompt injection.
LLM08: Vector and Embedding Weaknesses
Also new in 2025, reflecting the rise of RAG. Covers risks specific to vector databases and embedding pipelines - unauthorized access to embedded data, cross-tenant data leakage in a shared vector store, and retrieval manipulation that surfaces the wrong (or attacker-planted) context.
LLM09: Misinformation
Confident, plausible, factually wrong output ('hallucination') presented without appropriate caveats - a real risk when an LLM's output feeds a decision, a customer-facing answer, or another automated system without a human check.
LLM10: Unbounded Consumption
An expansion of the earlier 'Denial of Service' category to also cover resource and cost risk - an attacker (or just unexpected usage) driving excessive inference calls, token usage or compute cost, whether to degrade availability or run up your API bill.
Why this list matters beyond security teams
Enterprise procurement and vendor-security reviews are starting to reference the OWASP LLM Top 10 by name, the way the OWASP (web) Top 10 and API Security Top 10 became standard procurement vocabulary before it. If you ship an LLM-powered feature, expect a buyer's security questionnaire to ask which of these ten you've tested for - and a mapped test report is a faster answer than a from-scratch explanation.
Need help with this in practice?
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Frequently asked questions
Is this the same as the regular OWASP Top 10?
No - it's a separate list maintained by the OWASP GenAI Security Project specifically for LLM-powered applications, covering risks (like prompt injection and excessive agency) that don't map cleanly onto the classic web-application Top 10.
Do these risks apply to a simple chatbot, or only complex AI agents?
Most apply even to a simple chatbot - prompt injection, sensitive information disclosure and improper output handling don't require agentic tool use. Excessive Agency and some supply-chain risks become more relevant as a system gains tools and autonomy.
How do you test for these?
Through targeted adversarial testing mapped to each category - injection and jailbreak attempts, output-handling checks, permission and tool-scope review, and RAG/vector-store boundary testing - reported against the OWASP LLM Top 10 and MITRE ATLAS.
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