Owasp LLM Top 10
Also known as: OWASP Top 10 for LLMs, OWASP LLM Top Ten, LLM Top 10
- Owasp LLM Top 10
- A ranked list of the ten most critical security risks in large language model applications, maintained by the Open Worldwide Application Security Project (OWASP). It provides a shared vocabulary and prioritization framework for teams securing AI-powered systems against threats like prompt injection and data poisoning.
The OWASP LLM Top 10 is a community-driven security framework that identifies the ten most critical vulnerabilities in applications built on large language models, from prompt injection to data poisoning.
What It Is
If you’re building or buying software that uses a large language model, you need a way to know which security risks matter most. The OWASP LLM Top 10 gives you that list — a ranked catalog of the ten most dangerous vulnerability categories specific to LLM-powered applications.
Think of it like a building inspector’s checklist for AI. A structural inspector doesn’t check every nail; they focus on the load-bearing walls, the foundation, and the fire exits. The OWASP LLM Top 10 does the same thing for LLM security: it tells you where the structural failures are most likely to happen and where the damage would be worst.
OWASP (the Open Worldwide Application Security Project) has maintained a similar Top 10 for traditional web applications since 2003. The LLM-specific version launched in response to the rapid adoption of language models across industries. According to OWASP, the current version is 2025 (v1.1.0), with Prompt Injection (LLM01) holding the top spot as the single most critical risk.
The list covers ten vulnerability categories. Prompt injection sits at number one because attackers can override system instructions by embedding malicious commands in user input or external data. Other entries address sensitive information disclosure (when models leak training data or system prompts), supply chain vulnerabilities (compromised model weights or plugins), and excessive agency (when an LLM-connected tool takes actions beyond what the user intended).
According to Qualys Blog, the 2025 edition added two new entries: Vector and Embedding Weaknesses and System Prompt Leakage. These reflect the industry’s shift toward retrieval-augmented generation (RAG) architectures and agentic workflows where models act autonomously. According to Practical DevSecOps, OWASP also released a separate Top 10 for Agentic Applications in December 2025, recognizing that autonomous AI agents introduce risk patterns distinct from standard LLM chat interfaces.
How It’s Used in Practice
Most teams encounter the OWASP LLM Top 10 when they’re running security assessments on AI features — either during red teaming exercises or vendor evaluations. A product manager reviewing an AI vendor’s security posture might ask: “Which OWASP LLM Top 10 categories does your product address?” A security team planning a red teaming engagement uses the list to structure their test cases, making sure they probe for prompt injection, data leakage, and excessive agency rather than testing randomly.
The list also shows up in compliance conversations. Organizations building responsible AI governance programs reference it alongside internal risk frameworks to define what “secure AI deployment” means in practice.
Pro Tip: Don’t treat the list as a pass/fail checklist. Each category contains a spectrum of attack surfaces. Use it as a conversation starter with your security team: “For LLM01 (Prompt Injection), what’s our specific exposure given how we handle user input?” That question turns a generic framework into targeted risk reduction.
When to Use / When Not
| Scenario | Use | Avoid |
|---|---|---|
| Structuring a red teaming engagement against an LLM feature | ✅ | |
| Evaluating an AI vendor’s security documentation | ✅ | |
| Building a threat model for a new AI-powered product | ✅ | |
| Auditing a traditional SQL database for injection attacks | ❌ | |
| Replacing a full penetration test with only the top 10 checklist | ❌ | |
| Training non-technical stakeholders on AI security priorities | ✅ |
Common Misconception
Myth: Covering all ten items on the OWASP LLM Top 10 means your application is secure. Reality: The list identifies the most common vulnerability categories, not every possible attack vector. Real-world exploits often combine multiple categories or target implementation-specific weaknesses that no generic list can predict. The OWASP list gives you a starting point, not an endpoint — especially since automated testing tools can miss context-dependent vulnerabilities that require human judgment to discover.
One Sentence to Remember
The OWASP LLM Top 10 tells you which walls to check first, but an inspector still needs to walk the building — use it to focus your security efforts, not to declare the job done.
FAQ
Q: How often does the OWASP LLM Top 10 get updated? A: According to OWASP, the current version is 2025 (v1.1.0), following the initial 2023 release. The list updates as the threat environment shifts, reflecting new attack patterns and architectural trends.
Q: Is the OWASP LLM Top 10 a compliance requirement? A: No, it’s a voluntary community framework, not a regulatory mandate. However, many organizations reference it in their AI governance programs as a baseline for responsible deployment.
Q: How does the OWASP LLM Top 10 differ from the traditional OWASP Top 10? A: The traditional list covers web application risks like cross-site scripting and broken access control. The LLM version targets AI-specific risks such as prompt injection, hallucination, and excessive model agency.
Sources
- OWASP: OWASP Top 10 for Large Language Model Applications - Official project page with the current ranked list and mitigation guidance
- Qualys Blog: What’s Changed in the OWASP Top 10 for LLMs 2025 - Analysis of new entries and structural changes in the 2025 edition
Expert Takes
The OWASP LLM Top 10 categorizes attack surfaces by observed frequency and severity, not by theoretical complexity. Prompt injection remains at the top because it exploits a fundamental property of how language models process instructions — they cannot reliably distinguish between developer-set system prompts and adversarial user input. This is a constraint of the architecture itself, not a bug waiting for a patch.
When you build a red teaming plan, the OWASP list gives you a structured framework to organize test cases. Map each category to specific entry points in your application: where does user text reach the model, what tools can the model invoke, and what data can it access. That mapping turns a generic list into a targeted test suite with clear coverage metrics.
Every AI vendor will tell you they take security seriously. The OWASP LLM Top 10 gives buyers a shared vocabulary to verify those claims. If a vendor can’t explain how they handle prompt injection or excessive agency in concrete terms, that tells you everything you need to know about their security maturity.
The list ranks risks by technical severity, but the hardest vulnerabilities to catch are the ones that require understanding human intent and social context. Automated tools can scan for prompt injection patterns, yet they struggle with attacks that exploit cultural norms, emotional manipulation, or domain-specific knowledge gaps — precisely the areas where red teaming coverage falls short.