Definition: OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications is a ranked, community-maintained list of the ten most critical security risks facing large language model applications, published by the OWASP GenAI Security Project to help developers, security teams, and auditors prioritize defenses.
Core characteristics of the OWASP Top 10 for LLM Applications
Hundreds of security researchers, AI engineers, and practitioners maintain the list under the open, vendor-neutral OWASP umbrella, and it is revised as new attack techniques and real-world incidents come to light.
- Ranks ten categories from LLM01 to LLM10, led by prompt injection
- Revised periodically, with 2025 marking the list’s second major edition
- Free and vendor-neutral, unlike proprietary security frameworks
- Written for developers, AppSec teams, and compliance auditors alike
OWASP Top 10 for LLM Applications vs. the OWASP Top 10 for web applications
The original OWASP Top 10 addresses classic web risks such as broken access control and injection at the HTTP and database layer. The LLM-specific list instead addresses risks unique to how models process natural-language instructions mixed with untrusted data, such as prompt injection, hallucination-driven misinformation, and unbounded resource consumption. A web application can pass every check on the original list through firewalls and input validation and still ship a chatbot that leaks customer data through a crafted prompt, since that vulnerability class barely existed before LLMs. Most enterprises running generative AI now need both checklists side by side, one for the application shell and one for model behavior itself.
Importance of the OWASP Top 10 for LLM Applications in enterprise AI
As generative AI moves from pilot chatbots to AI agents that write to production systems, Gartner predicts 25% of enterprise generative AI applications will suffer at least five minor security incidents per year by 2028, up from 9% in 2025. The OWASP list gives security and compliance teams a common vocabulary to scope AI red teaming exercises and vendor questionnaires instead of starting from scratch on every project.
Methods and procedures for OWASP Top 10 for LLM Applications
Enterprises operationalize the list through three complementary practices.
Gap assessment against each category
Security teams walk through an LLM application category by category, checking prompt handling, output rendering, and tool permissions against each of the ten risks before go-live.
- Category-by-category review referencing LLM01 through LLM10 for audit traceability
- Findings prioritized by exploitability and business impact, not list order alone
- Gaps fed into a remediation backlog owned by engineering, not just security
Automated scanning and continuous testing
Commercial and open-source scanners generate adversarial payloads mapped to specific OWASP categories and run them against a deployed model or agent on every release. This closes the gap between an annual audit and a weekly model update, and results typically feed the same reporting pipeline used for structured red teaming.
Vendor and third-party due diligence
Procurement and compliance teams increasingly cite specific OWASP LLM categories in vendor security questionnaires, asking AI suppliers to document controls against prompt injection, supply chain risk, and data poisoning before a contract is signed, much like ISO 27001 certificates are already requested from other software vendors.
Important KPIs for OWASP Top 10 for LLM Applications
Tracking a program against the list requires coverage, strategic, and quality metrics.
Coverage and testing metrics
- Category coverage: 10/10 OWASP categories actively tested
- Test cadence: full adversarial suite run on every release
- Time to remediation: critical findings fixed within two weeks
- Automated regression pass rate: above 95% between releases
Strategic risk metrics
IBM’s 2025 Cost of a Data Breach Report found 13% of breached organizations had experienced an AI model or application breach, and 97% of those lacked proper AI access controls, a gap that maps directly to unaddressed categories on the list such as excessive agency and sensitive information disclosure.
Quality and audit metrics
Mature programs track which categories generate repeat findings across releases and correlate open items with real incidents, feeding results into AI audit documentation so remediation effort concentrates on the highest-impact gaps rather than the easiest fixes.
Risk factors and controls for OWASP Top 10 for LLM Applications
Three category groups account for most real-world exposure.
Prompt injection (LLM01)
Prompt injection remains the top-ranked category across two consecutive editions of the list because it exploits a structural weakness: models process instructions and untrusted data through the same channel.
- Direct injection through user-typed input
- Indirect injection hidden in retrieved documents or emails
- Injection propagating across multi-agent handoffs
Excessive agency and sensitive information disclosure (LLM06, LLM02)
Agents with broad, standing permissions across CRM, ERP, and email amplify the damage of a single successful attack, since a hijacked instruction inherits whatever access the agent already holds. Retrieval-augmented systems without a properly scoped index can also surface confidential records to users who should never see them, independent of any attack at all.
Supply chain and governance gaps (LLM03)
Third-party models, plugins, and fine-tuning datasets introduce dependency risk similar to open-source software components. Without an AI governance process and a current inventory of every model and plugin in use, these dependencies go untracked until an incident forces a review.
Practical example
A 160-employee specialty chemicals distributor in Bavaria built an internal chatbot that answers technical questions using its safety data sheets and product specifications. Before rollout, an external AI partner mapped the application against all ten OWASP categories and found two gaps: the retrieval index returned supplier pricing to any employee who asked, and a crafted question could make the model quote outdated safety guidance as current. Both issues were fixed and retested before the tool reached the shop floor.
- Category-by-category test report kept as compliance evidence
- Retrieval index rebuilt with role-based access scoping
- Version-dated safety guidance with an explicit “last verified” flag
- Quarterly retest scheduled on every model or document-source update
Current developments and effects
The list itself, and how enterprises use it, keeps evolving.
The 2025 (v2.0) update
The OWASP GenAI Security Project added three categories in the 2025 edition to reflect agentic and retrieval-heavy architectures that barely existed when the list was first drafted.
- System Prompt Leakage (LLM07)
- Vector and Embedding Weaknesses (LLM08)
- Unbounded Consumption (LLM10), expanded from the earlier Model Denial of Service entry
Alignment with regulatory frameworks
The list increasingly appears alongside EU AI Act Article 15 resilience requirements and NIST’s AI Risk Management Framework in audit checklists, giving Mittelstand companies a concrete reference point when a regulator or customer asks how AI-specific risks were tested.
Agentic AI expanding the list’s focus
As agents gain the ability to call tools and act autonomously, categories such as excessive agency and unbounded consumption draw more attention than in earlier editions, and AI guardrails implementations increasingly cite specific OWASP categories directly in their design documentation.
Conclusion
The OWASP Top 10 for LLM Applications turns a fast-moving, unfamiliar risk landscape into a fixed reference point that developers, auditors, and vendors can all point to. Its ten categories will keep shifting as agentic architectures create new failure modes, but the underlying discipline, testing systematically instead of reactively, does not change. Companies that map every LLM-powered workflow against the list before launch catch the gaps a functional demo never reveals. As regulators and customers increasingly ask for evidence, having that mapping on file turns a compliance question into a five-minute answer.
Frequently Asked Questions
What is the OWASP Top 10 for LLM Applications used for?
It gives developers, security teams, and auditors a shared, ranked list of the most critical LLM-specific security risks to test against, replacing ad hoc or vendor-specific risk lists with one open, widely recognized standard.
How is it different from the classic OWASP Top 10 for web applications?
The classic list covers infrastructure and application-layer risks like broken access control. The LLM-specific list covers risks unique to how models process instructions mixed with untrusted data, such as prompt injection and unbounded consumption, that a firewall or input validator was never designed to catch.
Is this relevant for a company with 50 to 200 employees, or only for AI vendors that build models?
It applies to any company deploying LLM-powered chatbots or agents, not just model builders. A Mittelstand company connecting a chatbot to internal documents carries the same categories of risk, sensitive information disclosure and prompt injection especially, as a large enterprise, just at a smaller scale.
How does the OWASP LLM Top 10 relate to the EU AI Act and DSGVO?
Article 15 of the EU AI Act requires high-risk AI systems to resist attempts to exploit vulnerabilities, and the OWASP categories give a concrete way to demonstrate that resilience during a conformity assessment. Under the DSGVO, a successful attack that exposes personal data through categories like sensitive information disclosure can also trigger breach notification obligations.
What does an OWASP-mapped assessment cost and how long does it take?
A scoped assessment covering one chatbot or agent typically takes one to three weeks and costs a few thousand to low five-figure euros, depending on whether testing is manual, automated, or both, plus a smaller recurring cost for retesting after each significant update.
Do we need our own security team to run this, or can an AI partner handle it?
Most Mittelstand companies rely on their implementation partner or an external security specialist for the initial mapping and periodic retesting. Superkind scopes each AI employee’s tool access and approval checkpoints to the specific systems it connects to during rollout, which directly narrows several OWASP categories, especially excessive agency, by design rather than as an afterthought.
