The OWASP Top 10 for LLM and Generative AI Applications organizes the risks that recur most often across real LLM application incidents into a shared vocabulary security teams, developers, and auditors can all use in common. Its value is similar to the original OWASP Top 10 for web applications: not a complete, exhaustive risk catalog for every possible system, but a widely recognized starting point that cuts the chance a review process forgets a category of risk that shows up again and again in practice.
Why a shared vocabulary matters more than the specific list
Before this kind of guidance existed, different teams working on LLM security tended to use inconsistent terminology for the same underlying risks, one team's "instruction override" and another's "context manipulation" describing essentially the same prompt injection phenomenon. A shared vocabulary makes it possible to compare findings across teams, talk to external auditors or customers using terms they'll recognize, and build training materials and review checklists that don't need reinventing independently by every team hitting these risks for the first time.
Using the list as a review structure, not a compliance checkbox
The most effective use of this guidance treats each risk category as a prompt for a specific review question against your actual system: does this application filter untrusted content before it reaches the model, does this agent's tool access respect least privilege, does generated output get validated before reaching a sensitive destination. Treat the list as a checkbox exercise instead, confirming each category has been "addressed" without ever engaging with the specific question for your specific architecture, and you get a superficial review that misses the actual risk the category was meant to surface.
Excessive agency deserves particular attention for agentic systems
Among the categories in this guidance, excessive agency, granting a model or agent more autonomy, permissions, or functionality than its task requires, connects most directly to the tool authorization and least privilege discussions running throughout AI security guidance generally. Organizations building agents with real tool access should treat this category as a primary review focus, since it's exactly where the gap between "the model was manipulated" and "the manipulation caused real damage" gets decided.
The guidance evolves as the field does
Because LLM application risks are a relatively young and fast-moving area, this kind of guidance gets updated far more often than long-established frameworks in more mature security domains. Treat a specific version of the guidance as a snapshot, not a permanent reference, and check periodically for updates, since new risk categories or a refined understanding of existing ones can shift what a thorough review should specifically be looking for.
Combining this guidance with broader risk frameworks
This OWASP guidance focuses specifically on application-level, mostly technical risks. It works well alongside broader governance frameworks like the NIST AI Risk Management Framework, which covers organizational accountability and risk management process more broadly. Use both together, OWASP's guidance for technical review depth and NIST's framework for organizational structure, and you get a more complete picture than either gives alone, since neither one was ever meant to fully replace the other.
Communicating findings using recognized terminology
When a security review turns up a gap, framing the finding using this guidance's recognized terminology, rather than some internally invented description, helps when talking to stakeholders who may already know the category from external training, vendor documentation, or industry discussion. That's a practical communication benefit separate from the guidance's substantive content: shared vocabulary cuts the translation work needed to get a finding understood and acted on.
Applying the guidance proportionally to system risk
Not every LLM application needs the same depth of review against every category here. A low-reach internal tool with no tool access can move through a review of these categories fairly quickly, focusing mainly on data exposure and prompt injection categories relevant even to a text-only system. A customer-facing agent with tool access deserves a thorough review against every category, particularly excessive agency, insecure output handling, and supply chain risks, since the consequence of a gap scales right along with the system's actual capability and exposure.
Where TELEON fits
Several categories in this guidance, excessive agency, insecure output handling to some extent, and the monitoring implied by several other categories, connect directly to what TELEON's runtime policy enforcement and audit trail provide when deployed through the gateway or supported middleware: authorization at the point of action and a record of what was requested, decided, and executed.
Working through the review questions this guidance raises for prompt handling, retrieval design, and supply chain evaluation remains application-specific work that has to be done by the team building and reviewing the system.
A short checklist
- Use the guidance as a structured set of review questions for your
specific architecture, not a checkbox exercise.
- Give particular attention to excessive agency for any system with real
tool access.
- Check for updated versions of the guidance periodically, since it
evolves with the field.
- Combine this technical guidance with a broader governance framework
like the NIST AI RMF.
- Use the guidance's recognized terminology when communicating findings
to stakeholders.
- Scale review depth to the system's actual risk and capability.
The OWASP Top 10 for LLM and Generative AI Applications earns its value as a shared vocabulary and review structure applied thoughtfully to a specific system's actual architecture, not as a document to be checked off without engaging with the specific question each category raises.