Every enterprise team shipping an LLM-powered feature is quietly exposed to a set of risks that did not exist in traditional application security. The OWASP LLM Top 10 is the closest thing the industry has to a standard checklist for these risks, and most teams building production AI features have not actually read it end to end.
Why This List Exists
Traditional application security assumes an attacker is trying to break the code. LLM security has to assume an attacker is trying to talk the system into breaking itself, through a crafted prompt, a poisoned document in a retrieval pipeline, or a plugin call the model was never supposed to be able to make. The OWASP LLM Top 10 organizes these risks so a team can test for them systematically, before discovering each one the hard way in production.
The Risks That Show Up Most in Real Builds
- Prompt injection: a user, or a document the system reads, contains instructions designed to override the system’s original behavior.
- Insecure output handling: the model’s response gets passed directly into a downstream system, a database query, a shell command, without the validation any other user input would get.
- Sensitive information disclosure: the model reveals training data, system prompts, or other users’ data it should never have surfaced.
- Excessive agency: the model is given more permission to take action than the use case requires, so one bad output can do real damage well beyond looking wrong on screen.
{{LINK2:AI Application Security Testing}} Has to Be Different Too
Standard penetration testing tools were not built for this threat model. Testing an LLM feature properly means red-teaming the prompts themselves, checking what happens when a user tries to extract the system prompt, and verifying the model’s output gets sanitized before it touches anything else in the stack. Ariel Software Solutions builds OWASP LLM Top 10 awareness and AI application security testing into its AI development process from the start, informed by sixteen years of production software delivery and an ISO 9001:2015 certified engineering process based in Mohali, Punjab, with a US office in Sheridan, Wyoming.
Where Teams Should Start
Teams shipping their first LLM feature should not try to defend against all ten risks on day one. Start with the two that cause the most damage for the specific use case, usually prompt injection and excessive agency for anything that takes action on a user’s behalf, and build the testing discipline outward from there. A checklist read once in a planning meeting rarely changes behavior on its own; a checklist run against every release does.
Building Security Into the Development Process From Day One
Security work that arrives after a feature is already built tends to get scoped down to whatever fits the remaining timeline, which is exactly backward for a threat model this dynamic. Teams that get the best results treat prompt and output testing as part of the same review cycle as any other code change, so a new tool integration or a new retrieval source gets the same scrutiny as a new API endpoint would.
That review has to be genuinely adversarial, run by someone other than the person who wrote the feature. A second set of eyes actively trying to break the system, feeding it edge-case prompts, malformed documents, requests designed to extract configuration it should never reveal, catches a category of failure a friendly functional test never will.
The cost of building this in from the start is real but small compared to the alternative: retrofitting security review onto a system already handling production traffic, where every fix now has to account for behavior real users have already come to depend on.
What This Means for Procurement and Vendor Selection
Enterprises buying an AI feature from a vendor should ask directly which of these ten risks the vendor has actually tested against, and ask for evidence: a red-team report, a documented testing process, something more concrete than a verbal assurance that security was “considered.” A vendor who cannot name specific tests it ran is either new enough to this threat model that it has not built the discipline yet, or has simply not prioritized it, and either answer is worth knowing well before a contract gets signed.
