SaaS teams shipping an AI assistant
Enterprise buyers now ask about AI in security questionnaires. An independent test report answers them.
What buyers look forLLM apps · RAG · AI agents · OWASP LLM Top 10
LLM and AI application penetration testing from Sumrite, before your users test it for you.
Your AI assistant can read documents, call APIs and act for your users. Sumrite tests what an attacker can make it do: leak another customer’s data, run a tool it should not, or turn its output into an injection, together with the APIs and cloud behind it.
OWASP LLM Top 10Every risk from prompt injection to unbounded consumption is in scope.
Whole attack pathThe model layer is tested with the APIs, identity and cloud services around it.
Measured, not anecdotalEach finding records how often the attack works, with exact prompts to reproduce it.
LLM application penetration testing is a security test of a product feature built on a large language model, such as a chatbot, a retrieval-augmented (RAG) assistant or an AI agent. Sumrite attacks the model layer with direct and indirect prompt injection, data extraction, tool abuse and unsafe output, maps every finding to the OWASP Top 10 for LLM Applications, and tests the surrounding application, APIs and cloud in the same engagement, with a retest included.
What an LLM penetration test covers
| Risk | What Sumrite tries |
|---|---|
| LLM01 Prompt injection | Direct jailbreaks and indirect injection hidden in documents, web pages, emails and tool results the model reads |
| LLM02 Sensitive information disclosure | Extracting other users’ data, internal documents, credentials or personal data through questions and summaries |
| LLM03 Supply chain | The models, plugins, SDKs and third-party APIs in use, and where they come from |
| LLM04 Data and model poisoning | Whether users can influence fine-tuning data, feedback loops or the shared knowledge base |
| LLM05 Improper output handling | Getting the model to emit script, SQL, shell commands or links that the application renders or runs |
| LLM06 Excessive agency | Triggering tools and actions such as refunds, emails or admin calls without real approval |
| LLM07 System prompt leakage | Recovering hidden instructions and checking them for secrets or access logic |
| LLM08 Vector and embedding weaknesses | Cross-tenant retrieval and missing permission filters on the vector store |
| LLM09 Misinformation | Confidently wrong answers in pricing, policy or other high-stakes flows |
| LLM10 Unbounded consumption | Prompts and request patterns that run up cost or exhaust quotas |
Who needs AI application testing
Products with RAG over customer data
Retrieval that ignores permissions is the most common serious finding. Test it before a tenant sees another tenant’s files.
A related case studyAgents that take actions
If the model can send, change or pay, every tool it can call is part of the attack surface.
API security testingHow Sumrite runs an LLM penetration test
- Threat model the feature. Who uses it, what data it can see, what actions it can take and the worst realistic outcome.
- Map data and tool flows. Inputs, retrieval sources, tools, output destinations and the permissions at each step.
- Test the conventional layer. Authentication, access control, APIs and cloud configuration, with the same methods as a web application and API test.
- Attack the model layer. Prompt injection, data extraction, tool abuse, output-handling payloads and cost abuse, mapped to the OWASP LLM Top 10.
- Measure and report. Successful attacks are repeated to record a success rate; each finding has exact prompts, impact, root cause and a fix.
- Retest. Fixes are retested against the same attacks and the report is updated.
What you receive
- An executive summary in business terms: what data or action an attacker could reach
- Each finding with the prompts, documents or payloads used, the model and version, and how often it worked
- Root cause and fixes that hold: access control before retrieval, smaller tool permissions, confirmation for risky actions, output treated as untrusted, rate and cost limits
- Mapping to the OWASP Top 10 for LLM Applications, and to SOC 2, ISO 27001 or the questionnaire your buyer sent
- A retest of fixed findings and an updated report you can share with customers
Frequently asked questions
What is LLM application penetration testing?
A security test of a product feature built on a large language model. It checks what an attacker can make the feature do, such as leaking data, calling tools without approval or producing output that the application executes, and tests the APIs and cloud around it.
Do you test the model provider, such as OpenAI or Anthropic?
No. You cannot test the provider’s platform and do not need to. Sumrite tests how your application uses the model: its prompts, retrieval, tools, permissions and output handling.
How is this different from a normal web application pentest?
The conventional application is still tested, but the model adds new inputs (any text it reads), non-deterministic results and actions it can take. Findings therefore include success rates and exact prompts.
Is AI red teaming the same thing?
AI red teaming usually focuses on model behaviour and harmful output. An LLM penetration test focuses on security impact in your product: data exposure, unauthorised actions and injection. Sumrite can combine both, and larger programmes can add red team operations.
Is a retest included?
Yes. Fixed findings are retested against the same attacks and the report is updated.
Related
Shipping an AI feature?
Tell us what your assistant can see and do. NDA first, fixed-scope quote, retest included.