LLM Application Penetration Test (OWASP LLM Top 10)

• CTO or VP Engineering
• Head of Security or CISO
• AI Product Lead
• Application Security Engineer

Your product now has a chat assistant, a copilot or an AI search box, built on a hosted model with a system prompt, retrieval and perhaps a few tools. Your annual pentest covered the web app, but nobody has checked whether the model can be made to reveal its instructions, read another customer's data or pass unsafe output to the rest of your stack.

We run a scoped penetration test of your LLM feature and the application around it. Testing combines manual work by an experienced tester with automated probe sets run against your endpoint, so coverage is broad and the important findings are verified by hand. We test each OWASP Top 10 for LLM Applications category that applies, from prompt injection and sensitive information disclosure to improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses and unbounded consumption. We also test the surrounding authentication, authorisation and API layer. Every finding includes a reproducible transcript or request, a severity rating, a MITRE ATLAS mapping where relevant and a specific fix. A retest confirms remediation.

• Threat model of the LLM feature: data flows, trust boundaries, tools and retrieval sources
• Direct and indirect prompt injection testing, including content placed in retrieved documents
• Data disclosure tests across users, roles and tenants
• Output handling tests where model output reaches browsers, databases or other services
• System prompt leakage and secrets exposure checks
• Findings mapped to OWASP LLM Top 10 and MITRE ATLAS, with a retest

1. 01Agree scope and rulesWe agree the feature, environments, test accounts, rate limits and stop conditions in writing.
2. 02Threat modelWe map data flows, trust boundaries, retrieval sources, tools and every place model output goes.
3. 03Automated probingProbe sets for each relevant OWASP LLM category run against your endpoint, and every exchange is logged.
4. 04Manual testingA tester works through the feature by hand, chaining weaknesses and testing business context that automation misses.
5. 05Verify and rateEach candidate issue is reproduced, false positives are removed, and severity is agreed against real impact.
6. 06Report and retestYou receive findings with evidence and fixes, and we retest after remediation.

1. Scope and threat model
   • Rules of engagement and test accounts
   • Data flow and trust boundary map
2. Test execution
   • Endpoint connector for chat API or UI
   • OWASP LLM probe library
   • Manual tester workspace
3. Observation
   • Full request and response logging
   • Token and cost tracking for consumption tests
4. Analysis
   • Reproduction and false-positive check
   • Severity rating with impact notes
   • OWASP LLM and MITRE ATLAS mapping
5. Reporting
   • Findings report with transcripts
   • Retest record

An experienced tester verifies every finding by hand before it reaches the report and agrees severity with your team. Automated probes widen coverage but never decide severity on their own.

• Your LLM gateway or chat API, on OpenAI, Azure OpenAI, Anthropic, Bedrock or self-hosted models
• Staging environment with test tenants
• Issue tracker such as Jira or Linear
• Observability tools such as Langfuse or LangSmith, read-only
• Secrets manager for test credentials

• Written rules of engagement with stop conditions
• Test accounts and test tenants only, with no access to real customer data
• Rate limits agreed to protect cost and availability
• Evidence stored encrypted and deleted after an agreed period
• No destructive actions without explicit approval

• Findings are mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS, which many enterprise buyers ask for.
• Test records can feed risk management evidence under NIST AI RMF and ISO/IEC 42001.

• SaaS & AI Product Companies

  AI Agent Red-Teaming (Tool Misuse and Goal Hijack)

  Red-team your AI agents for goal hijacking, tool misuse, excessive agency, privilege abuse and memory poisoning, in a sandbox that records every tool call and decision.

• SaaS & AI Product Companies

  RAG Data-Leakage and Access-Control Testing

  Test whether your RAG pipeline leaks documents across users, roles or tenants through retrieval, caching, citations or logs, using seeded canary documents and role-based test personas.

• SaaS & AI Product Companies

  Continuous AI Security Regression in CI

  Run versioned prompt-injection, jailbreak and data-leakage suites in CI on every prompt, model, tool or index change, and block releases that regress past agreed severity thresholds.

• Fintech & Payments

  Pre-Launch Security Assessment for LLM Features

  Test a new chatbot, copilot or LLM feature for prompt injection, cross-customer data leakage, off-policy answers and unsafe output handling before release, with evidence and a retest.