AI & LLM APPLICATION SECURITY
Testing prompt injection resistance, RAG data leakage, autonomous agent permissions, and Generative AI application security.
Is this right for you?
When to choose this service
AI & LLM Assessment Vectors
- Direct and Indirect Prompt Injection payload testing
- RAG (Retrieval-Augmented Generation) vector store authorization and tenant isolation
- System prompt extraction, memory inspection, and sensitive data leakage mitigation
- Autonomous AI agent privilege boundaries and downstream API execution control
What you receive
- AI-system threat model and trust-boundary diagram
- Abuse-case library with repeatable tests
- Findings separating application defects, model limitations and governance gaps
- OWASP LLM/GenAI and MITRE ATLAS mapping where relevant
- Security requirements for agent permissions, RAG and data handling
- AI security regression suite and retesting cadence
Delivery flow
From scope to a verified result
Scope and safety boundaries. Confirm the objective, systems, roles, environment, exclusions, authorised actions and emergency stop contact.
Information and access. Receive only the documentation, accounts, configuration or evidence needed for the work through a secure channel.
AI & LLM Threat Modeling. We evaluate prompt injection risks, training data leakage, and model interface security controls.
Validation and reporting. Confirm findings, remove false positives and connect each risk to business impact and an accountable owner.
Workshop and follow-through. Explain priorities, answer delivery teams, agree remediation timing and perform a retest where included.
Before we start
Frequently asked questions
How long does an engagement usually take?
Timing depends on the number of systems, roles, environments, available documentation and agreed constraints. After initial information is received, the scope states the stages, customer involvement and a specific schedule.
What should we prepare before work starts?
Usually we need a system or process owner, current scope, access and test accounts, architecture or process information, critical business scenarios and an emergency contact. Never send passwords through a normal website form.
Will we receive only a technical report?
No. The standard output includes an executive summary, prioritised detail, evidence, remediation guidance and a results workshop. Where relevant, the engagement includes a retest or implementation roadmap.
Can we guarantee that a model will never disclose data?
No. Testing reduces known scenario risk and validates system controls, but model behaviour is not fully deterministic. Sensitive-data access must be restricted architecturally, tool actions constrained and real use monitored.
Is testing the public chat interface enough?
No. The assessment should also cover system prompts, RAG retrieval, document ingestion, model/API configuration, tool permissions, memory, logs, tenant isolation and downstream applications consuming model output.
Related next steps
API Security & Stress Testing
In-depth penetration testing of REST, GraphQL, and gRPC endpoints across multiple authentication tokens and multi-step workflows.
Code Review & DevSecOps
Manual and automated static code analysis, SAST/SCA tool calibration, and automated security gates in your CI/CD pipeline.
Licensing & products
We supply enterprise licenses and competitive procurement for top global security vendors (EDR, SIEM, WAF, email, and identity protection) alongside technical integration support.
Contact
Book a 30-minute discovery call or submit a scoping request directly to our senior security engineers.