Service
LLM & AI Security
Authorized security assessments of LLM-powered and AI-enabled applications, focused on prompt injection, model abuse, sensitive data exposure, and the authentication, authorization, and backend risks introduced by AI features.
What We Test
Coverage areas applied through manual validation and tool-assisted analysis.
- AI application security assessment across user, system, and tool boundaries
- Prompt injection (direct and indirect) and jailbreak resistance
- Model abuse scenarios including misuse, denial of wallet, and unsafe output handling
- Sensitive data exposure through prompts, embeddings, logs, and training context
- Insecure plugin, tool, and function-calling integrations
- Authentication and authorization issues in AI-enabled workflows and agents
- API and backend risks connected to AI features (SSRF, IDOR, secret exposure)
- Retrieval-augmented generation (RAG) data poisoning and source trust boundaries
- Excessive agency, unsafe automation, and over-permissioned AI actions
Methodology
Assessments are aligned with the OWASP Top 10 for LLM Applications and adapted to your specific architecture, including model providers, orchestration layer, tool integrations, and downstream systems the AI can reach.
We combine manual adversarial prompting with targeted testing of the surrounding application, APIs, and trust boundaries — confirming exploitability against live behavior before reporting.
Deliverables
What you receive at the close of the engagement.
- Executive summary
- Technical findings with verified reproduction steps
- Impact, severity, and business risk ratings
- Evidence including prompts, responses, requests, and screenshots
- Prioritized remediation guidance for application and model layers
- Optional retest summary