We perform targeted penetration testing on Large Language Models (LLMs), AI agents, and GenAI applications to uncover real-world attack paths — including prompt injection, data leakage, model manipulation, and cross-system abuse.

Our methodology is built around the OWASP Top 10 Risks for LLMs and GenAI Applications, ensuring comprehensive coverage of threats such as prompt injection, data poisoning, sensitive information disclosure, insecure output handling, supply-chain risks, and more.

Combining deep adversarial testing with engineering validation, we provide a clear picture of your model’s resilience, exposure, and trust boundaries.

​

Our testing covers:

• Prompt injection and indirect prompt manipulation

• Sensitive data leakage (training, memory, or vector stores)

• RAG and retrieval logic abuse

• Model jailbreaks, prompt re-framing, and sandbox escapes

• Agent orchestration, workflow logic, and API abuse

 

Deliverables include:

• Executive risk summary aligned with OWASP LLM Top 10 categories

• Technical findings with reproducible PoCs and severity ratings

• Secure design recommendations and model-specific mitigations

• Optional retesting after remediation