// 03 — Solution
AI systems introduce attack surfaces that conventional security testing frameworks were never designed to handle. We assess large language model applications, retrieval-augmented generation pipelines, autonomous AI agents, and model supply chains against the latest adversarial techniques before your users or regulators find the gaps.
Discuss This EngagementComplete security assessment of GenAI-powered features including chatbots, copilots, and AI-augmented workflows. Coverage includes OWASP LLM Top 10, prompt injection, data extraction, system prompt leakage, and web and API layer vulnerabilities surrounding the model.
Focused evaluation of retrieval-augmented generation architectures. Tests for indirect prompt injection via ingested documents, cross-tenant data leakage in shared vector stores, embedding manipulation, and retrieval bypass techniques.
Purple-team evaluation of autonomous agents and tool-using systems. Assesses excessive agency, function-calling abuse, unsafe tool chaining, and privilege escalation paths that emerge when LLMs are given access to external systems and APIs.
Multi-week black-box engagement simulating external adversaries targeting your AI infrastructure. Uses objective-based methodology with multi-vector chained attacks and full MITRE ATLAS coverage to expose systemic risks beyond individual model vulnerabilities.
Security audit of model hosting infrastructure, serialised model files, and third-party AI dependencies. Covers pickle and safetensors remote code execution risks, HuggingFace dependency validation, and insecure model serving configurations.
Hands-on security training for engineering and product teams building AI systems. Covers threat modelling for LLM applications, secure prompt engineering patterns, abuse-case analysis, and integrating AI security checks into your development lifecycle.