Organizations are deploying AI faster than they can secure it. Every LLM integration, AI agent, and automated workflow is a new attack surface — and adversaries are already exploiting them. We help you move fast without leaving your defenses behind.
The rise of large language models, AI agents, and LLM-powered applications has created a category of security risks that traditional controls weren't designed to handle. Prompt injection, training data poisoning, model inversion, insecure output handling, and supply chain attacks against AI components require a fundamentally different security approach — one that understands how these systems work, not just how to patch CVEs.
VANGUR AI's AI security practice combines offensive research, architecture review, and governance frameworks to help organizations deploy AI responsibly and securely. Whether you're building proprietary models, integrating third-party LLM APIs, or deploying AI agents with access to sensitive systems, we assess the risk, design the controls, and monitor the deployment — so your AI remains an advantage, not a liability.
Our AI red team simulates the full range of adversarial attacks against your LLM deployments — prompt injection, jailbreaking, indirect injection via RAG sources, model extraction, and data exfiltration through model outputs. Every finding comes with a remediation path.
We help organizations establish AI security governance frameworks — covering model risk management, acceptable use policies, output validation controls, human oversight requirements, and third-party AI vendor assessment — so security keeps pace with the speed of AI adoption.
Assessment and remediation of direct and indirect prompt injection vulnerabilities in LLM-powered applications — including RAG pipeline attacks, tool-use manipulation, and multi-agent injection chains.
Evaluation of model security posture — covering training data validation, fine-tuning supply chain risks, model inversion defenses, and membership inference attack mitigations.
Structured adversarial testing of your AI systems by security researchers who specialize in LLM attack techniques — delivering a prioritized list of exploitable vulnerabilities before attackers find them.
End-to-end AI security governance: risk classification, model inventory, acceptable use policies, output monitoring requirements, and incident response procedures tailored to AI-specific failure modes.
Security assessment of third-party LLM providers, AI APIs, and AI-enabled SaaS tools — evaluating data handling, model access controls, prompt logging, and contractual security obligations.
Security architecture review and runtime monitoring for autonomous AI agents with access to tools, APIs, data stores, and external services — containing the blast radius of compromised or manipulated agents.
Let's assess your AI attack surface before adversaries do.
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