Autonomous adversarial testing across your models, RAG pipelines and agentic systems
AI red teaming means attacking your own AI the way an adversary would: through prompts, context and tools rather than ports and payloads. Hexashield AI makes this continuous and autonomous. With only minimal business context, it starts generating adversarial prompts and testing whether your AI can be broken.
Because Hexashield connects at different layers of an agentic environment - directly to the AI model and to the MCP servers that give agents their tools - it tests how the whole system behaves, not just the chat window. Every finding lands on a live dashboard and in the GRCortex AI risk register, so each weakness becomes a tracked, owned risk rather than a line in a stale PDF.
Security programs built for traditional applications were never designed to test how an AI model responds to manipulation.
AI changes every sprint - new prompts, data sources, tools and agents - so a yearly test is out of date almost immediately.
Pen tests, SAST and WAF look at ports and payloads. They cannot see prompt injection, jailbreaks or data leakage.
Agentic AI widens the attack surface: agents call tools through MCP servers and act on enterprise data.
Manual red teaming cannot keep pace across a growing number of AI applications.
Give Hexashield a short description of what the AI application does and who uses it. It generates adversarial prompts tailored to that context and works to break the model, covering attack categories such as prompt injection, jailbreaks and data leakage.
Hexashield connects directly to the AI model or to MCP servers inside an agentic environment. This lets it penetrate each layer and check how models, tools and context behave under attack.
Testing draws on Cygeniq's own adversarial library of more than 400,000 scenarios, which continues to grow.
Hexashield measures AI behavior at a much more granular level than the OWASP LLM Top 10. Findings are also mapped to OWASP LLM Top 10, OWASP Agentic risks and MITRE ATLAS for audit credibility.
Tests run as your AI changes. Runtime findings feed the next test cycle, and results flow to a live dashboard and into the GRCortex AI risk register.
Define the AI application in scope, its business context and the rules of engagement.
Map the model, data sources, tools, MCP servers and agents that make up the application.
Hexashield generates and runs adversarial prompts across the model and agentic layers.
Check whether existing guardrails hold up under attack.
Executive and technical findings, mapped to frameworks, with remediation recommendations.
Fixes are retested, and testing continues as the application changes.
A bank runs a customer-facing loan assistant. It is an LLM application connected to a RAG knowledge base of policy documents and to an MCP server that looks up customer records. The security team gives Hexashield a one-line description: a customer-facing assistant that answers loan eligibility questions.
Hexashield generates adversarial prompts tailored to a lending context and tests both the model and the MCP server. It finds that a crafted prompt injection can make the assistant reveal customer personal data retrieved through the MCP server.
The finding appears on the live dashboard, mapped to the relevant OWASP LLM Top 10 categories, and flows into GRCortex AI, where its residual risk is scored against financial, reputational and regulatory impact. The team tightens the tool permissions, Hexashield retests, and the risk is marked resolved.
Prompt injection, jailbreak and data-leakage issues are found before real users or attackers find them.
Continuous testing keeps pace with every change to prompts, data, tools and agents.
Findings are mapped to OWASP LLM Top 10, OWASP Agentic and MITRE ATLAS.
A live view of AI risk over time instead of a one-off report.
Continuous assurance that production AI is being tested, with evidence to show the board.
Actionable findings on the model and its tools before and after release.
Consistent coverage across LLM, RAG and agentic applications.
Red-team findings flowing directly into the AI risk register.
Autonomous, continuous adversarial testing for LLM apps, RAG pipelines and agentic AI, mapped to OWASP and MITRE ATLAS.
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