LLM red teaming for AI applications and agents.
Chatbots, copilots and autonomous agents add an attack surface that traditional testing does not cover. Cyrion’s AI Red Team probes how your model, prompts, tools and data sources behave under adversarial input.
Agents build a model of your target before testing it, instead of sweeping it with signatures.
Individually minor weaknesses are combined into end-to-end attack paths.
A second agent re-attempts each issue in a sandbox before it reaches your report.
Reproduction steps, request logs and framework mappings in every report.
What the agents
test.
Every module shares one memory of your environment, so a finding on llm & ai agents fuels hypotheses on the next surface.
Prompt injection
Direct and indirect, including payloads hidden in retrieved documents and web pages.
Jailbreaks
System-prompt extraction and guardrail bypass.
Data leakage
Sensitive data exposed from context windows, RAG indexes and logs.
Excessive agency
Tools and actions an agent can be tricked into taking.
Insecure output handling
Model output that reaches a browser, shell or database.
Governance mapping
OWASP LLM Top 10, NIST AI RMF and EU AI Act obligations.
From a poisoned document to a tool call.
The risk is rarely the model alone. It is what the model can reach once an attacker controls its input.
How an engagement
runs.
The same loop a human red team follows, recon, hypothesis, exploit and debrief, running continuously instead of once a year.
Catalogue the prompts, tools, retrieval sources and output sinks of your AI application.
Plan direct and indirect injection, extraction and tool-abuse attacks.
Run adversarial inputs and confirm what the model leaks or does.
Findings mapped to the OWASP Top 10 for LLMs and the NIST AI RMF.
Frameworks
Findings map to the OWASP Top 10 for LLM Applications, the NIST AI Risk Management Framework and EU AI Act obligations, so they can feed straight into your AI governance process.
Indirect prompt injection triggers a privileged tool call
What you get.
- Reproduction steps with the exact requests and responses for each finding.
- Severity based on demonstrated impact, not a signature match.
- Remediation guidance your developers can act on.
- An auditor-ready report you can share with customers and assessors.
Test llm & ai agents the way an attacker would.
Start with a free demo scan, or tell us what you need to cover and we will scope an engagement.
Related reading.
OWASP Top 10 for LLM Applications: What Security Teams Need to Know
A practitioner's walkthrough of the OWASP LLM risk categories, with concrete examples of how each shows up in production.
What Is Agentic Penetration Testing? A Practical Guide
Why chaining autonomous reasoning agents finds attack paths that scanners and one-off pentests miss — and how validation keeps it safe.
Continuous Attack Surface Management: Why Point-in-Time Pentests Aren't Enough
Production changes weekly. Your security testing cadence probably doesn't. Here's what closing that gap actually requires.