Find your AI's weaknesses before someone else does
Standard penetration tests were not built for generative AI. We test the attack surface that is, automatically and continuously.
Someone will find your AI's weaknesses. The question is who.
If you run an internal AI assistant, a chatbot, or anything wired into your models, it is worth asking how it is being tested. The vulnerabilities in an AI system get discovered eventually. Red teaming is how you make sure you are the one who finds them.
Unseen deploys the prompts and injection techniques a real attacker would, and simulates the scenarios that actually cause damage: data pulled out, actions taken without authority, and business logic bent for gain.
Attack vectors we test
Prompt injection
Hidden instructions in content that hijack the model’s behavior.
Jailbreaks
Prompts that talk the model out of its own guardrails.
Data exfiltration
Coaxing the system into revealing data it should never return.
Context manipulation
Poisoning the surrounding context to change an answer.
Privilege escalation
Getting the AI to act beyond the permissions it was given.
Business logic abuse
Forcing refunds, discounts, or access it should refuse.
PII extraction
Pulling personal data out of the model or its connected sources.
Guardrail bypass
Finding the gap between the written policy and what actually gets blocked.
What you get back
Automated and continuous
Tests run on a schedule, not once a year, so new weaknesses are caught as your AI changes.
Reports you can act on
Each finding comes with the exact prompt, the impact, and what to change, not just a severity score.
Tied to your controls
Findings map back to the guardrails and policies in your gateway, so a fix is a setting, not a project.
Where the fixes live: