AI Red Teaming
Adversarial testing of AI systems against misuse, jailbreaks, and harmful-output scenarios.
AI red teaming is adversarial testing of AI systems against misuse — jailbreaks, guardrail bypass, and harmful-output scenarios — to show how your model behaves under determined, creative attack.
Where attackers get in — and where we look.
Jailbreak & guardrail bypass
Harmful-content elicitation
Data-exfiltration paths
Tool & agent abuse
Abuse-scenario coverage
An evidence-driven methodology.
Scope & recon
We agree objectives and rules of engagement, then map what you actually expose.
Map the attack surface
Enumerate entry points, roles, and trust boundaries a real attacker would target.
Manual exploitation
Certified testers exploit flaws by hand — chaining issues scanners never connect.
Prove impact
Findings ship with supporting evidence and business context.
Report & retest
Risk-ranked reporting with remediation guidance; retests under agreed terms.
Evidence you can act on.
Findings and supporting evidence
QA-reviewed findings with evidence and reproduction steps.
Risk-ranked reporting
CVSS + business context, prioritized for your team.
Remediation guidance
Actionable fixes mapped to each finding.
Retests under agreed terms
Retests are requested and tracked in the platform.
Make it a program.
Pair this test with a program that extends testing between engagements.
AI Red Teaming — questions buyers ask.
How is AI red teaming different from LLM pentesting?
LLM pentesting targets the application’s technical security; AI red teaming focuses on model behavior — jailbreaks, harmful content, and misuse scenarios at scale.
Do you test guardrails and safety filters?
Yes — we test how reliably guardrails hold under adversarial prompting and where they can be bypassed.
What frameworks guide the testing?
We draw on OWASP LLM, MITRE ATLAS, and emerging AI-safety practice to structure abuse-scenario coverage.
Prove what an attacker could actually do.
A short scoping call, no obligation.