Challenge AI behavior under realistic adversarial pressure.
Scenario-driven adversarial testing that evaluates how AI systems respond to misuse, manipulation, data exposure, unsafe actions, and control bypass attempts.
Security work that changes decisions.
Every engagement is designed around the risk your team needs to understand and the action it needs to take.
Evidence of control effectiveness
Prioritized AI misuse risks
Improved safety and security guardrails
Use this review when the decision matters.
The strongest time to test is before risk becomes a release blocker, customer concern, or incident.
Before a major launch, migration, or architecture change
When enterprise customers or partners request security evidence
After meaningful changes to identity, data, integrations, or infrastructure
When your team needs an independent view of real-world risk
Depth where it matters.
Clarity at every step.
We combine proven methodology with the judgment needed to uncover context-specific risk.
Threat-led AI misuse scenarios
Jailbreak and guardrail bypass testing
Agent and tool abuse validation
Cross-user and cross-tenant data exposure testing
Safety control effectiveness review
AI red-team findings workshop
Test hypotheses, not just checklists.
Frameworks support consistent coverage. The assessment remains driven by your architecture, trust boundaries, threat model, and business workflows.
- Can realistic misuse scenarios bypass policy or safety controls?
- Can encoded, multilingual, multi-turn, or indirect inputs change outcomes?
- Can an agent be manipulated into unsafe tool selection or parameter use?
- Do monitoring and escalation controls identify high-risk AI behavior?
- Scenario library mapped to system capabilities and misuse objectives
- Successful and unsuccessful bypass observations
- Control-gap analysis covering prevention, detection, and response
Real exploitation mindset.
Responsible delivery.
Testing is scoped, evidence-led, and designed to help your team reduce risk without creating unnecessary disruption.
Context first
We learn the system, users, trust boundaries, and business objectives before testing begins.
Expert-led testing
Automation supports coverage. Human analysis finds the issues that depend on judgment and context.
Clear prioritization
Findings are ranked by exploitability, impact, and what matters to your organization.
Remediation partnership
We stay engaged through fixes, answer engineering questions, and validate closure.
Useful during remediation.
Defensible after it.
Reports are written for the people who need to make decisions, fix issues, and demonstrate improvement.
What does AI Red Teaming include?+
The engagement is scoped around your environment and objectives, then combines expert-led testing, evidence-based findings, prioritized remediation, and a final readout.
Do you rely only on automated tools?+
No. Tools support coverage and efficiency, but findings are reviewed and validated by security practitioners before they are reported.
Can you support remediation and retesting?+
Yes. We clarify findings with your team, answer implementation questions, and retest agreed fixes to document final status.
Ready to strengthen your ai red teaming?
Tell us what you are building, changing, or concerned about. We will help you define the right security review.