Test AI systems before users and attackers do.
Expert-led security testing for AI-enabled applications, LLM workflows, copilots, agents, retrieval systems, and the APIs that connect them.
Security work that changes decisions.
Every engagement is designed around the risk your team needs to understand and the action it needs to take.
Validated AI application risks
Safer model and tool integration
Practical AI security controls
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.
Prompt injection and instruction hierarchy testing
Sensitive data and system prompt exposure review
RAG and vector-store access control testing
Tool-use and agent authorization review
Model abuse and unsafe output analysis
AI application threat modeling
Test hypotheses, not just checklists.
Frameworks support consistent coverage. The assessment remains driven by your architecture, trust boundaries, threat model, and business workflows.
- Can direct or indirect prompt injection alter intended behavior?
- Can users retrieve system prompts, sensitive context, or another tenant’s data?
- Can model output reach downstream interpreters without safe validation?
- Can tools, agents, or workflows act beyond the requesting user’s authority?
- Adversarial prompt and retrieval test cases
- Data-flow and trust-boundary findings for models, tools, and stores
- Guardrail effectiveness observations with repeatable scenarios
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 & LLM Security Testing 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 & llm security?
Tell us what you are building, changing, or concerned about. We will help you define the right security review.