AI agents can act.
Can your security model keep up?
When a model can search, call tools, read private data and change real systems, the security boundary is no longer just the prompt. We examine how your AI application crosses trust, identity and execution boundaries — and validate realistic attack paths.
Defined scope · Authorization before testing · Reproducible evidence
The system surrounding the model.
Untrusted inputs → privileged actions
Indirect prompt injection, retrieval poisoning, adversarial instructions in documents and tool outputs, and action authorization gaps.
Agent execution surfaces
MCP-connected tools, API capabilities, data exfiltration paths, permission boundaries, and unsafe compositions of legitimate tool calls.
Memory, credentials and persistence
Long-term context manipulation, RAG/memory poisoning, user-to-user isolation, secrets exposure, and delegated identity assumptions.
From architecture to demonstrated impact.
Agree on the agent, target environment, test accounts, allowed actions, sensitive boundaries and stop conditions.
Map untrusted sources, tool access, permissions and security controls to the decisions your agent can make.
Test high-value hypotheses, confirm reachability and document what an attacker can actually cause.
Prioritize architectural fixes, provide reproductions where safe, and review the evidence with your engineers.
Actionable evidence. Not a theater report.
Verified attack paths
Entry point, assumptions, preconditions, observed behavior and real-world impact — with limitations documented.
Engineering remediation
Specific boundary changes, authorization controls, containment and validation guidance mapped to your system.
Decision-ready summary
Clear severity rationale and a debrief that lets technical and business owners choose what to fix first.
Before we begin.
Do we need to share our model weights or proprietary methodology?
No by default. Most assessments begin with architecture, test access and agreed behavior. Additional source or configuration access is defined during scoping only when required.
Is this only prompt-injection testing?
No. Prompt injection is one mechanism. We test the boundaries between model outputs, tools, authorization, memory, data sources and infrastructure.
Will testing disrupt production?
We agree on test environments, rate limits, sensitive assets and stop conditions before testing. High-impact validation is performed only within explicitly authorized constraints.
What happens after a finding is confirmed?
We provide reproduction details, supporting evidence, realistic impact, fix guidance and a discussion with the responsible engineering team.
Bring your agent architecture.
We will bring the adversarial questions.
Start with a 30-minute scoping discussion. We will identify the target, trust boundaries and what a useful assessment would deliver. No commitment before a defined scope.