Offensive testing for production AI
We find exploitable weaknesses in AI applications before they become data exposure, unauthorized access, or unsafe action.
Services & attack surface
Choose the area closest to your architecture. When an attack path crosses identities, data, models, and tools, we test those boundaries together.
Adversarial testing of conversations, system instructions, memory, guardrails, session state, and application logic.
You are launching or operating a customer-facing or internal LLM application.
Testing of agent planning, tool selection, MCP client and server trust, generated parameters, identity propagation, and permissions.
Your AI can call tools, change data, access internal systems, or connect to MCP servers.
Assessment of document ingestion, vector search, retrieval filters, source permissions, tenant isolation, and private context.
Your application searches private, tenant-scoped, or user-supplied knowledge.
Application and API testing of AI-mediated requests, acting identities, object permissions, transaction controls, and business logic.
Model-generated calls can reach customer data, privileged APIs, or business transactions.
Review of model provenance, serialization, loaders, adapters, dependencies, runtime isolation, and deployment configuration.
You import third-party artifacts or operate models in your own infrastructure.
Focused validation of input and output filters, policy engines, confirmations, approval steps, and human-in-the-loop controls.
You need evidence that a safety or security control remains effective under attack.
Methodology
The work is system-led, human-driven, and evidence-based. We focus on weaknesses that can be reproduced and connected to real impact.
We review trust boundaries, identities, permissions, data flows, integrations, and operating constraints to target the paths that matter in your system.
Automation provides coverage. Human testers vary inputs, chain weaknesses, change roles and context, and follow the system's actual responses.
We validate the affected identity, data, action, or runtime boundary within agreed safety limits and record the evidence needed to reproduce it.
Findings mapped to relevant controls and attack techniques.
LLM, Agentic AI, and API security guidance
AI Risk Management Framework
Adversarial tactics and techniques for AI systems
Risk and governance context
Mapping supports governance. It does not guarantee compliance or certification.
Deliverables
One set of outputs for leadership, security, and the engineers responsible for remediation.
An executive summary, attack-surface view, and findings ordered by credible impact.
Affected components, attack paths, transcripts, proof of impact, and reproduction steps.
Engineering guidance for prompts, application logic, permissions, APIs, and architecture.
Confirmation that the original path is closed, including relevant bypass and regression checks.
We scope around the architecture, reachable tools and data, testing access, environment, and the decisions or actions the system can make. After an initial review, you receive a defined scope, rules of engagement, timeline, deliverables, and price.
Timing depends on the number of components, roles, integrations, environments, and attack paths in scope. A focused review is shorter than a multi-agent or multi-tenant platform assessment; the schedule is agreed before work begins.
Production testing is possible when risk is controlled through explicit boundaries, dedicated identities, rate limits, monitoring, stop conditions, and coordinated windows. Techniques with unacceptable operational risk are moved to staging or a representative test environment.
Usually an architecture walkthrough, test accounts for relevant roles, environment access, API or tool documentation, and a technical contact. Logs and source access improve depth but are agreed according to the chosen black-, gray-, or white-box approach.
Confidentiality, evidence handling, storage, transfer, retention, and deletion requirements are agreed before testing. Access follows least privilege, and evidence is limited to what is necessary to reproduce and validate a finding.
Yes, when the testing is authorized and permitted by the provider's terms. The assessment can cover your integration, prompts, data flows, retrieval, permissions, and application controls; provider infrastructure remains out of scope unless separately authorized.
No. AI red teaming complements application, API, cloud, and infrastructure testing by focusing on model-mediated trust and action. Framework mapping supports governance conversations but does not provide certification or guarantee compliance.
Security inquiry
Start with the essentials. We’ll use this context to understand fit and respond with the right next step.