Your model is only as secureas the system around it.
Shipping an LLM feature introduces a class of risk your existing security tooling was never built to catch — prompt injection, data exfiltration through model output, and agents that take actions no one reviewed. We help engineering teams find those gaps before an attacker does.
A methodology built around how LLM systems actually fail.
AI security is a different discipline than application security.
A traditional web app has a fixed set of inputs and a predictable code path. An LLM-backed feature doesn’t — it accepts open-ended natural language, often from untrusted users, and turns that input into decisions, database queries, or tool calls. That gap between "what the model was told to do" and "what it can be tricked into doing" is where most AI-specific incidents actually happen, and it sits outside what a conventional penetration test or WAF is designed to catch. We work with engineering teams to close that gap at the architecture level, not just patch individual prompts after something goes wrong.
What an AI security review actually covers.
We organize every engagement around the OWASP Top 10 for LLM Applications, mapped to the parts of your stack where each risk actually lives.
Input & prompt layer
- Prompt injection (direct & indirect)
- Jailbreak & guardrail bypass testing
- Insecure output handling downstream
- Training & fine-tuning data poisoning checks
Orchestration & agent layer
- Excessive agency & tool-call scoping
- Insecure plugin / function design
- Human-in-the-loop gate review
- Multi-agent trust boundary mapping
Data & infrastructure layer
- Sensitive information disclosure
- RAG & vector store access control
- Model denial-of-service & cost abuse
- Model theft & extraction resistance
The core pillars of our AI security practice.
Guardrails belong in the architecture, not just the prompt.
A system prompt that says "don’t reveal confidential data" is a suggestion, not a control. We help teams build the layers that actually enforce it — before and after the model runs.
Input & output validation
Structured schemas and content filters on both sides of the model call, so a crafted input can’t become an unchecked action or a leaked secret.
Least-privilege tool access
Every tool and API an agent can call is scoped to exactly what that workflow needs, with destructive actions gated behind explicit confirmation.
Isolated retrieval boundaries
RAG systems are architected so one user’s query can never retrieve another tenant’s or user’s indexed data, even under adversarial prompting.
Logging built for incident response
Full prompt/response/tool-call traces retained and structured so a security review — or a real incident — can be reconstructed after the fact.
How an AI security engagement runs.
Architecture mapping
We map every model call, tool integration, and data source in the system to understand the real attack surface — not just the chat interface a user sees.
Adversarial testing
Structured red-teaming against the OWASP LLM Top 10, using crafted prompts, tool-abuse chains, and data-extraction attempts specific to your use case.
Findings & risk scoring
Every issue is scored by exploitability and business impact, and grouped by the architectural layer it belongs to so fixes can be prioritized sensibly.
Remediation & re-test
We work directly with your engineers on fixes — guardrail design, scope reduction, architectural changes — then re-test to confirm the gap is closed.
Common questions on AI security engagements.
Can't find what you're looking for? Reach out to our engineering team directly.
Can't find what you're looking for? Reach out to our engineering team directly.
Find the gap before it’s an incident.
Whether you’re shipping your first LLM feature or running production agents at scale, we’ll help you understand exactly where your AI system is exposed — and how to close it.
