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.
- Location
- Remote-first, global engagements
- Industry
- AI Cybersecurity
- Cooperation period
- Point-in-time review or ongoing coverage
- Services used
- LLM red-teaming · RAG & agent security · Guardrail design · Model risk assessment
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
Input & prompt layer
The first place AI systems get attacked: direct and indirect prompt injection, jailbreak and guardrail bypass, insecure output handling, and 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
Orchestration & agent layer
Where agents act: excessive agency and tool-call scoping, plugin and function design, human-in-the-loop gates, and multi-agent trust boundaries.
Data & infrastructure layer
- Sensitive information disclosure
- RAG & vector store access control
- Model denial-of-service & cost abuse
- Model theft & extraction resistance
Data & infrastructure layer
What sits underneath: sensitive information disclosure, RAG and vector store access control, model denial-of-service and cost abuse, and model theft.
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.
FAQ
Common questions on AI security engagements.
A conventional pentest is built around fixed inputs, known endpoints, and code-level vulnerabilities like injection or broken auth. An LLM application accepts open-ended natural language and can be manipulated through the conversation itself — prompt injection, jailbreaks, and tool-call abuse don’t show up in a standard OWASP web scan. We test the model, the orchestration layer, and the data pipeline together, using techniques specific to how LLM systems actually get exploited.
No. Most engagements are black-box or gray-box: we interact with your deployed system the way a real user or attacker would, and review architecture diagrams, tool definitions, and prompt templates where relevant. We only request deeper access — like fine-tuning data — if a specific finding requires it to confirm root cause.
Yes, and it’s usually the larger share of the risk. The model provider secures the model itself; everything around it — your system prompt, tool permissions, retrieval pipeline, output handling, and rate limiting — is your responsibility, and that’s exactly where most real-world incidents originate.
Yes — that’s often where the highest-impact findings are. We specifically test excessive agency: what happens when an agent with API or file-system access is manipulated into taking an action outside its intended scope, and whether your permission boundaries and confirmation steps actually hold.
A written report with every finding mapped to the OWASP LLM Top 10, severity and exploitability ratings, and concrete architectural recommendations — followed by direct engineering support to implement fixes and a re-test to confirm closure.
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.
