AI security
Red teaming for LLM apps, AI agents and MCP servers.
AI features open doors classic security testing never looked at: instructions hidden in a document your assistant reads, an agent that can be talked into using a tool it shouldn’t, customer data that leaks through an answer. We attack your LLM applications, agents and MCP servers the way real adversaries do, fix what gets through with layered guardrails and tighter permissions, and turn every successful attack into a test that runs before each release.
01 / What we test
Direct attacks in the chat and indirect ones hidden in web pages, emails, files and tool results your AI reads.
Agents pushed beyond their intended scope: unauthorised tool calls, privilege escalation and actions without confirmation.
System prompts, other users’ data and retrieved documents coming out in answers — including through RAG and memory.
Authentication, authorisation, tool descriptions and data exposure of the MCP servers you build or connect to.
02 / Stack
Industry-standard, boring in the good way: tools with a future, that the next engineer will know too.
Attack tooling
garak
Open-source LLM vulnerability scanner
PyRIT
Framework for automated AI red teaming
promptfoo
Adversarial test suites in CI
Custom attack suites
Attacks written for your product and data
Defences we validate
Guardrail layers
Layered checks around model input and output
Input/output classifiers
Detection of injection and sensitive data
Tool permission policies
Least-privilege tool access per agent
Execution sandboxes
Isolation for code and browser actions
Visibility
Agent tracing
Full traces of agent reasoning and tool calls
Langfuse
LLM tracing and evaluation
OpenTelemetry
Standard telemetry into your stack
Per-call audit logs
Who called which tool, with what, and when
03 / Approach
Every finding tied to the OWASP Top 10 for LLM applications, so risk is described in terms your team and auditors share.
Attacks built for your product, documents and tools — not just a generic jailbreak list.
No single filter stops injection, so we combine permissions, confirmations, isolation and checks on input and output.
Every successful attack becomes a regression test in CI, so new models and prompts can’t reopen it.
04 / Standards
We adhere to industry standards and proven methods on every project — they are what makes the estimate hold.
05 / Infrastructure
Good questions
Straight answers to what buyers ask before they start.
Testing an AI system the way an attacker would: trying to make it ignore its instructions, reveal data it should not, or take actions it should not take. WNC tests LLM chats, RAG assistants, AI agents and MCP servers and maps every finding to the OWASP Top 10 for LLM applications.
Related work
Projects and products of ours that involved this service.
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