AI Security
Security Testing for AI Systems
We test MCP servers, LLM integrations, and AI agents. Scoped assessments that examine how AI systems interact with tools, data, and users.
Answer
AI security testing validates how models, tools, and data sources behave under adversarial use—so AI features stay safe, scoped, and reviewable.
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MCP vulnerability categories documented
Public MCP pentesting checklist
Trusted by 120+ security-conscious companies
Why AI Security Testing is Different
Traditional application security testing assumes deterministic inputs and outputs. AI systems break this model. They interpret, execute, and act — often in ways their developers didn't anticipate.
Testing Real Attack Paths
We examine how AI systems interact with tools, data, and users — uncovering risks that emerge from real-world usage patterns, not just theoretical vulnerabilities.
Clear Findings, Clear Fixes
Every finding includes specific remediation steps with code examples. You'll know exactly what's wrong and how to fix it.
Evidence for Internal Review
Reports are structured for technical teams and leadership alike. Your security and engineering teams get what they need to prioritize and act.
Our Work in MCP Security
With 10+ years in product security testing and 120+ products tested, we bring established methodology to the emerging AI security space. Our open-source tools and research are used by security teams worldwide.
Years in product security testing
Products tested
Public MCP pentesting checklist
What We Test
AI systems create attack surfaces that traditional security testing misses. Each service addresses specific ways attackers target AI infrastructure.
MCP Server Pentesting
MCP servers let AI assistants access tools and data — file systems, databases, APIs. Attackers target these connections to hijack AI capabilities.
When an attacker can manipulate prompts or server responses, they gain the same access the AI has.
What we look for
- • Path traversal in file tools
- • Indirect prompt injection via documents
- • Excessive OAuth scopes
- • Supply chain risks from typosquatted packages
LLM Integration Security
RAG pipelines, embedding stores, and API integrations process untrusted content alongside trusted instructions. This creates injection opportunities.
Attackers embed malicious instructions in documents that get retrieved and processed alongside legitimate queries.
What we look for
- • RAG document injection
- • Vector store access control gaps
- • API key exposure in client bundles
- • Output filter bypasses
AI Agent Security
Autonomous agents chain multiple actions together — browsing, coding, API calls. An attacker who corrupts agent memory or inputs can hijack this entire chain.
By poisoning an agent's memory, attackers alter behavior across all future interactions with that agent.
What we look for
- • Confirmation dialog bypasses
- • Tool chaining privilege escalation
- • Memory injection persistence
- • Multi-step attack chains
When you are ready
A conversation about your AI stack.
No commitment required.
Tell us about the AI features you are building. We will explain what we would test, answer your questions, and provide a fixed quote if you would like one.
Start a conversationor view the MCP pentesting checklist first