AishiSec Services
AI, LLM & MCP Security Testing
Could your AI application be manipulated or exploited?
AI systems add a new kind of attack surface. The model does what it is told, and an attacker's instructions can hide in a document, a web page, or a message your agent processes. The risks include prompt injection, data leakage and insecure integrations.
We test AI, LLM and MCP (Model Context Protocol) systems the way an attacker would: what can the model be made to do, what can it reveal, and what can be reached through the tools connected to it.
What we test
Prompt injection: direct and indirect, including instructions hidden in content the model processes
Data leakage: training data, system prompts and customer data appearing where they shouldn't
Model misuse: bypassing guardrails and producing content the product never intended
Tool and integration security: what the model can do through plugins, APIs and MCP servers
MCP server security: authentication and the scope of tools exposed to model-driven callers
Infrastructure: model endpoints, API keys and logging
How we test it
We agree the scope first: what to test, when, and what is off limits.
Testing combines manual work with automated tools and AI assisted analysis.
Every finding is verified by hand, with evidence captured for your team.
The weaknesses that matter
These are the kinds of findings this assessment is built to catch, each explained the way it would appear in your report.
OWASP LLM01:2025
Prompt injection
Instructions hidden in a document, web page or email the model processes can make it do something it shouldn't: act as another user, or hand over its tools.
OWASP LLM02:2025
Sensitive information disclosure
A model repeats another customer's data, or its own system prompt, giving away how the product works.
OWASP LLM06:2025
Excessive agency
The model can call tools, send emails, update records or act through MCP-connected systems, far beyond what the user asked for, because its permissions are wider than the task needs.
OWASP LLM05:2025
Improper output handling
Model output is treated as safe and fed straight into code, queries or messages. A manipulated reply becomes a real action, a stored script or a poisoned database entry.
OWASP LLM03:2025
Supply chain risk
Models, plugins and MCP servers sourced from providers nobody has verified. A compromised component can behave normally for months, then act on the attacker's behalf.
OWASP LLM10:2025
Unbounded consumption
A single attack can drive the system to spend tokens, compute and API calls without limit: a significant bill in an afternoon, or a service that stops responding.
What you receive
Executive summary written for decision-makers
Findings with severity, business impact and evidence
Practical remediation guidance for your team
Retest results after fixes are applied
When to perform it
Before launching an AI feature, after connecting new tools or MCP servers, and after any incident or near miss.
Who it's for
Businesses building AI products, chatbots, agents or MCP integrations, and businesses adopting them before the risk is understood.
Common questions
Related services
Know where your security stands.
Tell us what you're building, operating or protecting. We'll help you determine where security testing should start.