Product Security Testing

AI & MCP Security Testing

Scoped, non-disruptive testing for AI-powered applications and Model Context Protocol implementations. We focus on tool boundaries, data access, and configuration so you can understand risk without operational surprises.

Authors of the MCP pentesting checklist.

Public checklist

23+ GitHub stars

The open MCP pentesting checklist buyers use to inspect tool safety, prompt injection, auth, and trust boundaries.

Training lab

157+ stars on the MCP lab

An intentionally vulnerable lab that makes real MCP attack paths concrete before a client engagement starts.

Operating model

Scoped before testing

We confirm tools, data paths, connected systems, and exclusions in writing before anything starts.

Transport & Tool Safety

Attackers move through MCP by chaining transport messages, tool calls, and returned data. We model those paths end-to-end so you can see where controls should live and how they can be bypassed.

What we analyze

  • Transport boundaries across stdio, HTTP, and event streams, including message validation.
  • Tool invocation policies, parameter constraints, and least-privilege defaults.
  • Cross-tool data flows that can re-enter prompts, logs, or downstream systems.

Example finding

A tool accepted unrestricted file paths over stdio, allowing the model to read configuration files outside the intended working directory.

Prompt Injection & Data Leakage

Prompt injection shows up when attackers steer model behavior through untrusted content. We model those behaviors inside real MCP workflows so the testing reflects how tools, data, and instructions interact in production.

Attacker behaviors we model

  • Indirect injection via tool output, retrieved documents, or user content that re-enters the prompt.
  • Instruction override attempts that influence tool choice, parameters, or retrieval scope.
  • Context egress paths that expose secrets through responses, logs, or downstream systems.

How we test in MCP engagements

  • Trace prompt -> tool -> data return loops to locate untrusted content re-entry points.
  • Validate tool permission boundaries and allowlist enforcement under adversarial prompts.
  • Verify redaction, output shaping, and safe failure modes for sensitive data.

Example finding

Untrusted tool output was reinserted into the system prompt, enabling extraction of internal runbook snippets.

Guardrails & Configuration

Guardrails are only useful if they hold under adversarial input and real execution paths. We model how prompts, tool outputs, and external data hit those controls, then test the runtime paths where limits, allowlists, and policy checks should stop unsafe actions.

What we validate

  • Runtime limits for token, tool, and request budgets, including safe failure behavior.
  • Allow/deny lists for tools, hosts, and data sources, with explicit boundaries for each role.
  • Red-team prompt suites that probe bypass patterns and configuration drift over time.

Example finding

A safety policy existed in configuration but was not enforced in the runtime path used by background tool calls.

Third-party OAuth Hygiene

OAuth is the control plane for most AI vendors and MCP tools. Attackers look for over-scoped grants, long-lived refresh tokens, and cross-tenant consent paths they can reuse. We model those behaviors across real tool chains so the testing reflects how tokens actually move between services.

Attacker behaviors we model

  • Reusing broad-scoped tokens to reach data sources beyond the intended tool.
  • Pivoting through refresh tokens and background jobs after access is removed.
  • Abusing shared tenant or workspace grants to cross boundaries between customers or environments.

How we test in MCP engagements

  • Trace OAuth grants from model prompts through tool calls, consent, and token exchange.
  • Validate scope minimization, rotation, and revocation workflows under realistic tool usage.
  • Verify per-tenant isolation and vendor-side controls for token fan-out.

Example finding

A vendor-issued refresh token remained valid after access was revoked, allowing continued data pulls from a connected workspace.

Depth Across Real AI Attack Paths

Attackers chain prompts, tools, and tokens rather than relying on a single weakness. We map those paths and test how controls behave in the exact workflows your teams run.

MCP Server Security

Transport validation, tool authorization, and data exposure paths across MCP.

LLM Integration Security

RAG data boundaries, embeddings pipelines, and API integration abuse paths.

AI Agent Security

Tool controls, memory boundaries, and privilege escalation paths in agents.

Why teams trust AI and MCP reviews here

AI and MCP authority should look like public protocol work, not generic AI copy

This surface becomes believable when buyers can inspect the checklist, lab, tooling, and reporting model before they hear the pitch.

AM

Practice lead

Akash Mahajan

Founder & CEO

Akash leads Appsecco's product security testing practice and the public research work around it. Buyers usually inspect the methods, labs, and reporting artifacts before they commission an engagement. This section makes that trail easier to follow.

  • 10+ years in product security and 700+ security engagements
  • Conference speaker at BlackHat, OWASP, and regional security events
  • Author of open-source security training, MCP testing resources, and practitioner checklists
LinkedIn GitHub Appsecco Open Source

Public protocol work

The public body of work is unusually important for AI and MCP because buyers want to know whether the team has already mapped the protocol boundaries and abuse paths.

MCP pentesting checklist

23+ GitHub stars

The public checklist used by security teams to reason about tool safety, prompt injection, auth, and supply-chain trust in MCP systems.

Vulnerable MCP servers lab

157+ GitHub stars

A training lab for making MCP attack paths concrete instead of theoretical.

MCP client and proxy

12+ GitHub stars

Interception tooling for stdio-based MCP reviews and practical tool-path testing.

AI and MCP proof paths

For AI buyers, the most convincing next steps are usually the flagship MCP page, the report standard, and the broader guide material that explains how scope is thought through.

Standalone MCP service page

Flagship route

The direct commercial overview of MCP methodology, pricing, deliverables, and protocol-specific testing depth.

Sample report

Evidence standard

A public look at the report discipline that sits behind AI and MCP engagements.

AI red teaming guide

Scoping logic

A guide that shows how Appsecco frames AI application testing, tool abuse, and workflow-level risk.

What recent buyers say after the review

"The kind of vulnerabilities they found were things we never expected - things which were not on our radar. That changed how we think about our own attack surface."

Founder & CEO

Asia's leading Threat Intel Company

"Found multiple interesting exploitable vulnerabilities across our product. Clear reporting, thorough walkthroughs of each finding, and they stayed engaged until every issue was resolved."

Manager

Most popular Vulnerability Scanner (100+ countries)

"We engaged with Appsecco for red teaming. Their findings were specific, well-documented, and gave our team a clear path to remediation."

Senior Expert

European Giant in FinTech

Reference-ready next step

Need the closest AI or MCP proof path for your buyer review?

We can start from the checklist, lab, report example, or flagship MCP scope page that best matches the system you are reviewing.

This is one area where the public protocol trail matters disproportionately. Buyers are often checking whether the practice has already done the thinking it claims to have done.

Request scoped review

AI & MCP security FAQ

What is in scope for an AI and MCP security assessment?

We scope the actual AI workflows, connected tools, MCP servers, data sources, approval gates, and OAuth paths your product relies on. The goal is to test the real attack path from prompt to action, not just one isolated component.

Do you test the model provider or our implementation?

We focus on your implementation: prompt construction, retrieval, tool use, MCP boundaries, token handling, and output controls. We also review how vendor-issued permissions and integrations are used in your stack.

Can one engagement cover both app logic and MCP servers?

Yes. Many teams need one assessment that covers AI application behavior together with MCP server exposure. We can scope both so the findings reflect the full buyer-facing and runtime path.

What access do you need to test safely?

Usually we need a staging environment, scoped credentials, architecture context, and enough access to exercise the in-scope flows. If production validation is required, we agree the exact limits and safe methods before testing begins.

What will the final report include?

You receive prioritized findings, attack-path narratives, tool or integration-specific evidence, practical remediation guidance, and retest support so engineering and security reviewers can validate fixes clearly.

Explore AI security testing

Related AI security services and resources

Move from AI security concepts into testing scope, agent risks, prompt injection, MCP exposure, and practical assessment paths.

Guide

MCP Security Testing Checklist for Buyers

How to evaluate MCP scope, public proof, connected-resource coverage, and reporting quality before launch.

Guide

AI Agent Security Testing vs MCP Security Testing

A buyer-facing guide for separating workflow-level agent risk from MCP protocol and tool-path risk.

Service

LLM Integration Security Testing

Security testing for LLM features, RAG workflows, prompt handling, tool calls, and connected data exposure.

Service

AI Agent Security Testing

Assessment of agent workflows, tool permissions, approval boundaries, memory handling, and autonomous actions.

Service

MCP Server Security Testing

Scoped testing for transport security, tool safety, prompt injection, OAuth hygiene, and access boundaries.

Glossary

AI Red Teaming

Adversarial testing for AI-enabled product behavior, tools, retrieval, agents, and workflows.

Guide

AI Red Teaming for LLM Applications

How to scope adversarial testing for LLM apps, RAG, agents, tools, MCP, and workflow actions.

Guide

AI Red Teaming vs AI Security Testing

How adversarial AI behavior testing fits with broader product and system security testing.

Glossary

LLM Security

Risks and controls for LLM applications, RAG systems, embeddings, and model-connected workflows.

Safe next step

Talk through your AI/MCP scope.No commitment required.

Share how your AI stack works. We will outline what we would test, what stays out of scope, and provide a fixed quote if that is useful.

Talk through AI/MCP scope

or View a sample report first

No obligation to proceed
Fixed scope and pricing
Clear, written test plan