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The service

The AI-Native Security Review

A focused, hands-on review of the attack surface your AI agents and MCP servers add, the part traditional appsec and pentests weren’t built to reason about.

Scope

What we test

Specifically the AI-added surface, not a generic checklist re-run with an AI label.

  • MCP server tool exposure and per-invocation authorization
  • Agent tool-invocation boundaries and human-in-the-loop gaps
  • Prompt-injection paths that reach a real tool call
  • Over-broad tool scopes and excessive capability
  • Secrets and data reachable from agent context
  • Confused-deputy and privilege-escalation chains

Methodology

Scan → Findings → Fix guidance

  1. 01 —

    Scan

    We map your MCP servers and agent tool surface from configuration and a scoped test environment, then probe authorization boundaries the way an attacker-steered agent would. No standing production access required.

  2. 02 —

    Findings

    Each exposure is written up with a severity, the context it is reachable from, evidence, and a plain explanation of why it matters, ranked so you know what to address first.

  3. 03 —

    Fix guidance

    Every finding ships with concrete remediation mapped to your intended trust boundary: scope reductions, authorization checks, and guardrails that fit how your system is actually built.

The chain we look for

How one injected instruction becomes an action

An example privilege-escalation chain — the injection propagates down the stack until it reaches a real, privileged action.

privilege-escalation chain (illustrative) 5 STAGES
  1. 1
    Ingress

    Untrusted input

    Prompt injection: attacker-controlled instructions arrive inside external content the agent ingests, such as a web page, email, PDF, or another tool’s output.

  2. 2
    Context

    Agent context

    The model cannot reliably separate attacker text from legitimate instructions, so it acts on the injected command. This is the classic confused-deputy problem.

  3. 3
    Attack surface

    MCP server

    Tools are exposed to the agent without per-invocation authorization or human-in-the-loop approval, so any instruction the agent follows can call them.

  4. 4
    Capability

    Over-broad tool

    A reachable tool (e.g. shell.exec, fs.write, http.fetch) grants far more capability than the task needs. That excess scope is the attacker’s leverage.

  5. 5
    Impact

    Impact

    The injected instruction drives a privileged action: data exfiltration, unauthorized writes, or remote code execution in the tool’s context.

Illustrative chain, not a specific target. Each stage is an exposure class REDHEXX probes for.

Deliverables

What you get

  • A ranked findings report: every exposure with severity and evidence
  • Remediation guidance mapped to your trust boundary, per finding
  • A live readout to walk through the findings and priorities
  • A re-check on the highest-severity fixes once you have addressed them

Who it’s for

A narrow fit, on purpose

  • Pre-Series A, AI-native SaaS teams shipping MCP servers or agents
  • Founders and CTOs heading into enterprise procurement or security review
  • Teams who added an agent’s ability to act faster than they reviewed it

See it against your own stack.

Join the waitlist for early access as we onboard design partners.

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