EresusSecurity
MCP Security

Test MCP servers and agent tools against real abuse paths.

Eresus validates MCP registration flows, transport boundaries, tool-call permissions, identity context, prompt-to-action chains, and agent runtime decisions through offensive security testing.

Best fit

This engagement creates value fastest for teams like these.

AI product and platform teams

Teams shipping LLM, RAG, MCP, agent, or model-intake workflows into internal or customer-facing environments.

Security leaders expanding into AI

Organizations that already run pentest programs and now need guardrail, prompt, and tool-abuse validation.

Teams that need explainable hardening

Groups that need policy, prompt, MCP, and runtime findings translated into concrete mitigations and release decisions.

Scope

MCP server registration, transport, and identity flows
Tool permissions, parameter boundaries, and approval paths
Tool abuse after prompt injection
Agent memory, retrieval, and API action chains

Risk signals

Unauthorized MCP server registration
User context confusion or impersonation
Sensitive data exposure through tool calls
Production action triggered through prompt control

Outcomes

MCP attack-path report
Tool permission hardening guidance
Agent runtime checklist
PoC evidence and retest workflow
Engagement model

Not scanner output. Offensive work that produces proof.

01

Scope and objective

We align assets, workflows, user roles, testing windows, and safe operating boundaries before execution starts.

02

Expert validation

Eresus analysts validate exploitability and business impact instead of forwarding automated scanner output.

03

Proof, fix, retest

Each finding ships with evidence, impact, remediation guidance, and retest steps so teams can close risk quickly.

FAQ

The questions buyers want answered early.

What AI surfaces do you test?+
We test prompts, agents, RAG flows, MCP servers, tool use, model intake, and policy boundaries around real user workflows.
Is this just prompt injection testing?+
No. Prompt injection is one layer. We also validate identity, tool permissions, data leakage, model artifacts, and cross-system abuse paths.
Do you translate findings into engineering actions?+
Yes. We map each issue to guardrail changes, prompt updates, identity boundaries, tool scopes, or rollout decisions.

We tie risk to business impact.

Findings do not stop at severity labels. We explain which customer workflow, data class, or operational objective is affected.

Deliverables work for engineers and executives.

Engineering teams get reproducible proof and remediation direction; leadership gets the risk narrative, priority, and closure status.

Next step

Let’s scope this work against the surface that matters most.

Whether this starts as a pilot, a single application, a critical API, an AI agent flow, or a wider program, we start from the highest-impact surface.