Test LLM, agent, and MCP systems with real attack workflows.
Eresus validates prompt injection, indirect prompt injection, RAG leakage, tool abuse, MCP registration/transport risk, agent authorization boundaries, and model artifact intake through offensive testing.
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
Risk signals
Outcomes
Not scanner output. Offensive work that produces proof.
Scope and objective
We align assets, workflows, user roles, testing windows, and safe operating boundaries before execution starts.
Expert validation
Eresus analysts validate exploitability and business impact instead of forwarding automated scanner output.
Proof, fix, retest
Each finding ships with evidence, impact, remediation guidance, and retest steps so teams can close risk quickly.
The questions buyers want answered early.
What AI surfaces and architectures do you test?+
Is this limited to simple prompt injection testing?+
How are findings translated into engineering actions for AI teams?+
What is the duration and pricing for an AI Security Assessment?+
Do you assess compliance with the EU AI Act and OWASP LLM Top 10?+
Is retesting included for AI guardrail and prompt fixes?+
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.
Research and advisories that support this service motion.
What is AI Security? A Complete Enterprise Blueprint for Securing Machine Learning Ecosystems
A deep dive into the complex world of AI Security. Understand the mechanics behind data poisoning, adversarial ML evasion, and prompt injection attacks...
AI Agent Traps: Web Attacks Against Agents
How hidden web content, poisoned context, and tool access can manipulate autonomous AI agents in real enterprise workflows.
The April 2026 MCP RCE Wave
Why MCP security depends on architecture, identity, tool isolation, and registration control more than a single CVE.
Unauthenticated Remote Code Execution via Arbitrary Command Injection in MCPHub Server Registration
MCPHub accepts attacker-controlled command and args values during server registration and spawns them through STDIO, enabling full remote code execution on the host.
Authentication Bypass via skipAuth Configuration Grants Full Admin Access in MCPHub
When skipAuth is enabled, MCPHub bypasses both authentication and admin authorization checks, allowing any unauthenticated user to access privileged API functionality.
SSE Endpoint Accepts Arbitrary Username from URL Path, Enabling User Impersonation in MCPHub
MCPHub accepts an attacker-controlled username from the SSE URL path and creates internal user context without authenticating or validating the account, enabling user impersonation.
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.