Test prompt security by impact, not by response filters alone.
Eresus measures how untrusted inputs such as user messages, email, documents, web pages, RAG sources, and MCP tool descriptions can change LLM behavior.
Vulnerabilities our researchers found and disclosed
Public advisories credited to the Eresus research team. The same people run your engagement.
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
Evidence standard
Proof you can inspect. Boundaries you can plan around.
Every engagement is bounded by written authorization, evidence handling, and a clear handoff. These are the defaults; the signed scope can tighten them.
Evidence you receive
- Tested prompt, agent, RAG, MCP, and tool-use paths tied to real workflows.
- Redacted transcripts and request/response evidence for validated failures.
- Policy, identity, tool-scope, and rollout remediation guidance.
- Retest status for critical guardrail changes.
Explicit exclusions
- Compliance certification or a claim that the model is safe.
- Training on, retaining, or redistributing customer data or model weights.
- Unapproved destructive tool calls or abuse of third-party systems.
- A guarantee that every prompt or agent path has been discovered.
A practical test flow that turns input threats into business impact.
Problem
We map which untrusted content the LLM reads and which actions it can reach.
Attack scenario
We test visible user messages and hidden document instructions separately.
Proof
Successful bypasses are shown through responses, tool calls, accessed data, and logs.
Delivery
Fixes are described across system prompts, data separation, tool scopes, and approval gates.
The questions buyers want answered early.
What AI surfaces do you test?+
Is this just prompt injection testing?+
Do you translate findings into engineering actions?+
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.
The Art of LLM Jailbreaking: Demystifying Offensive Prompt Engineering
How do Red Teamers bypass the safety filters of Large Language Models? Dive deep into the manipulative art of LLM Jailbreaking, DAN prompts, and...
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.
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.
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.