EresusSecurity
Prompt Injection

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.

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

Direct prompt injection tests
Indirect injection through documents, email, and web content
RAG and context-window manipulation
Tool-call, data leakage, and approval bypass checks

Risk signals

Model ignores system instructions
Hidden content executes instructions the user did not see
Sensitive data enters summaries or tool output
Agent action creates risk even when prompt filters pass

Outcomes

Prompt injection finding set
Reproduction steps for bypassed controls
Prompt, RAG, and tool-boundary fixes
CI/CD or release-gate test recommendations
Test model

A practical test flow that turns input threats into business impact.

01

Problem

We map which untrusted content the LLM reads and which actions it can reach.

02

Attack scenario

We test visible user messages and hidden document instructions separately.

03

Proof

Successful bypasses are shown through responses, tool calls, accessed data, and logs.

04

Delivery

Fixes are described across system prompts, data separation, tool scopes, and approval gates.

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.