EresusSecurity
LLM Red Teaming

Stress-test LLM products with realistic adversarial scenarios.

Eresus validates prompt injection, jailbreak, sensitive data leakage, tool abuse, and policy bypass risk across chatbots, RAG systems, copilots, agents, and LLM flows connected to production APIs.

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 and indirect prompt injection
Jailbreak and policy bypass attempts
RAG data leakage and context poisoning
Tool-call, MCP, and agent action boundaries

Risk signals

Sensitive customer data exposed in responses or tool output
Agent executes unauthorized API actions
System instructions overridden by user-controlled content
Persistent behavior drift through RAG sources

Outcomes

Attack scenario matrix
Reproducible prompt and response evidence
OWASP LLM risk mapping
Guardrail and retest recommendations
Engagement flow

A red-team flow from problem framing to release decision.

01

Problem

We identify which data, tools, customer flows, and decision boundaries the LLM can touch.

02

Attack scenario

We design prompt injection, RAG poisoning, jailbreak, and tool-abuse tests around real use.

03

Proof

Each successful scenario is backed by prompt, response, tool-call, log, and reproduction evidence.

04

Delivery

Findings ship with priority, OWASP LLM mapping, fix direction, and retest steps.

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.