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.

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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 and architectures do you test?+
We test autonomous AI agents, LLM integrations, RAG semantic search pipelines, MCP servers, tool execution boundaries, serialized model files (.pkl, .joblib, .gguf, .onnx, .keras), and guardrail implementations.
Is this limited to simple prompt injection testing?+
No. While prompt injection is evaluated, our red team focuses on systemic abuse: privilege escalation via tool use, indirect injection from retrieved documents, model extraction, training data poisoning, and remote code execution via unsafe model deserialization.
How are findings translated into engineering actions for AI teams?+
We map each vulnerability to specific defensive guardrail rules, system prompt structural hardening, strict JSON schema validators for tool calling, context window isolation, or container sandboxing configurations.
What is the duration and pricing for an AI Security Assessment?+
Typical AI and agentic security assessments take between 7 to 20 business days based on agent tooling count, RAG datasource complexity, and custom model architectures. Pricing is scoped transparently per integration boundary.
Do you assess compliance with the EU AI Act and OWASP LLM Top 10?+
Yes. All tests evaluate the full OWASP Top 10 for LLM Applications and provide risk categorization mapped against EU AI Act high-risk system transparency and cybersecurity requirements.
Is retesting included for AI guardrail and prompt fixes?+
Yes. We re-run targeted adversarial jailbreak suites and tool boundary validation within 30 days to ensure patched guardrails cannot be bypassed with semantic permutations.

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.

Related proof

Research and advisories that support this service motion.

Research

Beyond Jailbreaks: Contextual Red Teaming for Agentic AI

Why standalone jailbreak tests miss agent risk, and how to test indirect prompt injection, retrieval boundaries, tool permissions, and recovery in multi-step systems.

Research

Automated Red Teaming for Agentic AI Workflows: What to Test and What to Measure

A practical framework for testing prompt injection, tool abuse, data exposure, excessive agency, and recovery in enterprise agent workflows.

Research

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...

Advisory

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.

Advisory

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.

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.

Book a security briefingRequest a scope review