Your AI stack as a human body: why stopping at LLM + RAG leaves it without an immune system

IA y amenazas emergentes
31 Oct 2026, 3:40 p.m. By Juan Carlos Hernandez 💬 0
Your AI stack as a human body: why stopping at LLM + RAG leaves it without an immune system

There is a simple way to understand an AI stack: as a human body. The language model is the brain, RAG is the brain with a library, MCP is the nervous system, skills are muscle memory and the agent is the brain with hands. The analogy works, but it is missing one organ, the one behind almost every incident we see in our AI audits: the immune system. Most companies build the first two layers and stop. The ones that keep going plug in tools and automate actions with nothing in place to detect, isolate or stop an attack. This article walks the five layers, the typical weak spot of each according to the OWASP Top 10 for LLM Applications 2026, and the check-up worth doing before building further.

LLM, RAG, MCP, skills and agents as organs, with the OWASP 2026 risk of each layer; the immune system is AI security.

LLM, RAG, MCP, skills and agents as organs, with the OWASP 2026 risk of each layer; the immune system is AI security.

1. LLM, the brain: understands, writes and reasons

The language model only knows what it learned in training and what you put in its context. It is where almost every company starts: an assistant to draft, summarize or answer questions.

The weak spot: sensitive information disclosure (LLM02:2026) and hidden context exposure (LLM08:2026). The model repeats data it saw in an earlier conversation or reveals its internal instructions, the tools it has and the rules that govern it.
The check-up: which data goes into the model, who can see it and whether the vendor uses it for training. An honest inventory starts with the shadow AI your teams already use.

2. RAG, the brain with a library: answers from your documents

Retrieval-augmented generation (RAG) looks up your documents before answering and grounds the answer in your own data. It is the natural second step and, for many companies, the last one.

The weak spot: vector and embedding weaknesses (LLM09:2026) and data poisoning (LLM05:2026). If the vector store does not separate permissions, a user retrieves documents they should not see. If someone plants a document with hidden instructions, the model obeys them when it reads it.
The check-up: the library inherits the permissions of the original documents, what gets indexed is reviewed, and a user without privileges is used to test what can be retrieved.

3. MCP, the nervous system: one plug for tools and data

The Model Context Protocol (MCP) is an open standard for connecting AI applications to files, databases, APIs and tools; its authors describe it as the USB-C port for AI. This is where the stack stops merely answering and starts touching real systems.

The weak spot: supply chain (LLM04:2026) and excessive agency (LLM03:2026). OWASP documents a database MCP server running with administrator credentials that allowed a production database to be dumped, bypassing row-level security, and a malicious MCP package that silently copied outgoing email to an attacker.
The check-up: every MCP server is pinned to a verified version, runs with the least credential possible and has its tool descriptions audited, because the model reads them as instructions.

4. Skills, muscle memory: saved know-how

A skill is a folder with an instructions file that the agent loads only when a task needs it; it is an open format already adopted by several products. It is convenient: repeated know-how is packaged and versioned.

The weak spot: supply chain again (LLM04:2026 and ASI04 in OWASP's agentic list). A skill downloaded from a public repository is code and text that run with the agent's permissions; OWASP gives the example of a malicious skill published on an open-source repository.
The check-up: skills are treated like software dependencies: review before install, known origin, pinned version and a register of which ones are active.

5. AI agent, the brain with hands: plans and acts

The agent plans the steps and uses tools to act, not just to answer. It sends email, opens tickets, changes records and runs commands. It is the layer with the most value and the most consequences.

The weak spot: prompt injection (LLM01:2026) turned into agent goal hijack (ASI01) and tool misuse (ASI02). Text planted in an email, a support ticket or a public issue is read by the agent with your company's credentials and ends up executing what the attacker wrote. OWASP cites a case where a poisoned issue was used to exfiltrate private repositories.
The check-up: the agent acts under its own identity with scoped permissions (ASI03), irreversible actions require human confirmation, and it is tested the way an attacker would.

The missing organ: the immune system

Anatomy of the attack described in the article.

Anatomy of the attack described in the article.

A body with a brain, a library, nerves, muscle memory and hands, but no immune system, does not survive the first infection. In AI the immune system is AI security and governance: knowing which AI exists in the company, what each layer can touch, testing it in a controlled way and having rules and owners. The fact that excessive agency climbed to third place in the OWASP Top 10 2026 says a lot about where incidents are heading. This is how T.I. RESCUE covers it:

Inventory and classification: which models, assistants, agents, MCP servers and skills the company uses, and the risk level of each use case with the AI risk classifier.
Technical testing: injection attempts, data extraction and unauthorized actions against your assistants and agents, based on OWASP, as part of the AI audit or the pentest.
Governance: use policy, impact assessment and life-cycle controls with ISO 42001, which already covers vendors, data and responsible use.
Clear rules for people: the acceptable use policy takes minutes with the AI acceptable use policy generator, and the frameworks are compared in ISO 42001 vs NIST AI RMF vs EU AI Act.

Where to start?

Take the inventory with the shadow AI checklist: you cannot protect what you do not know about.
If you already have RAG, test what a user without privileges can retrieve.
If you connect MCP or skills, pin versions, review origins and remove administrator credentials.
If you have agents, ask for a controlled test before giving them access to email, payments or production.
Measure your governance with the ISO 42001 self-assessment and read what ISO 42001 requires.

AI stack security frequently asked questions

What is an AI stack?

It is the set of layers a company uses artificial intelligence through: the language model (LLM), document retrieval (RAG), connectors to tools and data (MCP), skills with reusable instructions, and agents that plan and act.

Why is LLM + RAG not enough?

Because it solves answering with your own data, but it does not govern who accesses what, does not test the application against attacks and does not prepare the company for the next layer, agents acting with real permissions.

What is MCP and why is it a risk?

The Model Context Protocol is an open standard for connecting AI to files, databases and APIs. The risk lies in MCP servers of doubtful origin or with excessive credentials: the OWASP Top 10 2026 documents database dumps and email theft.

What is excessive agency?

It is OWASP risk LLM03:2026: an agent with more functions, permissions or autonomy than the task needs, which acts on behalf of the company when it receives a malicious instruction.

How does T.I. RESCUE help?

With inventory and classification of the AI in use, technical tests against assistants and agents based on OWASP, and governance with ISO 42001 and NIST AI RMF, from a one-off audit to support on the way to certification.

Sources

  1. OWASP GenAI Security Project. OWASP Top 10 for LLM Applications 2026. 2026 https://genai.owasp.org/llm-top-10/
  2. OWASP GenAI Security Project. OWASP Top 10 for Agentic Applications 2026. 2025 https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/
  3. Model Context Protocol. What is MCP and security best practices. 2026 https://modelcontextprotocol.io/
  4. Agent Skills. Agent Skills overview and specification. 2026 https://agentskills.io/
Controls and monitoring that stop the attack.

Controls and monitoring that stop the attack.

Does your AI already have hands? Ask for a check-up of your stack

Inventory, controlled tests against assistants and agents, and governance with ISO 42001. A prioritized report, without stopping operations.

See the AI audit

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