The 7 Layers of AI Security Nobody Talks About | Stack of Truths

The 7 Layers of AI Security Nobody Talks About

May 5, 2026 — 8 min read — Pedro Jose

Everyone’s building AI stacks. LLMs here. Vector databases there. Frameworks connecting everything.

It’s beautiful. It’s powerful. It’s also a massive attack surface.

I see founders mapping out their AI ecosystem with pride. And I see attackers mapping out the same thing — looking for the weak spot in every layer.

⚠️ THE REALITY

AI isn’t one tool. It’s an ecosystem. And every layer has vulnerabilities that most builders never consider.

The 7 Layers of AI (And Where They Break)

🧠 1️⃣

LLMs — The Thinking Layer

GPT, Claude, Gemini, Mistral. They understand language and generate ideas. But they’re also gullible.

🔴 Security risks:

Prompt injection — attackers trick your model into ignoring instructions
Jailbreaks — role-play attacks that bypass safety filters
System prompt extraction — leaking your secret instructions
Data leakage — training data extracted through clever queries

✅ How to fix: Input sanitization, output validation, least privilege, regular red teaming.

🔌 2️⃣

Frameworks — The Wiring

LangChain, LlamaIndex, Haystack. They connect models to tools, data, and workflows. They also inherit vulnerabilities.

🔴 Security risks:

MCP design flaws — 150M+ downloads with RCE vulnerabilities
Tool call abuse — attackers trick your agent into calling malicious tools
Unsafe deserialization — framework blindly trusts serialized data
Supply chain attacks — compromised dependencies

✅ How to fix: Update frameworks regularly, audit MCP implementations, validate all tool calls, sandbox execution.

📚 3️⃣

Vector Databases — The Memory

Pinecone, Weaviate, Chroma. They store meaning, not just text. They also store everything else.

🔴 Security risks:

Data poisoning — attacker injects malicious content into your knowledge base
Unauthorized retrieval — no access control at the vector level
PII leakage — sensitive data stored in vectors, never deleted
Indirect prompt injection — malicious content retrieved from vectors triggers harmful behavior

✅ How to fix: Sanitize data before embedding, implement access controls, audit vector contents, monitor retrieval patterns.

📥 4️⃣

Data Extraction — The Input Pipeline

FireCrawl, Crawl4AI, Docling. They pull information from messy sources. They also pull in attacks.

🔴 Security risks:

Indirect prompt injection — hidden instructions in webpages your agent reads
Malicious source documents — PDFs, emails, or websites with embedded attacks
SSRF via crawler — attacker makes your system fetch malicious URLs
Data exfiltration — extracted data sent to attacker-controlled endpoints

✅ How to fix: Validate all sources, sanitize extracted content, limit crawler scope, treat extracted data as untrusted.

⚙️ 5️⃣

LLM Runtimes — The Control Layer

Hugging Face, Ollama, Groq. They run models locally or in the cloud. They also run with configs you forgot to lock down.

🔴 Security risks:

Exposed endpoints — no authentication on internal APIs
Misconfigured permissions — anyone can load or unload models
Model theft — attackers download your fine-tuned models
Adversarial inputs — crafted inputs that cause model misbehavior

✅ How to fix: Require authentication for all endpoints, implement rate limiting, audit model access, use API gateways.

🔢 6️⃣

Embeddings — The Meaning Engine

OpenAI, SBERT, Voyage, Cohere. They convert text to vectors. They also can leak what they encoded.

🔴 Security risks:

Reverse engineering — attackers reconstruct sensitive data from vectors
Membership inference — attackers determine if specific data was in training
Embedding inversion — extracting original text from vector representations
Property inference — learning statistical properties of your data

✅ How to fix: Use differential privacy, limit embedding access, audit query patterns, consider local embeddings for sensitive data.

📊 7️⃣

Evaluation — The Quality Check

Giskard, Ragas, TruLens. They measure accuracy and reliability. They don’t measure security.

🔴 Security risks:

False sense of security — “it passes evaluation” ≠ “it’s secure”
No adversarial testing — evaluation doesn’t include attack scenarios
Blind spots — evaluation misses prompt injection and jailbreak success rates
Missing red team metrics — you don’t know what you’re not measuring

✅ How to fix: Add adversarial evaluation, test for prompt injection success rates, include red team metrics, regularly pentest your agents.

┌─────────────────────────────────────────────────────────────┐
│  THE MISSING LAYER: SECURITY                              │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Layer 0 should be security. Not after. Not “we’ll fix     │
│  it later.” At every layer, from LLM to evaluation,        │
│  attackers are looking for the gap you left open.          │
│                                                             │
│  • Who tests prompt injection on your LLM?                 │
│  • Who audits your framework version?                      │
│  • Who checks your vector database for poison?             │
│  • Who verifies your extraction pipeline is safe?          │
│  • Who pentests your runtime configuration?                │
│  • Who monitors embedding access patterns?                 │
│  • Who evaluates the evaluation?                           │
│                                                             │
└─────────────────────────────────────────────────────────────┘
        

            🔐 The bottom line:

            You built the AI stack. Now let me show you where it breaks.

            Every layer has vulnerabilities. Attackers know them. They’re probing your systems right now.

            The question isn’t “if” someone will find a weakness. It’s “when” — and “what will they do when they find it?”

What You Should Do Right Now

Map your AI stack — List every layer. Every tool. Every integration.
Audit each layer for common vulnerabilities — Use the checklists above.
Test your LLM for prompt injection and jailbreaks — Simple tests reveal critical flaws.
Review your framework versions — MCP has known RCE vulnerabilities. Update now.
Check vector database access controls — Can anyone query anything?
Validate your data extraction sources — Treat all external content as untrusted.
Secure your runtimes — No exposed endpoints without auth.
Add adversarial evaluation — Don’t just measure accuracy. Measure security.
Get a real pentest — Automated scanners miss what human-led red teaming finds.

🔮 THE BOTTOM LINE

Most founders understand the AI stack. Few understand where it breaks.

Attackers do.

Don’t wait for them to show you.

🦞🔐

You built the AI stack. Let me test where it breaks.

Full AI agent pentest: $3,000. AI Red Team: $5,000. Security retainer: $1,500/month.

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