🛡️ SQLi Vulnerability Analysis in Zabbix with NotebookLM

---

📋 Table of Contents

• Context and Objectives
• Source Curation
• Prompt Engineering and "Scars"
• Study Miniguide
• Glossary
• Reusable Prompts

---

🎯 Context and Objectives

This thematic notebook was developed as part of a practical project challenge for the DIO platform, using NotebookLM as an active artificial intelligence tool for curation, synthesis, and organization of knowledge.

The chosen theme focuses on Application and Infrastructure Security, specifically on the analysis of SQL Injection (SQLi) vulnerabilities that affected the Zabbix ecosystem — one of the most widely used asset monitoring tools in the corporate market.

General Objective

To analyze the technical and operational impact of SQL injection flaws in the Zabbix ecosystem, understanding how an SQLi vector can escalate to Remote Code Execution (RCE) and document the best remediation practices (hardening).

Target Audience

Profile	Relevance
👨‍💻 AppSec Professionals	Technical analysis of attack vectors and remediation
🔍 SOC Analysts	Detection and response to incidents involving Zabbix
🖧 Network Administrators	Hardening and protection of monitoring servers
🎓 Cybersecurity Students	Practical learning with real CVEs and exploitation chain

---

📚 Source Curation

To ensure technical accuracy of the analyses in NotebookLM, official open sources and threat intelligence reports were selected:

1. 🔵 Zabbix Security Advisories (Official Portal)

Official documentation containing corrective patches, technical descriptions of scope, and CVSS severity matrix for system flaws.
🔗 zabbix.com/security_advisories

2. 🟠 NIST National Vulnerability Database (NVD)

Detailed consultation on the records and specific attack vectors for the analyzed CVEs:

• CVE-2026-23921 — SQLi via sortfield parameter in the API (critical, minimal authentication)
• CVE-2024-42327 — SQLi in the PHP Web API abstraction layer
• CVE-2016-10134 — Legacy SQLi in the jsrpc.php component (unauthenticated)

🔗 nvd.nist.gov

3. 🔴 CISA Known Exploited Vulnerabilities Catalog

Intelligence reports demonstrating the real impact and active exploitation (in the wild) of legacy flaws in the jsrpc.php component.
🔗 cisa.gov/known-exploited-vulnerabilities-catalog

---

🧠 Prompt Engineering and "Scars"

Below are documented strategic interactions with the language model in NotebookLM, illustrating the iterative refinement of questions to obtain deep technical answers.

Prompt Evolution

🔴 Initial Prompt — Generic (Weak Result)

"Summarize for me the Zabbix vulnerabilities that are in the files."

❌ Problem: Superficial response, only listing the years of the flaws without explaining the root cause in the code or the affected parameters.

---

🟡 Refined Prompt 1 — Contextualized with Persona (Excellent Result)

"Acting as a senior application security (AppSec) analyst, examine the 
attached documents and extract the exact Zabbix PHP Frontend parameters that fail 
data sanitization in the case of CVE-2026-23921."

✅ Result: NotebookLM precisely mapped the dynamic concatenation flaw in the CApiService.php file associated with the sortfield ordering parameter, identifying the exact injection point in the SQL query.

---

🟢 Refined Prompt 2 — Practical Technical Scenario (Exceptional Result)

"Explain in a technical and structured manner, in topics, how an attacker with minimal 
API privileges manages to transform a time-based Blind SQLi in Zabbix into an 
Administrator Session Hijack."

✅ Result: The model generated a complete flow detailing the bit-by-bit extraction of hashes and tokens from the sessions table, validating the privilege escalation vector to RCE.

---

🩹 Scars — Difficulties and Learnings

#	Challenge Encountered	How I Solved It (Troubleshooting)
1	NotebookLM mixed concepts from the 2016 flaw (jsrpc.php) with the API logic of recent flaws (2024/2026)	I applied CVE restriction in the prompt, instructing the AI to segment responses by CVE identifiers and chronology, separating public endpoints (unauthenticated) from internal endpoints that require tokens
2	Initial responses were too generic about SQLi	I added a specialized persona ("acting as a senior AppSec analyst") to induce technical depth
3	Difficulty connecting SQLi to real operational impact	I used chain prompts — first extracted technical data, then requested the full attack narrative in steps

💡 Lesson learned: The quality of an AI's response is directly proportional to the quality and specificity of the prompt. Persona + context + constraints = precise technical results.

---

📝 Study Miniguide (Final Delivery)

📂 Structured Summaries

1. Anatomy of Recent API Flaws

The modern flaws in Zabbix are concentrated in the data abstraction layer of the Web API, written in PHP.

CVE-2026-23921 and CVE-2024-42327 — Root Cause:

API Request
       │
       ▼
 CApiService.php
       │
       ├── Parameter: sortfield
       │         │
       │         └── ❌ NO strict parameterization
       │
       ▼
 Dynamically assembled SQL query
       │
       ▼
 Database (MySQL / PostgreSQL)
       │
       └── SLEEP() / pg_sleep() → Time-based Blind SQLi

The attacker (with minimal API privileges) sends structured SQL payloads in the sortfield field. Since there is no parameterization, the command is concatenated directly into the query executed by the database. Through conditional delay functions (SLEEP(), pg_sleep()), it is possible to infer data from the sessions table character by character.

---

2. The Complete Attack Chain to RCE

Unlike conventional web systems where data theft is the end of the attack, in Zabbix database compromise almost always escalates to Remote Code Execution: