richjj98/analise-vulnerabilidades-zabbix-notebooklmCaderno Temático NotebookLM: análise de vulnerabilidades SQL Injection (CVE-2024-42327, CVE-2026-23921) no Zabbix, com engenharia de prompts, cadeia de ataque até RCE e miniguia de hardening
Thematic Notebook developed as a deliverable for the DIO Project Challenge
Using NotebookLM as an active AI tool for curation, synthesis, and organization of knowledge in Cybersecurity
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.
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).
| 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 |
To ensure technical accuracy of the analyses in NotebookLM, official open sources and threat intelligence reports were selected:
Official documentation containing corrective patches, technical descriptions of scope, and CVSS severity matrix for system flaws.
🔗 zabbix.com/security_advisories
Detailed consultation on the records and specific attack vectors for the analyzed CVEs:
sortfield parameter in the API (critical, minimal authentication)jsrpc.php component (unauthenticated)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
Below are documented strategic interactions with the language model in NotebookLM, illustrating the iterative refinement of questions to obtain deep technical answers.
"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.
"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.
"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.
| # | 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.
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.
Unlike conventional web systems where data theft is the end of the attack, in Zabbix database compromise almost always escalates to Remote Code Execution:
┌─────────────────────────────────────────────────────────────┐
│ ATTACK CHAIN │
├─────────────────────────────────────────────────────────────┤
│ │
│ STEP 1: Time-based Blind SQLi │
│ ├── Payload in the sortfield field │
│ ├── Bit-by-bit inference via SLEEP() │
│ └── Extraction of tokens from the `sessions` table │
│ │
│ STEP 2: Session Hijacking │
│ ├── Cookie replacement with Admin token │
│ └── Full control of the Zabbix web interface │
│ │
│ STEP 3: Remote Code Execution (RCE) │
│ ├── Access to native "Scripts" functionality │
│ ├── Creation of malicious script (reverse shell) │
│ └── Execution on the server with zabbix user privileges │
│ │
│ RESULT: Access to the operating system terminal 🔓 │
└─────────────────────────────────────────────────────────────┘
| Measure | Description | Priority |
|---|---|---|
| 🔄 Immediate updating | Apply patches from Zabbix's fixed versions | 🔴 Critical |
| 🔒 Query parameterization | Ensure use of prepared statements throughout the API | 🔴 Critical |
| 🌐 Network isolation | Never expose Zabbix frontend directly to the internet | 🔴 Critical |
| 🛡️ WAF | Implement rules to detect payloads in the sortfield parameter | 🟠 High |
| 👤 Principle of least privilege | Limit API user permissions to the minimum necessary | 🟠 High |
| 📋 Log auditing | Monitor anomalous requests with SLEEP() in the API layer | 🟡 Medium |
| Term | Definition |
|---|---|
| Blind SQL Injection | Attack where the application does not display the extracted data directly. The attacker asks "true/false" questions to the database and observes changes in response behavior |
| Time-based Blind SQLi | Subcategory of blind injection where timed delay functions (e.g., SLEEP) are used to infer data based on server response time |
| RCE (Remote Code Execution) | Critical flaw that allows executing arbitrary commands on a remote machine's operating system |
| Session Hijacking | Technique of stealing an authenticated session to assume another user's identity without needing their password |
| Hardening | Process of mapping, analyzing, and closing security gaps to reduce the attack surface |
| CVSS | Common Vulnerability Scoring System — standardized vulnerability severity scoring system (0 to 10) |
| Prepared Statements | Programming technique that separates SQL code from user data, preventing SQL injection |
| WAF | Web Application Firewall — protection system that filters malicious HTTP requests before they reach the application |
Use these structured prompts in your NotebookLM for review or knowledge expansion:
Analyze the notebook notes and build a 3-paragraph executive summary focused
on the IT directorate, explaining the financial and operational risks of keeping
an outdated Zabbix server exposed to the internal corporate network.
Based on the mitigation techniques mentioned in the material, describe step by step
how to configure a generic WAF (Web Application Firewall) rule to
identify attack signatures targeting the sortfield parameter.
Simulate an Incident Response (IR) Report for an organization that detected
active exploitation of CVE-2024-42327 on its Zabbix server, including
timeline, estimated impact, and immediate containment plan.
Compare the 2016 flaws (jsrpc.php) with the 2024/2026 flaws in the Zabbix API,
highlighting: evolution of the attack vector, change in authentication requirement, and
impact on the corporate exposure surface.
Rich JJ98 — DIO Project Challenge
NotebookLM Thematic Notebook: Application and Infrastructure Security
Made with 🛡️ for the DIO Project Challenge | NotebookLM + Cybersecurity