🛠️ Built-in Prerequisites Bridge

Build Your Foundation First

To master secure AI pipelines and LLM hacking, you must understand how systems communicate. We provide full foundation classes from scratch:

Why Do You Need These Foundations?

AI Security requires a strong base in Python, Web APIs, and Linux environments. To parse models, secure vector databases, build custom jailbreak scripts, or audit pipeline servers, you must first master Python scripting, web payload injection, and host terminal commands. Without these systems fundamentals, securing AI applications is impossible.

STEP 01

Python Scripting

Master data handling, parsing libraries, object serialization, and secure key operations in Python.

STEP 02

Web & API VAPT

Understand API endpoints, payload injection vectors, header fields, and parameter manipulation.

STEP 03

Linux Systems

Master terminal scripting, processes command trails, server configs, and host privileges.

📚 Curriculum Syllabus

20 Advanced Modules

MODULE 1

AI & Machine Learning Fundamentals

Understand machine learning lifecycles. Learn regression models, neural networks architectures, and base training steps.

#Supervised Learning #Neural Networks #Training Telemetry

MODULE 2

The OWASP Top 10 for LLM Apps

Comprehensive mapping of the top security bugs in Large Language Model applications according to the OWASP framework.

#OWASP LLM 10 #System Vulnerabilities #Risk Mitigations

MODULE 3

Direct Prompt Injection (System Hack)

Bypass system prompt developer constraints. Force LLMs to leak original prompts and hidden administrative parameters.

#System Prompt Leakage #Constraint Bypassing #Prompt Hijacking

MODULE 4

Indirect Prompt Injection (Data Attacks)

Inject commands dynamically through external files, webpages, and emails read by autonomous AI processing endpoints.

#External Data Attacks #Data Extraction #Third-Party Payloads

MODULE 5

LLM Jailbreaking & Guardrails Bypassing

Master complex Jailbreaking techniques inspired by HackTheBox. Deploy Do-Anything-Now (DAN) prompts and roleplay payloads.

#DAN Payloads #Roleplay Evasions #Guardrails Bypass

MODULE 6

RAG Hijacking & Vector Exploits

Compromise Retrieval-Augmented Generation loops. Poison vector database tables to deliver malicious injection payloads.

#RAG Poisoning #Vector Database Hack #Document Injections

MODULE 7

Training Data Poisoning

Attack ML pipelines during base learning. Poison training sets to inject hidden triggers and custom backdoors.

#Backdoor Injections #Trigger Detections #Dataset Poisoning

MODULE 8

Model Inversion & Inference Attacks

Reconstruct sensitive training data. Run model inversion to extract private user files, HIPAA data, or financial keys.

#Inference Auditing #Privacy Leaks #Reconstruction Logs

MODULE 9

Model Extraction & Weights Theft

Extract deep IP parameters. Reconstruct functional copies of private APIs and extract sensitive model weights.

#API Extraction #Weights Harvesting #Model Duplication

MODULE 10

Insecure Output Handling & RCE

Simulate critical output flaws. Exploit raw LLM output passed to system shells, executing Remote Code Execution (RCE).

#Shell Command Injection #System Shell Executions #Output Validation

MODULE 11

Insecure Plugin Design & Tool Hacking

Audit autonomous tool calls. Force models to trigger internal API endpoints, delete databases, or send spam.

#API Delegation Flaws #Autonomous Tool Exploits #Spawning Executions

MODULE 12

Denial of Service in LLMs

Exhaust server capacity. Deploy recursive prompts, token exhaustion triggers, and resource-heavy mathematical attacks.

#Token Exhaustion #Recursive Prompt Loops #Resource Starvation

MODULE 13

Attacking ML Pipelines (MLflow & Jupyter)

Audit pipeline dashboards. Compromise unauthenticated Jupyter notebook servers and exploit MLflow dashboards.

#Jupyter Notebook RCE #MLflow Dashboard Audits #Host Privilege Escalation

MODULE 14

Hugging Face Model Audits

Analyze community models for malware. Intercept and exploit insecure Python pickle serialization in uploaded weights.

#Pickle Deserialization RCE #HF Hub Investigations #Malware Weight analysis

MODULE 15

Safe Model Serialization

Implement secure model formats. Migrate legacy weights into modern safetensors files to prevent loading-time executions.

#Safetensors Formats #Weight Verification #Safe Model Deployments

MODULE 16

LLM Firewalls & Input Sanitization

Implement modern security shields. Configure NVIDIA NeMo Guardrails, Llama Guard, and meta filters to sanitize inputs.

#NVIDIA NeMo Guardrails #Llama Guard filters #Real-time Input Filtering

MODULE 17

Secure RAG & Vector Database Hardening

Secure enterprise document retrieval. Enforce strict tenant namespaces in Pinecone, ChromaDB, and configure read rules.

#Vector Namespace Security #Pinecone Hardening #Metadata Filtering

MODULE 18

Auditing Autonomous AI Agents

Audit self-correcting agent chains. Identify infinite planning loops, verify API execution boundaries, and lock write privileges.

#Agentic Loop Checks #Write Privilege Isolation #Sandbox execution

MODULE 19

Privacy-Preserving AI Deployments

Deploy private pipelines. Implement Differential Privacy, Federated Learning rules, and anonymize model training logs.

#Differential Privacy #Federated Learning keys #Data Anonymization

MODULE 20

AI Governance & Red Teaming Reports

Establish corporate AI safety guidelines. Map model risks against ISO 42001, and compile formal AI security audit reports.

#ISO 42001 compliance #Red Teaming reporting #Safety Auditing metrics

🎮 Gamified Cyber Labs

TryHackMe & HackTheBox Live Labs

Study with hands-on labs heavily inspired by cutting-edge AI security challenges from TryHackMe and HackTheBox.

PHASE 01: BEGINNER

TryHackMe AI Sandbox

Bypass direct prompt protections. Work through custom room models mimicking actual TryHackMe rooms to extract hidden flag keys and administrative master parameters.

Direct prompt injections & roleplay

OWASP Top 10 LLM model audits

Chatbot jailbreak scenarios

Target Labs: THM OWASP LLM Room

PHASE 02: INTERMEDIATE

HackTheBox LLM Inversion

Compromise autonomous tools connected to LLMs. Hijack retrieval engines (RAG) by inserting invisible payload elements inside web pages read by live AI scraper engines.

Indirect prompt injection via HTML scrapers

RAG hijacking & vector poisoning

Autonomous tool calling hijack loops

Target Labs: HTB Jailbreak Machine

PHASE 03: ELITE AUDIT

ML Pipeline Shell Escapes

Compromise unauthenticated development servers. Perform pickle deserialization attacks on raw weights files to gain administrative root shells on machine learning servers.

Pickle Deserialization RCE in PyTorch/HF

Jupyter RCE & unauthenticated MLflow exploits

NVIDIA NeMo Guardrails evasions

Target Labs: HTB Jupyter-RCE Host

Student Success Reviews

See how our alumni in Surat transformed their careers inside CyberEdu VAPT tracks.

Sneha Patel

AI Security Researcher @ CrowdStrike

"The Hugging Face pickle deserialization and prompt injection evasion labs are mind-blowing. Truly matches the hard labs of HackTheBox and TryHackMe!"

Karan Dhaduk

LLM Engineer @ TCS

"Securing vector databases (ChromaDB) and deploying NVIDIA NeMo Guardrails are extremely relevant for my day-to-day enterprise work."

Priya Gajera

AI Red Teamer @ Wipro

"The bridge foundation programs in Python scripting and Web APIs was extremely solid, helping me jump straight into jailbreaking scripts."

Raj Suhagiya

SecOps Architect

"Loved the Jupyter RCE and MLflow pipeline attack simulations. It gives true practical security validation skills."