AI Security Expert (Live Certification Program)
Become a job ready AI red teamer. The Techonquer Certified AI Security Expert program turns you into a hands-on AI red teamer who can break, test and secure modern AI systems. Start from the fundamentals, master offensive attacks across ML, LLMs, RAG, agents and multimodal AI, then learn to defend them like a pro. Every skill maps to industry frameworks such as MITRE ATLAS and the OWASP Top 10 for LLM Applications, so you finish job ready with proof to show for it.
- AI Red Teaming
- LLM and Prompt Injection Attacks
- RAG and AI Agent Exploitation
- Adversarial Machine Learning
- MITRE ATLAS and OWASP LLM Top 10
- AI Blue Team Defense
- Basic security or programming knowledge is helpful
- No prior AI experience required
- Laptop and Internet
Who is the AI Security Expert Program For?
AI is everywhere, and so are its attackers. This program is built for people who want to break, test and secure modern AI systems with a hands-on, methodology driven approach that maps to real industry frameworks.
Security Engineers and Pentesters
Professionals moving into AI security who want to add AI red teaming to their offensive security skill set.
ML and AI Engineers
Builders who need to secure the AI systems they design, train and deploy against real world attacks.
Red Teamers and Bug Bounty Hunters
Offensive security practitioners expanding into AI targets, prompt injection, and LLM vulnerability discovery.
Students, SOC Analysts and Blue Teamers
Learners building an AI security career and defenders protecting AI powered products through monitoring and guardrails.
09 Modules. 140+ Topics.
A complete path from AI fundamentals to advanced offensive AI red teaming and blue team defense. Every skill maps to MITRE ATLAS and the OWASP Top 10 for LLM Applications so you finish job ready.
AI Foundations
Core concepts of artificial intelligence and modern generative AI systems.
- Introduction to AI, machine learning and deep learning
- Introduction to generative AI
- Supervised, unsupervised and reinforcement learning
- Large language model fundamentals
- Neural networks, weights, biases and training
- Transformers, tokens, embeddings, context windows
- Training vs inference
- Prompting basics and model behavior
- Model inputs, outputs and prediction pipelines
- LLM APIs, inference endpoints and parameters
AI Red Teaming Fundamentals
The foundations of AI red teaming and how attackers analyze AI systems.
- Introduction to AI security and AI red teaming
- OWASP Top 10 for LLM Applications
- AI safety vs AI security vs AI ethics
- Identifying AI components in web apps
- AI threat landscape and real world attacks
- Mapping AI trust boundaries
- Understanding AI attack surfaces
- AI red team lab setup
- MITRE ATLAS framework overview
- Overview of AI red teaming tools
Reconnaissance and Attack Surface Discovery
Gathering information about AI systems before launching attacks.
- Identifying AI powered features in applications
- Mapping data sources used by AI systems
- Fingerprinting LLMs and ML models
- Identifying inputs, tools, plugins, integrations
- Discovering hidden AI endpoints and APIs
- Understanding AI permissions and access boundaries
- Extracting system prompts and hidden instructions
- Preparing attack paths for AI exploitation
- Analyzing model behavior through prompt testing
Traditional Machine Learning Red Teaming
Offensive techniques used against classical machine learning models.
- Introduction to adversarial machine learning
- Model extraction and model stealing
- Data poisoning attacks
- Query based model replication
- Label flipping and training data manipulation
- Surrogate model creation
- Backdoor attacks in ML models
- Membership inference attacks
- Dataset contamination and integrity attacks
- Model inversion attacks
- Evasion attacks and adversarial examples
- Training data and PII extraction
- White box vs black box attacks
- ML supply chain and malicious pretrained models
- FGSM and PGD attack techniques
- Pickle deserialization and model file exploitation
- Fooling image, text and tabular models
Large Language Model Red Teaming
Attacking LLMs, chatbots and prompt based AI applications. This is the core offensive module of the program.
- Introduction to LLM vulnerabilities
- Multilingual prompt attacks
- Direct prompt injection
- Indirect prompt injection
- Instruction override attacks
- Hidden instructions in documents
- System prompt manipulation
- Poisoned web content attacks
- Context hijacking
- Email and file based prompt injection
- Bypassing basic AI restrictions
- System prompt leakage
- Advanced jailbreaking techniques
- Training data memorization attacks
- Persona based jailbreaks
- Sensitive data extraction from LLMs
- Role playing exploits
- Exfiltration via markdown, links, tool output
- Encoding bypasses with Base64, ROT13, hex
- LLM red teaming exercises and challenges
- Payload splitting and multi step jailbreaks
Advanced Generative AI System Red Teaming
Attacks against modern AI architectures such as RAG systems, AI agents and multimodal AI.
- Retrieval augmented generation architecture
- Remote code execution through agent capabilities
- Red teaming RAG applications
- Resource exhaustion and infinite loop attacks
- Document poisoning
- Multimodal AI red teaming
- Vector database manipulation
- Vision language model attacks
- Retrieval hijacking
- Hidden text inside images
- Access control bypass in RAG systems
- Adversarial image prompts
- Cross tenant data leakage in AI apps
- Audio based injection attacks
- Red teaming AI agents
- Fine tuning and alignment exploitation
- Tool use exploitation
- Safety alignment bypass and reward hacking
- Function calling abuse
- LoRA adapter security risks
- Command injection through AI agents
- Session and memory abuse in AI apps
- SSRF attacks via AI tools
AI Red Team Automation and Operations
Scaling AI red teaming and running structured offensive security assessments.
- Automated AI vulnerability scanning
- Kubernetes and container risks for AI
- Prompt fuzzing techniques
- Offensive use of AI in cyberattacks
- Automated jailbreak testing
- AI powered phishing
- Using Garak for LLM security testing
- Deepfake based social engineering
- Using Promptfoo for prompt evaluation
- Voice cloning attacks
- Custom Python scripts for AI attacks
- AI assisted malware generation
- Chaining AI with web vulnerabilities
- Scoping AI red team engagements
- XSS through AI outputs
- Rules of engagement
- SQL injection via AI generated queries
- Legal and ethical considerations
- SSRF through AI connected tools
- Threat actor emulation
- MLOps and AI infrastructure attacks
- Multi stage AI attack simulation
- ML pipeline exploitation
- Exploit chaining and risk prioritization
- Model registry attacks
- Evidence collection and proof of concept
- CI/CD poisoning
AI Red Team Reporting and Risk Communication
Turning technical attacks into professional red team reports.
- AI red team reporting structure
- Severity scoring for AI vulnerabilities
- Executive summary creation
- Business impact analysis
- Technical vulnerability documentation
- Mapping findings to OWASP and MITRE ATLAS
- Attack scenario explanation
- Remediation recommendations
- Proof of concept presentation
- Presenting AI security risks to stakeholders
Blue Team AI Defense and Monitoring
Defensive security measures to protect AI systems.
- Introduction to AI blue teaming
- Adversarial training basics
- Input validation for AI applications
- Safety alignment and model hardening
- Output filtering and content controls
- AI logging and audit trails
- LLM firewalls and guardrails
- Detecting prompt injection attempts
- Secure prompt design
- Monitoring abnormal model behavior
- System prompt protection techniques
- Drift detection and anomaly detection
- Access control for AI systems
- AI incident response
- Secure RAG architecture
- Building an AI security monitoring strategy
- Secure AI agent design
Watch Demo Session
Get a quick preview of our live AI Security training methodology, instructor teaching style, and real world hands-on approach before enrolling.
Chitra Karanam
Co-Founder & CEO, Techonquer (TQ)
Our Placed Students
Success stories of learners who transformed their careers through Techonquer's trainings
Placement Assistance
Our placement assistance is focused on making you job-ready by building your technical confidence, interview skills, and real world understanding of AI red team and blue team hiring requirements.
ATS-Friendly CV Preparation
Build a professional, ATS-optimized resume tailored for AI Security Engineer, AI Red Teamer and AI Security Analyst job roles after completing the program.
Monthly Mock Interview Sessions
Every month, practice real technical and HR interviews covering AI red teaming scenarios, prompt injection, LLM and RAG attacks, adversarial ML and situational AI security questions.
Interview Confidence Building
Dedicated sessions to improve communication, articulation of hands-on lab experience, and overall interview mindset, preparing you for both technical and behavioral rounds.
Job Opportunity Sharing
Throughout the program, relevant AI security job openings, internship listings, referrals and hiring updates are actively shared with all enrolled students.
What You Will Be Able To Do
Hands-on offensive and defensive AI security skills you will gain through the Techonquer Certified AI Security Expert program.
Map AI Attack Surfaces
Model AI threat surfaces and trust boundaries using MITRE ATLAS and the OWASP Top 10 for LLM Applications.
Attack LLM Applications
Execute prompt injection, jailbreaks, system prompt leakage and data exfiltration on real LLM apps.
Attack ML Models
Perform data poisoning, evasion, model extraction and model inversion against classical ML systems.
Red Team RAG and Agents
Attack RAG systems, AI agents and multimodal models including tool abuse, SSRF and command injection.
Automate AI Testing
Automate red teaming with Garak, Promptfoo and custom Python tooling for scalable assessments.
Build AI Defenses
Design guardrails, input and output filtering, monitoring and AI incident response as a blue teamer.
Write Professional Reports
Produce professional AI red team reports with severity scoring, business impact and remediation.
Earn a Verifiable Certificate
Finish job ready with the Techonquer Certified AI Security Expert credential and proof to show for it.
Frequently Asked Questions
When does the training start and on which days?
The program starts on 10 September and runs 3 days per week on Friday, Saturday and Sunday with live instructor-led sessions.
Is this live training or recorded?
This is a 100% live training program. All sessions are instructor-led and lifetime recording access is provided for revision.
Which tools and frameworks are covered?
You will get hands-on experience with Garak, Promptfoo, Python for AI security testing, Hugging Face, local LLM deployment tools and the Adversarial Robustness Toolbox, all mapped to MITRE ATLAS and the OWASP Top 10 for LLM Applications.
What is the fee structure?
The total program fee is Rs 9,999, payable as Rs 4,999 at registration and Rs 5,000 after the first month. If you choose one-time full payment, you get Rs 1,000 off and pay only Rs 8,999.
Do I need prior AI experience?
No. The program starts from AI fundamentals and moves to advanced red teaming, so it is beginner friendly. Basic security or programming knowledge is helpful but not required.
What certification will I receive?
On successful completion you earn the Techonquer Certified AI Security Expert credential, validating hands-on capability across the full AI attack and defense lifecycle.
Will I get mentor support?
Yes, you will receive 1-to-1 mentor support from Chitra Karanam throughout the training to help you understand concepts and clear doubts.
Are the seats limited?
Yes, this program has limited seats to ensure personalized attention, practical guidance, and effective mentor support for every student.