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AI Security Masterclass: Prompt Injection & LLM Security

Build, attack, and secure real-world LLM apps with RAG, tool calling, memory, AI agents, and Python.

  1. Topics
  2. Development
  3. Python Programming

AI Security Masterclass: Prompt Injection & LLM Security

InstructorArjun Vaid
Duration11h 42m
Students587
Rating0.0 (0)

More Related Topics

  • LLM Security & Protection38
  • Prompt Injection Defense19
  • Agentic AI Systems195
  • Retrieval-Augmented Generation264
  • Python Fundamentals343
  • Programming Fundamentals953
  • Advanced Python Concepts128
  • Django Web Framework312
  • Flask Framework64
  • Machine Learning (ML)1409
Sponsored
Price
$0.00
$14.99
Coupon
51/100 uses left
Last checked 50m ago
Access
Premium only
Upgrade to unlock this course deal

Coupon history

Comidoc has tracked 5 coupons for this course since 2026, last checked 50m ago.

Coupon codeDiscountAddedStatusLifetime
AUGFREE02100% offAug 1, 202603:58 PM UTCFully redeemed7d 13h
AUGFREE03100% offAug 1, 202603:58 PM UTCFully redeemed5d 11h
JULFREE03100% offJul 7, 202609:59 AM UTCExpired~29 daysRan full term
JULFREE02100% offJul 7, 202604:33 AM UTCExpired~29 daysRan full term
Comidoc Analysis

Defensive AI Engineering through Attack and Defense

Strengths

Comprehensive Security Lifecycle

The curriculum covers a wide range of attack vectors including prompt injection, RAG context poisoning, tool parameter injection, and persistent memory manipulation.

Architectural Defense Patterns

Instruction includes practical defense mechanisms such as source validation, context isolation, and human-in-the-loop approval workflows for autonomous agents.

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Limitations

Unverified Instructional Clarity

No substantive learner reviews are available to verify the effectiveness of the teaching style or the clarity of the technical explanations.

Best suited to

  • Python developers building LLM applications
  • AI engineers focusing on RAG security
  • Software engineers implementing agentic workflows

Less suited to

  • Learners seeking purely theoretical AI security studies
  • Developers without basic Python proficiency

Comidoc Score

6.5/10

Worth considering
Defined audience

Comidoc verdict

Python developers can progress from fundamental environment setup using Ollama toward advanced security architectures for AI agents. By integrating both offensive techniques and defensive programming, the curriculum provides a practical framework for securing modern LLM-driven features.

The technical depth is evident in the coverage of RAG pipelines, tool-calling permissions, and memory protection. This approach ensures that learners understand not just how to build an assistant, but how to harden it against specific vulnerabilities like indirect prompt injection or unauthorized tool execution.

This training is best suited for Python developers and AI engineers who want a hands-on, project-led experience in building secure, production-ready AI applications.

Score breakdown

Curriculum depth
8.0

The curriculum demonstrates depth across multiple specialized areas including RAG security, tool-calling vulnerabilities, and agentic workflows.

Applied learning
6.5

The path is project-led, culminating in the construction of a comprehensive AI Security Gateway and utilizing Docker for deployment.

Clarity & experience
5.0

No substantive sampled-review evidence was available to move teaching clarity away from a neutral assessment.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
6.5

The technical requirements for Python and the focus on LLM security align well with the declared target audience of developers and AI engineers.