AI Penetration
Testing
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FOR MORE DETAILS www.ignitetechnologies.in
AI Penetration
Testing
TABLE OF CONTENTS
AI AND LLM SECURITY MODULES
01
Introduction to Large Language Models (LLMs)
Overview of LLMs, their capabilities, limitations, and
real-world applications
LLM ARCHITECTURE
02
Deep dive into the structural design and functioning of
large language models, including training, tokenization,
and inference mechanisms.
LLM SECURITY PRINCIPLES
03
Core security concerns and challenges specific to the
deployment and operation of LLMs.
DATA SECURITY IN AI SYSTEMS
04
Best practices for safeguarding training and inference
data, including encryption, anonymization, and access
control.
MODEL SECURITY
05
Strategies to protect AI models from threats such as
model inversion, extraction, and poisoning.
AI Penetration
Testing
TABLE OF CONTENTS
INFRASTRUCTURE SECURITY
06
Ensuring the underlying hardware, cloud environments,
and networking components supporting LLMs are
secure and resilient.
OWASP TOP 10 FOR LLMS
07
Application of the OWASP Top 10 security
vulnerabilities tailored to LLM-based systems and AI
integrations.
LLM INSTALLATION AND DEPLOYMENT
08
Step-by-step guide for setting up and deploying LLMs
securely in development and production environments.
MODEL CONTEXT PROTOCOL (MCP)
09
Introduction to MCP and its role in maintaining safe and
consistent context handling in LLM applications.
PUBLISHING YOUR MODEL USING OLLAMA
10
Securely publishing and managing your custom LLMs
using the Ollama platform.
AI Penetration
Testing
TABLE OF CONTENTS
INTRODUCTION TO RETRIEVAL-AUGMENTED
11
GENERATION (RAG)
Understanding the RAG architecture and its use in
combining LLMs with external knowledge sources.
MAKING YOUR AI APPLICATION PUBLIC
12
Guidelines and precautions for exposing AI services to
external users or clients.
TYPES OF ENUMERATION USING AI
13
Exploration of enumeration techniques in cybersecurity
powered by AI for reconnaissance and threat
identification.
PROMPT INJECTION ATTACKS
14
Analysis of prompt injection vulnerabilities and
techniques for prevention and detection.
EXPLOITING LLM APIS: REAL-WORLD BUG
15
SCENARIOS
Examination of vulnerabilities and bugs commonly
found in LLM APIs, with practical examples.
AI Penetration
Testing
TABLE OF CONTENTS
PASSWORD LEAKAGE VIA AI MODELS
16
Risks and real-world cases where LLMs have
unintentionally leaked sensitive credentials.
INDIRECT PROMPT INJECTION TECHNIQUES
17
Advanced manipulation strategies to indirectly
influence model behavior through user-generated
content.
MISCONFIGURATIONS IN LLM DEPLOYMENTS
18
Identifying and mitigating common configuration errors
that can lead to security breaches.
EXPLOITATION OF LLM APIS WITH EXCESSIVE
19 PRIVILEGES
Assessing how overly permissive APIs can be abused to
perform unauthorized actions.
CONTENT MANIPULATION IN LLM OUTPUTS
20
Techniques used to manipulate LLM-generated
responses and how to defend against them.
AI Penetration
Testing
TABLE OF CONTENTS
DATA EXTRACTION ATTACKS ON LLMS
21
Investigating attacks aimed at extracting training data or
sensitive information from LLMs.
SECURING AI SYSTEMS
22
Holistic approaches to AI system security, covering
model, data, access, and deployment layers.
SYSTEM PROMPTS AND THEIR SECURITY
23 IMPLICATIONS
The role of system-level prompts and their influence on
model behavior and output integrity.
AUTOMATED PENETRATION TESTING WITH AI
24
Leveraging AI and LLMs for conducting automated
security assessments and vulnerability discovery.
+91 95993-87841
CONTACT US www.ignitetechnologies.in
