Techniques and frameworks for penetration testing AI systems focusing on vulnerabilities, model security, adversarial attacks, and risk assessment in deployed AI solutions.
Intermediate
100h
ai-pentest
cybersecurity
adversarial-attacks
model-security
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Milestone
Task
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Decision
Project
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Proof of Competence
0%
0 / 30 checkpoints declared
0/15 evidence needed for 100% — max 50% without proof
Foundations of AI Security
You can identify the primary security risks in an AI system architecture
Milestone
List at least 5 AI-specific vulnerabilities in a case study (documented with examples)
Checkpoint
You can map AI components to traditional cybersecurity controls and identify gaps
Milestone
Map AI system components to an attack surface model for pentesting (diagram or list)
Checkpoint
Create a checklist to adapt penetration testing methodology to AI environments
Project
AI Model Vulnerabilities
You can craft basic adversarial inputs to mislead a trained model
Milestone
Generate and test adversarial samples on an open-source classifier (success rate logged)
Checkpoint
Build an adversarial attack demo for a public AI model (image or text based)
Project
You can identify poisoned data and implement basic defense mechanisms
Milestone
Detect anomalous samples in a poisoned training dataset (document detections)
Checkpoint
Security Testing Tooling and Automation
You can run an AI attack framework to test model robustness
Milestone
Execute an adversarial robustness test on a sample model using one tool (capture report)
Checkpoint
Integrate an AI pentesting tool into a CI/CD pipeline for continuous assessment
Project
You can automate detection of AI weaknesses during standard pentest runs
Milestone
Write an automated script to scan for common AI model misconfigurations
Checkpoint
Advanced AI Attack Techniques
You can analyze model explanations to discover hidden vulnerabilities
Milestone
Use an interpretability toolkit to identify model biases or weaknesses in a test model
Checkpoint
Create a report demonstrating how explainability insights lead to novel attack vectors
Project
You can perform a controlled model extraction attack and assess data leakage risks
Milestone
Simulate an extraction attack on a hosted AI service and measure accuracy of cloned model
Checkpoint
Risk Assessment and Reporting
You can construct a detailed threat model specific to an AI system under test
Milestone
Document at least 3 high-risk threat scenarios in an AI deployment context
Checkpoint
Develop a threat modeling template customized for AI applications
Project
You can produce a comprehensive pentest report with actionable AI security recommendations
Milestone
Deliver a mock AI pentest report including risk levels, remediation advice, and testing evidence
Checkpoint
Specialized AI Pentest Scenarios
You can execute a penetration test targeting AI features within a web application ecosystem
Milestone
Conduct a pentest targeting AI-based access control on a demo web app (document vulnerabilities)
Checkpoint
Develop exploits for a vulnerable AI web authentication system
Project
You can assess and harden network security around AI infrastructure components
Milestone
Scan AI infrastructure for open ports and insecure configurations (report findings)
Checkpoint