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Provide ma AI Pentesting Checklist

1. Pre-Pentesting Preparation

Define the scope of the pentest.

Identify AI systems and components to be tested.

Gather documentation on AI models, architectures, and APIs.

Establish rules of engagement and obtain necessary approvals.

Assemble the pentesting team and assign roles.

2. Reconnaissance

Collect information about the AI system’s infrastructure.

Identify data sources used for training the AI models.

Analyze the AI system's input and output behavior.

Gather information on third-party libraries and dependencies.

3. Vulnerability Assessment

Review the AI model for common vulnerabilities (e.g., adversarial attacks).

Assess the data pipeline for data poisoning vulnerabilities.

Evaluate the security of APIs and endpoints used by the AI system.

Check for misconfigurations in cloud or hosting environments.

4. Exploitation

Attempt adversarial attacks to manipulate AI model predictions.

Test for data poisoning by injecting malicious training data.

Exploit API vulnerabilities to gain unauthorized access or control.

Assess the AI system's response to unexpected or malformed inputs.

5. Post-Exploitation

Evaluate the impact of successful exploitation on the AI system.

Analyze system functionality post-exploitation.
Determine changes in performance and behavior.
Identify compromised components and services.
Assess the integrity of AI models and data.
Evaluate impact on user trust and compliance.

Identify potential data breaches resulting from the exploitation.

Review access logs for unauthorized data access.
Identify sensitive data exposed during exploitation.
Assess the risk of data leakage to external parties.
Determine regulatory implications of data breaches.
Document affected datasets and potential exposure.

Document findings and evidence of vulnerabilities and exploits.

Collect screenshots and logs of exploitation.
Detail the steps taken during the pentest.
Catalog vulnerabilities using a standardized format.
Ensure evidence is clear and reproducible.
Compile a report for stakeholders and remediation teams.

Assess the risk associated with identified vulnerabilities.

Evaluate the likelihood of exploitation.
Determine the potential impact on the AI system.
Prioritize vulnerabilities based on risk levels.
Consider environmental factors and threat landscape.
Provide recommendations for mitigation strategies.

6. Reporting

Compile a comprehensive report detailing findings and recommendations.

Include evidence of vulnerabilities, exploitation methods, and impacts.

Provide remediation strategies for identified issues.

Present findings to stakeholders and management.

7. Remediation and Retesting

Collaborate with development teams to remediate identified vulnerabilities.

Implement security best practices for AI model training and deployment.

Conduct retesting to verify the effectiveness of remediations.

Update documentation and processes based on lessons learned.

8. Continuous Monitoring and Improvement

Establish a monitoring plan for ongoing security of AI systems.

Schedule regular pentests and security assessments.

Stay updated with the latest AI security trends and vulnerabilities.

Promote a culture of security awareness within the organization.

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