AI Summary
Execute AI-focused penetration testing engagements, perform threat modeling, and develop AI-driven tools and methodologies. Collaborate with engineering and security teams to communicate findings and advise on secure AI model development and deployment best practices.
Key Highlights
AI-focused penetration testing
Threat modeling for AI-powered software systems
Development of AI-driven tools and methodologies
Technical Skills Required
Benefits & Perks
100% Remote Work
Long-term employment opportunity
Job Description
Role: AI/ML Penetration Tester
Location: 100% Remote (USA)
Duration: Long Term
No. of positions: - 9
Position Summary / Purpose: Overview of the basic function and purpose of the job, and how it contributes to the successful achievement of department and organization objectives.
- Execute AI-focused penetration testing engagements that include manual penetration testing of systems incorporating AI/ML, objective-based testing of AI-driven features, and coverage of both traditional and AI-centric attack surfaces.
- Perform threat modeling for AI-powered software systems, evaluate AI-related business logic, and conduct architecture reviews. Focus on adversarial ML vectors, prompt-based vulnerabilities, and other AI-specific security risks.
- Develop and improve AI-driven tools and methodologies for offensive security tasks such as discovery, exploitation, fuzzing, and adversarial ML testing, emphasizing web apps, APIs, and mobile clients.
- Demonstrate AI penetration testing findings to technical and non-technical audiences, including live demos.
- Collaborate with engineering, development, and security teams to communicate findings, lead remediation discussions, and advise on secure AI model development and deployment best practices.
- Research emerging AI attack techniques and evaluate their potential impact, identify vulnerabilities, and provide actionable recommendations to strengthen AI defenses.
- Collaborate with internal Red Teams, SOC analysts, and AI security researchers, sharing insights and data to enhance AI-driven offensive security methodologies. Refine existing AI red teaming approaches by integrating new adversarial ML techniques and proven exploitation tactics.
- Act independently on AI penetration testing with minimal oversight, guiding engagements from planning through execution and reporting.
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