Master Thesis Embedded Pentesting with AI Agents
Master Thesis Embedded Pentesting with AI Agents
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Job Description
- During your Master thesis you will advance research in AI-driven cybersecurity by developing innovative solutions for embedded system pentesting using large language model agents.
- You will design and implement a modular testbench architecture that enables AI agents to interact with embedded hardware through standardized interfaces including power supplies communication protocols (CAN Ethernet UART SPI I2C) as well as monitoring equipment.
- Furthermore you will implement a Model Context Protocol (MCP) server to create seamless communication between AI agents and embedded hardware components enabling autonomous security assessments.
- Additionally you will develop a specialized AI pentesting agent capable of device reconnaissance vulnerability identification and exploit development while documenting findings for interdisciplinary security teams.
- Finally you will evaluate your solution through comprehensive testing against real embedded devices from automotive and IoT domains comparing AI-driven approaches with traditional pentesting methodologies to validate effectiveness and identify areas for improvement.
Qualifications :
- Education: Master studies in the field of Computer Science or comparable with excellent academic performance
- Experience and Knowledge: background in security and/or embedded systems; knowledge of basic pentesting methods; programming skills in Python
- Personality and Working Practice: you are highly motivated to learn and have an independent working style
- Languages: very good in English or German
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Dr. Max Eisele (Functional Department)
Dr. Christopher Huth (Functional Department)
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
