Automotive Embedded Security Tester (BH ID)

• Design & Execute Campaigns: Build and execute comprehensive penetration testing campaigns against a wide variety of automotive embedded targets.
• Advanced Fuzzing: Configure and deploy targeted fuzzing frameworks (e.g., AFL++, libFuzzer, Peach, Defensics) against vehicle computers, ECUs, and clusters.
• Vulnerability Discovery: Uncover memory corruption vulnerabilities (buffer overflows, use-after-free), resource exhaustion, and complex logic flaws that automated static analyzers often miss.

Comprehensive Wireless & Wired Protocol Analysis

• Wired Vehicle Networks: Intercept, manipulate, and inject traffic across internal wired topologies, including CAN, CAN-FD, Automotive Ethernet (SOME/IP, DoIP), LIN, and FlexRay. You will utilize industry-standard tools like Vector CANoe/CANalyzer and Vehicle Spy.
• Wireless Ecosystems: Aggressively analyze and exploit vulnerabilities across every wireless communication interface. This includes deep-dive assessments of Bluetooth/BLE, Wi-Fi (802.11), Cellular networks (4G/LTE, 5G, and C-V2X), UWB, NFC, and traditional RF/Keyless Entry Systems (RKE/PEPS) using Software Defined Radios (SDRs like HackRF, USRP).

Hardware & Firmware Reverse Engineering

• Physical Attack Vectors: Conduct hands-on, hardware-level security testing to identify physical attack vectors.
• Hardware Debugging & Exploitation: Utilize tools like Logic Analyzers, Bus Pirate, J-Link, and UART/JTAG/SPI debuggers, side-channel analysis (SCA), and voltage/clock fault injection techniques.
• Firmware Analysis: Extract firmware from flash memory for subsequent reverse engineering and static analysis using disassemblers like IDA Pro.

AI-Enhanced Fuzzing and Vulnerability Discovery

• Develop and apply AI-driven fuzzing techniques, using machine learning to intelligently guide test case generation and uncover complex vulnerabilities in vehicle software.
• Utilize ML models to perform automated analysis of source code and binaries, identifying potential zero-day vulnerabilities that evade traditional static and dynamic analysis tools.

Automated Anomaly Detection in Vehicle Networks

• Implement and manage machine learning systems to analyze real-time data from CAN, Automotive Ethernet, and wireless channels, automatically detecting anomalous patterns indicative of a cyberattack.

Adversarial AI/ML System Testing

• Conduct security assessments of on-board AI/ML systems (e.g., those used for perception, sensor fusion, or decision-making in autonomous driving).
• Design and execute adversarial attacks (e.g., data poisoning, evasion attacks) to test the resilience and integrity of automotive AI models.

Strategic Remediation

• Actionable Reporting: Document findings in meticulous, highly technical reports that include mitigation strategies.
• Engineering Collaboration: Partner directly with other security tester/consultants to craft actionable, robust remediation strategies that fix the root cause of vulnerabilities.

• Automotive industry background required
• Experience with Electronic Control Units (ECUs)
• Strong understanding of CAN (Controller Area Network) protocols

Technical Skills

1. Penetration testing experience
2. Must have experience beyond fuzz testing
3. Looking for candidates with security testing experience in one or more of the following areas:

• USB
• Wireless communications
• Bluetooth
• Similar embedded/connected device technologies, * Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Computer Engineering, or a heavily related technical discipline.
• Proven experience in applying AI/ML techniques to cybersecurity challenges, such as intelligent fuzzing, anomaly detection, or securing machine learning systems.
• 3+ years of hands-on experience in penetration testing, vulnerability research, or reverse engineering, specifically focused on automotive embedded systems, IoT devices, or specialized custom hardware.

Deep Technical Expertise & Certifications

• Deep understanding of automotive E/E architectures, RTOS (e.g., QNX, VxWorks, AUTOSAR OS), and POSIX-based systems (Automotive Linux).
• Familiarity with automotive microcontrollers (e.g., Infineon AURIX TriCore, Renesas RH850, ARM Cortex-R/M) and hardware security modules (HSM/SHE).
• Strong grasp of industry-standard cybersecurity regulations and frameworks, specifically ISO/SAE 21434, UNECE WP.29 R155, and MITRE Telecommunication&CK.
• Knowledge of common machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and their application in a security context.

Understanding of adversarial ML concepts and defenses.

• Preferred Certifications: OSCP, OSCE, OSWE, eCPTX, GXPN, or specialized automotive/IoT security certifications.

Programming & Tooling Proficiency

• Proficiency in scripting and low-level programming languages such as Python, C/C++, Bash, or Assembly (ARM/x86/TriCore).
• Experience with data science and machine learning libraries within Python (e.g., Pandas, NumPy).
• Extensive hands-on experience with hardware/software testing tools (e.g., Oscilloscopes, Wireshark, Burp Suite, GNU Radio, Binwalk).