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Florian Hofhammer

Publications and source records attributed to Florian Hofhammer.

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Reactive Peripheral Modeling for Faithful Firmware Rehosting

Rehosting enables tight control and introspection for firmware testing, but existing approaches largely fail to reach deeper application states and cannot drive embedded protocol stacks beyond early-stage initialization. This limitation reflects a broader weakness in current rehosting techniques: their inability to faithfully model complex peripheral semantics and dependencies. In particular, existing work typically relies on passive approximations of peripheral behavior and overlooks three key aspects: (i) the interplay among interrupts, MMIO, and DMA; (ii) implicit state transitions within peripherals; and (iii) interactions across multiple peripherals. To address this challenge, we propose Reactive Peripheral Modeling (RPM), an abstraction that models hardware peripherals as reactive and stateful systems. RPM captures peripheral behavior using event-condition-action semantics, enabling faithful representation of interrupt, MMIO, and DMA scheduling, implicit state transitions, and cross-peripheral interactions. We implement RPM in Bluezz for BLE firmware rehosting and fuzzing, and show that reactive modeling is necessary to reach deep protocol states. We evaluate Bluezz on representative BLE stacks, including NimBLE, Zephyr, and Nordic SoftDevice, a closed-source commercial stack. Across 18 targets, Bluezz achieves an average basic-block coverage more than 2.6 times that of prior state-of-the-art rehosting approaches. Unlike prior approaches, which remain largely confined to advertising and scanning logic, Bluezz reliably exercises connected BLE states and uncovers five previously unknown vulnerabilities that manifest only after connection establishment. Finally, we show that RPM generalizes beyond BLE to other embedded firmware running on different MCUs.

cs.CR

EmbedFuzz: High Speed Fuzzing Through Transplantation

Dynamic analysis and especially fuzzing are challenging tasks for embedded firmware running on modern low-end Microcontroller Units (MCUs) due to performance overheads from instruction emulation, the difficulty of emulating the vast space of available peripherals, and low availability of open-source embedded firmware. Consequently, efficient security testing of MCU firmware has proved to be a resource- and engineering-heavy endeavor. EmbedFuzz introduces an efficient end-to-end fuzzing framework for MCU firmware. Our novel firmware transplantation technique converts binary MCU firmware to a functionally equivalent and fuzzing-enhanced version of the firmware which executes on a compatible high-end device at native performance. Besides the performance gains, our system enables advanced introspection capabilities based on tooling for typical Linux user space processes, thus simplifying analysis of crashes and bug triaging. In our evaluation against state-of-the-art MCU fuzzers, EmbedFuzz exhibits up to eight-fold fuzzing throughput while consuming at most a fourth of the energy thanks to its native execution.

cs.CR

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes. We conceptualize these challenges through the lens of vulnerability, the potential for university assessments and learning outcomes to be impacted by student use of generative AI. We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses. Specifically, we compile a novel dataset of textual assessment questions from 50 courses at EPFL and evaluate whether two AI assistants, GPT-3.5 and GPT-4 can adequately answer these questions. We use eight prompting strategies to produce responses and find that GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions. When grouping courses in our dataset by degree program, these systems already pass non-project assessments of large numbers of core courses in various degree programs, posing risks to higher education accreditation that will be amplified as these models improve. Our results call for revising program-level assessment design in higher education in light of advances in generative AI.

cs.CY