Building and evaluating security tools for IoT firmware requires targets whose weaknesses are known in ad- vance and can be reproduced in a running device. Two problems remain: (1) firmware collected from real-world products provides realistic software, but hardware dependencies and undocumented defects make it difficult to establish what a security tool should be expected to find; and (2) existing benchmarks typically collect sets of vulnerable or misconfigured firmware images without providing purpose-built vulnerable services with explicit weaknesses that a security analysis tool can target. We present IoTPwnBench, a synthetic, modular benchmark that brings these requirements together. IoTPwnBench contains 716 vulnerable firmware images covering 86 weakness scenarios and 28 CWE classes across 11 device profiles, including industrial controllers, health monitors, and household devices. Each vulnerable image runs a plausible device application with simulated sensors and actuators that generate live readings over its native interfaces while exposing one target weakness mapped to a primary CWE. A documented exploitation payload and observable signal establish the expected behavior independently of the evaluated tool. Every vulnerable image is paired with a corrected twin that preserves the device and service while removing that weakness. Researchers can therefore compare the same target before and after correction to assess detection and false positives for the target weakness. The benchmark produces kernel and filesystem binaries for static inspection and dynamic testing under QEMU. Device profiles and weakness modules remain separate, allowing the same scenario to be reused across device contexts. Vulnerable targets were validated manually, and the open source build workflow regenerates the firmware bit-for-bit.

翻译：暂无翻译