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  1. Free, publicly-accessible full text available April 27, 2026
  2. Deeply embedded systems powered by microcontrollers are becoming popular with the emergence of Internet-of-Things (IoT) technology. However, these devices primarily run C/C\({+}{+}\)code and are susceptible to memory bugs, which can potentially lead to both control data attacks and non-control data attacks. Existing defense mechanisms (such as control-flow integrity (CFI), dataflow integrity (DFI) and write integrity testing (WIT), etc.) consume a massive amount of resources, making them less practical in real products. To make it lightweight, we design a bitmap-based allowlist mechanism to unify the storage of the runtime data for protecting both control data and non-control data. The memory requirements are constant and small, regardless of the number of deployed defense mechanisms. We store the allowlist in the TrustZone to ensure its integrity and confidentiality. Meanwhile, we perform an offline analysis to detect potential collisions and make corresponding adjustments when it happens. We have implemented our idea on an ARM Cortex-M-based development board. Our evaluation results show a substantial reduction in memory consumption when deploying the proposed CFI and DFI mechanisms, without compromising runtime performance. Specifically, our prototype enforces CFI and DFI at a cost of just 2.09% performance overhead and 32.56% memory overhead on average.

     
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    Free, publicly-accessible full text available September 30, 2025
  3. To study the security properties of the Internet of Things (IoT), firmware analysis is crucial. In the past, many works have been focused on analyzing Linux-based firmware. Less known is the security landscape of MCU-based IoT devices, an essential portion of the IoT ecosystem. Existing works on MCU firmware analysis either leverage the companion mobile apps to infer the security properties of the firmware (thus unable to collect low-level properties) or rely on small-scale firmware datasets collected in ad-hoc ways (thus cannot be generalized). To fill this gap, we create a large dataset of MCU firmware for real IoT devices. Our approach statically analyzes how MCU firmware is distributed and then captures the firmware. To reliably recognize the firmware, we develop a firmware signature database, which can match the footprints left in the firmware compilation and packing process. In total, we obtained 8,432 confirmed firmware images (3,692 unique) covering at least 11 chip vendors across 7 known architectures and 2 proprietary architectures. We also conducted a series of static analyses to assess the security properties of this dataset. The result reveals three disconcerting facts: 1) the lack of firmware protection, 2) the existence of N-day vulnerabilities, and 3) the rare adoption of security mitigation. 
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    Free, publicly-accessible full text available August 14, 2025
  4. Free, publicly-accessible full text available August 14, 2025
  5. Free, publicly-accessible full text available August 12, 2025
  6. Free, publicly-accessible full text available February 26, 2025
  7. With the rapid expansion of the Internet of Things, a vast number of microcontroller-based IoT devices are now susceptible to attacks through the Internet. Vulnerabilities within the firmware are one of the most important attack surfaces. Fuzzing has emerged as one of the most effective techniques for identifying such vulnerabilities. However, when applied to IoT firmware, several challenges arise, including: (1) the inability of firmware to execute properly in the absence of peripherals, (2) the lack of support for exploring input spaces of multiple peripherals, (3) difficulties in instrumenting and gathering feedback, and (4) the absence of a fault detection mechanism. To address these challenges, we have developed and implemented an innovative peripheral-independent hybrid fuzzing tool called . This tool enables testing of microcontroller-based firmware without reliance on specific peripheral hardware. First, a unified virtual peripheral was integrated to model the behaviors of various peripherals, thus enabling the physical devices-agnostic firmware execution. Then, a hybrid event generation approach was used to generate inputs for different peripheral accesses. Furthermore, two-level coverage feedback was collected to optimize the testcase generation. Finally, a plugin-based fault detection mechanism was implemented to identify typical memory corruption vulnerabilities. A Large-scale experimental evaluation has been performed to show ’s effectiveness and efficiency. 
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  8. Although the importance of using static taint analysis to detect taint-style vulnerabilities in Linux-based embedded firmware is widely recognized, existing approaches are plagued by following major limitations: (a) Existing works cannot properly handle indirect call on the path from attacker-controlled sources to security-sensitive sinks, resulting in lots of false negatives. (b) They employ heuristics to identify mediate taint source and it is not accurate enough, which leads to high false positives. To address issues, we propose EmTaint, a novel static approach for accurate and fast detection of taint-style vulnerabilities in Linux-based embedded firmware. In EmTaint, we first design a structured symbolic expression-based (SSE-based) on-demand alias analysis technique. Based on it, we come up with indirect call resolution and accurate taint analysis scheme. Combined with sanitization rule checking, EmTaint can eventually discovers a large number of taint-style vulnerabilities accurately within a limited time. We evaluated EmTaint against 35 real-world embedded firmware samples from six popular vendors. The result shows EmTaint discovered at least 192 vulnerabilities, including 41 n-day vulnerabilities and 151 0-day vulnerabilities. At least 115 CVE/PSV numbers have been allocated from a subset of the reported vulnerabilities at the time of writing. Compared with state-of-the-art tools such as KARONTE and SaTC, EmTaint found significantly more vulnerabilities on the same dataset in less time. 
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