相关论文: $\mu$AFL: Non-intrusive Feedback-driven Fuzzing fo…

The increasing complexity of embedded software has made comprehensive manual testing impractical, motivating the use of automated techniques such as fuzzing. Coverage-guided fuzzers like AFL++ have shown strong results for conventional…

Coverage-based graybox fuzzer (CGF), such as AFL has gained great success in vulnerability detection thanks to its ease-of-use and bug-finding power. Since some code fragments such as memory allocation are more vulnerable than others,…

Grey-box fuzzers such as American Fuzzy Lop (AFL) are popular tools for finding bugs and potential vulnerabilities in programs. While these fuzzers have been able to find vulnerabilities in many widely used programs, they are not efficient;…

Fuzzing has emerged as a powerful technique for finding security bugs in complicated real-world applications. American fuzzy lop (AFL), a leading fuzzing tool, has demonstrated its powerful bug finding ability through a vast number of…

Ever-increasing design complexity of System-on-Chips (SoCs) led to significant verification challenges. Unlike software, bugs in hardware design are vigorous and eternal i.e., once the hardware is fabricated, it cannot be repaired with any…

Fuzz testing (fuzzing) is a well-known method for exposing bugs/vulnerabilities in software systems. Popular fuzzers, such as AFL, use a biased random search over the domain of program inputs, where 100s or 1000s of inputs (test cases) are…

Software testing is becoming a critical part of the development cycle of embedded devices, enabling vulnerability detection. A well-studied approach of software testing is fuzz-testing (fuzzing), during which mutated input is sent to an…

Firmware serves as the critical interface between hardware and software in computing systems, making any bugs or vulnerabilities particularly dangerous as they can cause catastrophic system failures. While fuzzing is a promising approach…

As the complexity of logic designs increase, new avenues for testing digital hardware becomes necessary. Fuzz Testing (fuzzing) has recently received attention as a potential candidate for input vector generation on hardware designs. Using…

Emulation-based fuzzers enable testing binaries without source code, and facilitate testing embedded applications where automated execution on the target hardware architecture is difficult and slow. The instrumentation techniques added to…

The security of microcontrollers, which drive modern IoT and embedded devices, continues to raise major concerns. Within a microcontroller (MCU), the firmware is a monolithic piece of software that contains the whole software stack, whereas…

Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware vulnerability…

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…

Greybox fuzzing is one of the most useful and effective techniques for the bug detection in large scale application programs. It uses minimal amount of instrumentation. American Fuzzy Lop (AFL) is a popular coverage based evolutionary…

Dynamic testing or fuzzing of embedded firmware is severely limited by hardware-dependence and poor scalability, partly contributing to the widespread vulnerable IoT devices. We propose a software framework that continuously executes a…

Firmware fuzzing has gained attention for identifying firmware bugs. However, current approaches often directly integrate fuzzing tools for general software. General software receives input as it encounters I/O functions, but firmware input…

The increasing complexity of modern processors poses many challenges to existing hardware verification tools and methodologies for detecting security-critical bugs. Recent attacks on processors have shown the fatal consequences of…

Deep Learning (DL) frameworks have served as fundamental components in DL systems over the last decade. However, bugs in DL frameworks could lead to catastrophic consequences in critical scenarios. A simple yet effective way to find bugs in…

Modern embedded Linux devices, such as routers, IP cameras, and IoT gateways, rely on complex software stacks where numerous daemons interact to provide services. Testing these devices is crucial from a security perspective since vendors…

Exponential growth in embedded systems is driving the research imperative to develop fuzzers to automate firmware testing to uncover software bugs and security vulnerabilities. But, employing fuzzing techniques in this context present a…