相关论文: INSTILLER: Towards Efficient and Realistic RTL Fuz…
Verification is a critical process for ensuring the correctness of modern processors. The increasing complexity of processor designs and the emergence of new instruction set architectures (ISAs) like RISC-V have created demands for more…
Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, which can reduce exploration efficiency and lead to redundant…
Directed fuzzing is a critical technique in cybersecurity, targeting specific sections of a program. This approach is essential in various security-related domains such as crash reproduction, patch testing, and vulnerability detection.…
Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation. Unfortunately, without knowledge of input formats--for…
Mutation testing can help minimize the delivery of faulty software. Therefore, it is a recommended practice for developing embedded software in safety-critical cyber-physical systems (CPS). However, state-of-the-art mutation testing…
BusyBox, an open-source software bundling over 300 essential Linux commands into a single executable, is ubiquitous in Linux-based embedded devices. Vulnerabilities in BusyBox can have far-reaching consequences, affecting a wide array of…
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…
Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of reasoning data. However, knowledge distillation (KD) over lengthy sequences with prompt (P),…
Processor design and verification require a synergistic approach that combines instruction-level functional simulations with precise hardware emulations. The trade-off between speed and accuracy in the instruction set simulation poses a…
Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors,…
SystemC-based virtual prototypes have emerged as widely adopted tools to test software ahead of hardware availability, reducing the time-to-market and improving software reliability. Recently, fuzzing has become a popular method for…
Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Quite a few practitioners have devoted themselves to developing tools for detecting…
Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I…
Static analysis tools are essential for ensuring memory safety in Rust programs, particularly as Rust gains adoption in safety-critical domains. However, existing tools such as Rudra and MirChecker suffer from high false positive rates,…
Large Language Models (LLMs) with the Mixture-of-Experts (MoE) architectures have shown promising performance on various tasks. However, due to the huge model sizes, running them in resource-constrained environments where the GPU memory is…
The majority of mainstream neural vocoders primarily focus on speech quality and generation speed, while overlooking latency, which is a critical factor in real-time applications. Excessive latency leads to noticeable delays in user…
We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as follows: given a set of seed inputs, the fuzzer performs…
MLIR (Multi-Level Intermediate Representation) compiler infrastructure provides an efficient framework for introducing a new abstraction level for programming languages and domain-specific languages. It has attracted widespread attention in…
Contemporary quantum computers have relatively high levels of noise, making it difficult to use them to perform useful calculations, even with a large number of qubits. Quantum error correction is expected to eventually enable…
Testing a program's capability to effectively handling errors is a significant challenge, given that program errors are relatively uncommon. To solve this, Software Fault Injection (SFI)-based fuzzing integrates SFI and traditional fuzzing,…