GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks
GPU memory errors are a critical threat to deep learning (DL) frameworks, leading to crashes or even security issues. We introduce GPU-Fuzz, a fuzzer locating these issues efficiently by modeling operator parameters as formal constraints. GPU-Fuzz utilizes a constraint solver to generate test cases that systematically probe error-prone boundary conditions in GPU kernels. Applied to PyTorch, TensorFlow, and PaddlePaddle, we uncovered 13 unknown bugs, demonstrating the effectiveness of GPU-Fuzz in finding memory errors.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
FuzzCoder: Byte-level Fuzzing Test via Large Language Model
Fuzzing is an important dynamic program analysis technique designed for finding vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input to cause crashes, buffer over…
Language ModelingLanguage ModellingLarge Language ModelvalidUnsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Memory-safety bugs introduce critical software-security issues. Rust provides memory-safe mechanisms to avoid memory-safety bugs in programming, while still allowing unsafe escape hatches via unsafe code. However, the un…
Do we still need fuzzy classifiers for Small Data in the Era of Big Data?
The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory req…
Small Data Image ClassificationTensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-s…
software testingToolFuzz -- Automated Agent Tool Testing
Large Language Model (LLM) Agents leverage the advanced reasoning capabilities of LLMs in real-world applications. To interface with an environment, these agents often rely on tools, such as web search or database APIs. …
Large Language ModelPrompt Engineeringsoftware testing