Constraint-Guided Vulnerability Detection Techniques for Machine Learning Framework
The increasing integration of machine learning(ML)in various sectors for decision-making automation brings to light significant concerns regarding the vulnerabilities in ML frame-works.Such vulnerabilities pose a considerable risk,potentially undermining the integrity and reliability of ML applications in critical areas.Testing these frameworks,however,is notably challenging due to their complex implementations.The intricacy of these systems often masks vulnerabilities,making them difficult to detect with conventional methods.Historically,fuzzing ML frameworks has been met with limited success.The primary challenge in this area has been the effective extraction of input constraints and the generation of valid inputs.Traditional approa-ches often result in prolonged fuzzing periods,which are not only inefficient but also insufficient in reaching the deeper,more complex execution paths where critical vulnerabilities might lie.In response to these challenges,our paper introduces ConFL(Constraint Fuzzy Lop),a novel,con-straint-guided fuzzer designed specifically for ML frameworks.ConFL marks a significant advancement in the field of ML framework testing.Its ability to automatically extract constraints from source codes is a groundbreaking feature.This automation is particularly beneficial as it eliminates the need for prior knowledge of the framework's inner workings,thus democratizing the testing process.The constraint-guided approach of ConFL is instrumental in generating valid inputs that are more likely to pass through the initial layers of verification in ML frameworks.This capability enables ConFL to delve deeper into the operator code's pathways,thus uncove-ring vulnerabilities that would otherwise remain hidden in traditional testing methods.Moreover,ConFL innovates with a unique grouping technique designed to enhance fuzzing efficiency.This technique organizes the testing process in a more structured manner,allowing for a more thor-ough and systematic exploration of the framework's vulnerabilities.Our evaluation of ConFL's performance,primarily on the TensorFlow framework,has yielded impressive results.ConFL demonstrates a superior capability in covering more code lines and generating a greater number of valid inputs compared to state-of-the-art(SOTA)fuzzers.This increased efficiency is crucial in the practical application of fuzzing in ML frameworks,as it translates to more robust and secure ML applications.In the realm of known vulnerabilities within the TensorFlow framework,Con-FL has shown exceptional prowess.It has successfully detected a larger number of vulnerabilities than existing fuzzers.But perhaps more importantly,ConFL has identified 84 previously unknown vulnerabilities across various versions of TensorFlow.These newly discovered vulnera-bilities,which include 3 of critical severity and 13 of high severity,have been significant enough to warrant new CVE(Common Vulnerabilities and Exposures)ids.The versatility of ConFL is further demonstrated by its application to other ML frameworks such as PyTorch and Paddle.In these frameworks,ConFL has already identified 7 vulnerabilities,indicating its potential as a universal tool for ML framework testing.In conclusion,ConFL represents a significant step forward in securing ML frameworks.Its automated,constraint-guided approach not only makes the fuzzing process more efficient but also more effective in uncovering deep-seated vulnerabili-ties.As ML continues to permeate various sectors,tools like ConFL will be vital in ensuring the security and reliability of ML-driven systems.
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