arXiv subjects

Chung Hwan Kim

Publications and source records attributed to Chung Hwan Kim.

3 recordsLinked to original sources

arXiv2026

Stop Starving or Stuffing Me: Boosting Firmware Fuzzing Efficiency with On-demand Input Delivery

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 can be received asynchronously and independently of the firmware's execution, with uncertain timing and quantity. Without full awareness of firmware's exceptions, existing solutions often imprudently deliver fuzzer-generated input to the firmware in an ad-hoc way. This either overwhelms the processing function of the firmware (stuffing) or fails to deliver enough input data to trigger input processing functions (starving). In both cases, fuzzing capability is weakened. In this paper, we comprehensively investigate the input delivery issue. To determine the optimal timing and quantity for delivering test cases, we leverage the fact that firmware has to check input availability before using data. So we employ static and dynamic analysis to map each input processing route into three stages: input retrieval, availability check, and processing. This recovered semantic information allows the fuzzer to accurately deliver input at the availability check points within the expected length range. For multiple input routes problem, we also optimize the scheduling algorithm to reach more diverse routes. Our prototype, named FIDO, can serve as an add-on to existing firmware fuzzers to enhance their test-case delivery effectiveness. Compared to ad-hoc input delivery methods used in Fuzzware and MULTIFUZZ, FIDO increases their median code coverage by up to 115% and 54%, respectively. Compared to SEmu, which requires humans to manually specify input delivery points, FIDO still improves its coverage by up to 19%. As a result, FIDO discovers known bugs significantly faster and also identifies five previously unknown bugs.

cs.CR↗

arXiv2022

DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing

Autonomous driving has become real; semi-autonomous driving vehicles in an affordable price range are already on the streets, and major automotive vendors are actively developing full self-driving systems to deploy them in this decade. Before rolling the products out to the end-users, it is critical to test and ensure the safety of the autonomous driving systems, consisting of multiple layers intertwined in a complicated way. However, while safety-critical bugs may exist in any layer and even across layers, relatively little attention has been given to testing the entire driving system across all the layers. Prior work mainly focuses on white-box testing of individual layers and preventing attacks on each layer. In this paper, we aim at holistic testing of autonomous driving systems that have a whole stack of layers integrated in their entirety. Instead of looking into the individual layers, we focus on the vehicle states that the system continuously changes in the driving environment. This allows us to design DriveFuzz, a new systematic fuzzing framework that can uncover potential vulnerabilities regardless of their locations. DriveFuzz automatically generates and mutates driving scenarios based on diverse factors leveraging a high-fidelity driving simulator. We build novel driving test oracles based on the real-world traffic rules to detect safety-critical misbehaviors, and guide the fuzzer towards such misbehaviors through driving quality metrics referring to the physical states of the vehicle. DriveFuzz has discovered 30 new bugs in various layers of two autonomous driving systems (Autoware and CARLA Behavior Agent) and three additional bugs in the CARLA simulator. We further analyze the impact of these bugs and how an adversary may exploit them as security vulnerabilities to cause critical accidents in the real world.

cs.RO↗

arXiv2018

SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection

Recently, advanced cyber attacks, which consist of a sequence of steps that involve many vulnerabilities and hosts, compromise the security of many well-protected businesses. This has led to the solutions that ubiquitously monitor system activities in each host (big data) as a series of events, and search for anomalies (abnormal behaviors) for triaging risky events. Since fighting against these attacks is a time-critical mission to prevent further damage, these solutions face challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale provenance data. To address these challenges, we propose a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomalies. To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies. We deployed our system in NEC Labs America comprising 150 hosts and evaluated it using 1.1TB of real system monitoring data (containing 3.3 billion events). Our evaluations on a broad set of attack behaviors and micro-benchmarks show that our system has a low detection latency (<2s) and a high system throughput (110,000 events/s; supporting ~4000 hosts), and is more efficient in memory utilization than the existing stream-based complex event processing systems.

cs.CR↗