Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human Assistance
Yan Shoshitaishvili, Michael Weissbacher, Lukas Dresel, Christopher Salls, Ruoyu Wang, Christopher Kruegel, Giovanni Vigna
摘要
Software permeates every aspect of our world, from our homes to the infrastructure that provides mission-critical services. As the size and complexity of software systems increase, the number and sophistication of software security flaws increase as well. The analysis of these flaws began as a manual approach, but it soon became apparent that a manual approach alone cannot scale, and that tools were necessary to assist human experts in this task, resulting in a number of techniques and approaches that automated certain aspects of the vulnerability analysis process. Recently, DARPA carried out the Cyber Grand Challenge, a competition among autonomous vulnerability analysis systems designed to push the tool-assisted human-centered paradigm into the territory of complete automation, with the hope that, by removing the human factor, the analysis would be able to scale to new heights. However, when the autonomous systems were pitted against human experts it became clear that certain tasks, albeit simple, could not be carried out by an autonomous system, as they require an understanding of the logic of the application under analysis. Based on this observation, we propose a shift in the vulnerability analysis paradigm, from tool-assisted human-centered to human-assisted tool-centered. In this paradigm, the automated system orchestrates the vulnerability analysis process, and leverages humans (with different levels of expertise) to perform well-defined sub-tasks, whose results are integrated in the analysis. As a result, it is possible to scale the analysis to a larger number of programs, and, at the same time, optimize the use of expensive human resources. In this paper, we detail our design for a human-assisted automated vulnerability analysis system, describe its implementation atop an open-sourced autonomous vulnerability analysis system that participated in the Cyber Grand Challenge, and evaluate and discuss the significant improvements that non-expert human assistance can offer to automated analysis approaches.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper15
- Hackers vs. Testers: A Comparison of Software Vulnerability Discovery ProcessesDaniel Votipka, Rock Stevens, Elissa M. Redmiles, Jeremy Hu 等S&P 2018 · 被引用 151 次
- Ijon: Exploring Deep State Spaces via FuzzingCornelius Aschermann, Sergej Schumilo, Ali Abbasi, Thorsten HolzS&P 2020 · 被引用 146 次
- Zeror: Speed Up Fuzzing with Coverage-sensitive Tracing and SchedulingChijin Zhou, Mingzhe Wang, Jie Liang, Zhe Liu 等ASE 2020 · 被引用 35 次
- "Len or index or count, anything but v1": Predicting Variable Names in Decompilation Output with Transfer LearningKuntal Kumar Pal, Ati Priya Bajaj, Pratyay Banerjee, Audrey Dutcher 等S&P 2024 · 被引用 30 次
- DDGF: Dynamic Directed Greybox Fuzzing with Path ProfilingHaoran Fang, Kaikai Zhang, Donghui Yu, Yuanyuan ZhangISSTA 2024 · 被引用 10 次
它引用的顶会 Paper3
- SOK: (State of) The Art of War: Offensive Techniques in Binary AnalysisYan Shoshitaishvili, Ruoyu Wang, Christopher Salls, Nick Stephens 等S&P 2016 · 被引用 1,085 次
- Driller: Augmenting Fuzzing Through Selective Symbolic ExecutionNick Stephens, John Grosen, Christopher Salls, Andrew Dutcher 等NDSS 2016 · 被引用 1,021 次
- Ramblr: Making Reassembly Great AgainRuoyu Wang, Yan Shoshitaishvili, Antonio Bianchi, Aravind Machiry 等NDSS 2017 · 被引用 155 次
相关 Paper
- SoK: DARPA's AI Cyber Challenge (AIxCC): Competition Design, Architectures, and Lessons LearnedCen Zhang, Younggi Park, Fabian Fleischer, Yu-Fu Fu 等USENIX Security 2026 · 被引用 12 次
- Precise (Un)Affected Version Analysis for Web VulnerabilitiesYoukun Shi, Yuan Zhang, Tianhan Luo, Xiangyu Mao 等ASE 2022 · 被引用 7 次
- Automated Synthesis of Effect Graph Policies for Microservice-Aware Stateful System Call SpecializationWilliam Blair, Frederico Araujo, Teryl Taylor, Jiyong JangS&P 2024 · 被引用 6 次
- Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM AgentsPengfei He, Ash Fox, Lesly Miculicich, Stefan Friedli 等ICML 2026 · 被引用 10 次
- Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix ItDaniel Votipka, Kelsey R. Fulton, James Parker, Matthew Hou 等USENIX Security 2020
