Writing a working exploit for a vulnerability is generally challenging, time-consuming, and labor-intensive. To address this issue, automated exploit generation techniques can be adopted. In practice, existing techniques however exhibit an insufficient ability to craft exploits, particularly for the kernel vulnerabilities. On the one hand, this is because their technical approaches explore exploitability only in the context of a crashing process whereas generating an exploit for a kernel vulnerability typically needs to vary the context of a kernel panic. On the other hand, this is due to the fact that the program analysis techniques used for exploit generation are suitable only for simple programs but not the OS kernel which has higher complexity and scalability.
In this talk, we will introduce and release a new exploitation framework to fully automate the exploitation of kernel vulnerabilities. Technically speaking, our framework utilizes a kernel fuzzing technique to diversify the contexts of a kernel panic and then leverages symbolic execution to explore exploitability under different contexts. We demonstrate that this new exploitation framework facilitates exploit crafting from many aspects.
First, it augments a security analyst with the ability to automate the identification of system calls that he needs to take advantages for vulnerability exploitation. Second, it provides security analysts with the ability to achieve security mitigation bypassing. Third, it allows security analysts to automatically generate exploits with different exploitation objectives (e.g., privilege escalation or data leakage). Last but not least, it equips security analysts with an ability to generate exploits even for those kernel vulnerabilities for which the exploitability has not yet been confirmed or verified.
Along with this talk, we will also release many unpublished working exploits against several kernel vulnerabilities. It should be noted that, the vulnerabilities we experimented cover primarily Use-After-Free and heap overflow. Among all these test cases, more than 50% of them do not have working exploits publicly available. To illustrate this release, I have already disclosed one working exploit at my personal website (http://ww9210.cn/). The exploit released on my site pertains to CVE-2017-15649 for which there has not yet been an exploit publicly available with the demonstration of bypassing SMAP.
Wei Wu is currently a security researcher at Pennsylvania State University and also a PhD candidate at University of Chinese Academy of Sciences, China. His research interests are focused on kernel exploitation techniques. He plays CTFs as a member of team NeSE since 2015.
Dr. Xinyu Xing is an Assistant Professor at the Pennsylvania State University, and currently working at JD Inc. as a visiting researcher. His research interest includes exploring, designing and developing tools to automate vulnerability discovery, failure reproduction, vulnerability diagnosis (and triage), exploit and security patch generation. Recently, he is also interested in developing deep learning techniques to perform highly accurate binary and malware analysis. His past research has been featured by many mainstream medium, such as Technology Review, New Scientists and NYTimes etc. He was also the organizer of NSA memory corruption forensics competition.
Dr. Jimmy Su leads the JD security research center in Silicon Valley. He joined JD in January 2017. Before joining JD, he was the director of advanced threat research at FireEye Labs. He led the research and development of multiple world leading security products at FireEye, including network security, email security, mobile security, fraud detection, and end-point security. He led a global team including members from the United States, Pakistan, and Singapore from research to product releases on the FireEye's first machine learning based malware similarity analysis Cloud platform. This key technology advance was released on all core FireEye products including network security, email security, and mobile security. He won the Q2 2016 FireEye innovation award for his seminal work on similarity analysis. He earned his PhD degree in Computer Science at the University of California, Berkeley in 2010. After his graduation, he joined Professor Dawn Song's team as a post doc focusing on similarity analysis of x86 and Android applications. In 2011, he joined Professor Song in the mobile security startup Ensighta, leading the research and development of the automatic malware analysis platform. Ensighta was acquired by FireEye in December of 2012. He joined FireEye through the acquisition. JD security research center in Silicon Valley focuses on these seven areas: account security, APT detection, bot detection, data security, AI applications in security, Big Data applications in security, and IoT security.