Our research seeks to empower individuals and organizations to control how their data is used. We use techniques from cryptography, programming languages, machine learning, operating systems, and other areas to both understand and improve the security of computing as practiced today, and as envisioned in the future.

SRG
lunch
Security Research Group Lunch (12 December 2017)
Haina Li, Felix Park, Mainuddin Jonas, Anant Kharkar, Faysal Hossain Shezan, Suya,
David Evans, Yuan Tian, Riley Spahn, Weilin Xu, Guy "Jack" Verrier

Everyone is welcome at our research group meetings. To get announcements, join our Slack Group (any @virginia.edu email address can join themsleves, or email me to request an invitation).

Projects

Secure Multi-Party Computation
Obliv-C · MightBeEvil
Practical Secure Computation
Web and Mobile Security
ScriptInspector · SSOScan
Adversarial Machine Learning
EvadeML
Past Projects
Side-Channel Analysis · Perracotta · Splint
N-Variant Systems · Physicrypt · Social Networking APIs

News

Mutually Assured Destruction and the Impending AI Apocalypse

13 August 2018

I gave a keynote talk at USENIX Workshop of Offensive Technologies, Baltimore, Maryland, 13 August 2018.

The title and abstract are what I provided for the WOOT program, but unfortunately (or maybe fortunately for humanity!) I wasn’t able to actually figure out a talk to match the title and abstract I provided.

The history of security includes a long series of arms races, where a new technology emerges and is subsequently developed and exploited by both defenders and attackers. Over the past few years, “Artificial Intelligence” has re-emerged as a potentially transformative technology, and deep learning in particular has produced a barrage of amazing results. We are in the very early stages of understanding the potential of this technology in security, but more worryingly, seeing how it may be exploited by malicious individuals and powerful organizations. In this talk, I’ll look at what lessons might be learned from previous security arms races, consider how asymmetries in AI may be exploited by attackers and defenders, touch on some recent work in adversarial machine learning, and hopefully help progress-loving Luddites figure out how to survive in a world overrun by AI doppelgängers, GAN gangs, and gibbon-impersonating pandas.


Dependable and Secure Machine Learning

7 July 2018

I co-organized, with Homa Alemzadeh and Karthik Pattabiraman, a workshop on trustworthy machine learning attached to DSN 2018, in Luxembourg: DSML: Dependable and Secure Machine Learning.


Cybersecurity Summer Camp

7 July 2018

I helped organize a summer camp for high school teachers focused on cybersecurity, led by Ahmed Ibrahim. Some of the materials from the camp on cryptography, including the Jefferson Wheel and visual cryptography are here: Cipher School for Muggles.




Cybersecurity Goes to Summer Camp. UVA Today. 22 July 2018. [archive.org]

Earlier this week, 25 high school teachers – including 21 from Virginia – filled a glass-walled room in Rice Hall, sitting in high adjustable chairs at wheeled work tables, their laptops open, following a lecture with graphics about the dangers that lurk in cyberspace and trying to figure out how to pass the information on to a generation that seems to share the most intimate details of life online. “I think understanding privacy is important to that generation that uses Facebook and Snapchat,” said David Evans, a computer science professor who helped organize the camp. “We hope to give teachers some ideas and tools to get their students excited about learning about cryptography, privacy and cybersecurity, and how these things can impact them.”

(Also excerpted in ACM TechNews, 29 June 2018.)




UVa bootcamp aims to increase IT training for teachers. The Daily Progress. 22 June 2018. [archive.org]


DLS Keynote: Is “adversarial examples” an Adversarial Example?

29 May 2018

I gave a keynote talk at the 1st Deep Learning and Security Workshop (co-located with the 39th IEEE Symposium on Security and Privacy). San Francisco, California. 24 May 2018




Abstract

Over the past few years, there has been an explosion of research in security of machine learning and on adversarial examples in particular. Although this is in many ways a new and immature research area, the general problem of adversarial examples has been a core problem in information security for thousands of years. In this talk, I’ll look at some of the long-forgotten lessons from that quest and attempt to understand what, if anything, has changed now we are in the era of deep learning classifiers. I will survey the prevailing definitions for “adversarial examples”, argue that those definitions are unlikely to be the right ones, and raise questions about whether those definitions are leading us astray.


SRG at IEEE S&P 2018

29 May 2018

Group Dinner


Including our newest faculty member, Yongwhi Kwon, joining UVA in Fall 2018!

Yuan Tian, Fnu Suya, Mainuddin Jonas, Yongwhi Kwon, David Evans, Weihang Wang, Aihua Chen, Weilin Xu

Poster Session


Fnu Suya (with Yuan Tian and David Evans), Adversaries Don’t Care About Averages: Batch Attacks on Black-Box Classifiers [PDF]

Mainuddin Jonas (with David Evans), Enhancing Adversarial Example Defenses Using Internal Layers [PDF]

Huawei STW: Lessons from the Last 3000 Years of Adversarial Examples

23 May 2018

I spoke on Lessons from the Last 3000 Years of Adversarial Examples at Huawei’s Strategy and Technology Workshop in Shenzhen, China, 15 May 2018.

We also got to tour Huawei’s new research and development campus, under construction about 40 minutes from Shenzhen. It is pretty close to Disneyland, with its own railroad and villages themed after different European cities (Paris, Bologna, etc.).



Huawei’s New Research and Development Campus [More Pictures]

Unfortunately, pictures were not allowed on our tour of the production line. Not so surprising that nearly all of the work was done by machines, but was surprising to me how much of the human work left is completely robotic. The human workers (called “operators”) are mostly scanning QR codes on parts, and following the directions that light up with they do, or scanning bins and following directions on a screen to collect parts from bins and scanning them when they are put into the bin. This is the kind of system that leads to remarkably high production quality. The parts are mostly delivered on tapes that are fed into the machines, and many machines along the line are primarily for testing. There is a “bottleneck” marker that is placed on any points that are holding up the production line.

The public (at least to the factory) “grapey board” keeps track of the happiness of the workers — each operator puts up a smiley (or frowny) face on the board to show their mood for the day, monitored carefully by the managers. There is a batch of grapes to show performance for the month. If an operator does something good, a grape is colored green; if they do something bad, a grape is colored black. There was quite a bit of discussion among the people on the tour (mostly US and European-based professors) if such a management approach would be a good idea for our research groups… (or for department chairs for their faculty!)



In front of Huawei’s “White House”, with Battista Biggio [More Pictures]


Feature Squeezing at NDSS

25 February 2018

Weilin Xu presented Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks at the Network and Distributed System Security Symposium 2018. San Diego, CA. 21 February 2018.



Paper: Weilin Xu, David Evans, Yanjun Qi. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. NDSS 2018. [PDF]

Project Site


Why hasn’t Cross-Site Scripting been solved?

31 December 2017

By Haina Li

Introduction

In 2017, Bugcrowd reported that cross-site scripting (XSS) remains as the number one vulnerability found on the web, accounting for 25% of the bugs found and submitted to the bug bounty program. Additionally, XSS has remained in the top 3 on the list of the web’s top vulnerabilities for the recent years. Over the 17 years since XSS was first recognized by Microsoft in 2000, XSS has been the focus of intense academic research and development of penetration testing tools, yet we are still finding vulnerabilities even in top websites such as Facebook and Google. In this blog post, we explore some of the reasons why XSS is still a major problem today.

XSS has evolved

XSS evolved while modern applications became more complex than the static pages that they once were. While reflected and stored XSS have not disappeared because both server and client-side logic have become more elaborate, the pattern of replacing server-side logic with client-side JavaScript gave rise to DOM-Based vulnerabilities. Additionally, server-side XSS prevention tools that examined deviations between the request and response (XSSDS) do not work for DOM-Based vulnerabilities because the entire flow of malicious data from the source to the sink is contained within the browser and do not go through the server.

New methods that do prevent DOM-Based XSS attacks include XSS Filters and CSP. These myriad of sophisticated tools aimed to achieve the seemingly simple purpose of escaping user-provided content. As it stands currently, these tools are not able to catch all XSS vulnerabilities, and escaping everything all the time would break an web application altogether. For example, a recent work by Lekis et al. [PDF]
describes a new attack that was missed by every existing XSS prevention technique. In the new attack, the injected payload is benign-looking HTML but can be transformed by script gadgets to behave maliciously.

The effectiveness of web penetration tools are limited

In a study of automated black-box web application vulnerability testing by Bau et al. [PDF], researchers tested commercial scanners such as McAfee and IBM and found that the average scanner XSS vulnerability detection rates were 62.5, 15. and 11.25, respectively, for reflected, stored, and advanced XSS that used non-standard tags and keywords. The study found that the scanners were effective in finding straightforward, textbook XSS vulnerabilities, but lack sufficient modeling of more complex XSS with respect to the specific web application. Web application scanners are designed using a reactive approach, converting new vulnerabilities into test vectors only after they’ve become a problem. When it comes to stored XSS, XSS scanners also struggle to link an event to a subsequent, later observation. These scanners are also often difficult to configure and often take too long if they were set to fuzz every possible location in a large and complicated web application.

Conclusion

As with most web vulnerabilities, XSS is not going away anytime soon because of the constant evolving technologies of the web and the challenges in developing penetration tools with high true-positive rates. However, we may be able to eliminate most of the client-side security issues by replacing JavaScript with a new language that exhibits better control-flow integrity, such as WebAssembly.


Muzzammil Zaveri on Forbes 30 under 30

6 December 2017

Muzzammil Zaveri (BACS 2011) has been recognized by Forbes Magazine as one of the top 30 venture capitalists under 30. As an undergraduate researcher, Muzzammil worked on Guardrails (secure web application framework).

Forbes Recognition

UVa Today Article: Meet the 5 Alumni on Forbes’ new ‘30 under 30’ Lists, 15 November 2017.

Cavalier Daily Article: Forbes 30 under 30 recognizes five U.Va alumni, 4 December 2017.

Zaveri stressed the importance of pursuing passion and making positive use of free time while studying as an undergraduate.

“There’s nothing like being in a setting where you can make mistakes and explore interests,” he said. “Doing something that you’re strictly passionate about may not be the most productive — you can explore interests and area that you might be passionate about and that can be a great springboard into your own career, or whatever you decide to pursue in life after school.”

Zaveri believes he was very lucky with the connections he made at the University, especially with meeting his co-founder, Ethan Fast. He credits Evans, his advisor with empowering him with knowledge and encouraging him to learn more about tech startups.

“[Evans] really encouraged and spent time diving into startups and exploring some of my interests in building side projects,” he said. “And through that I met my co-founder [Ethan Fast] and ultimately, we ended up starting Proxino together.”


Letter to DHS

18 November 2017

I was one of 54 signatories on a letter organized by Alvaro Bedoya (from Georgetown University Law Center) from technology experts to DHS (Acting) Secretary Elaine Duke in opposition to the proposed plans to use algorithms to identify undesirable individuals as part of the Extreme Vetting Initiative: [PDF]. The Brennan Center’s Web page provides a lot of resources supporting the letter.

Some media coverage: