Giovanni Vigna

University of California, Santa Barbara

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Christopher Kruegel

University of California, Santa Barbara

220

Richard A. Kemmerer

University of California, Santa Barbara

Yan Shoshitaishvili

Arizona State University

Aravind Machiry

Purdue University West Lafayette

Yanick Fratantonio

Cisco Systems (United States)

William Robertson

University of Adelaide

Marco Cova

VMware (United States)

Gianluca Stringhini

Boston University

Hojjat Aghakhani

University of California, Santa Barbara

Antonio Bianchi

Purdue University West Lafayette

Cooperative Institutions

University of California, Santa Barbara

120

Institute of Electrical and Electronics Engineers

Politecnico di Milano

University of California System

IEEE Computer Society

Regional Municipality of Niagara

University of California, Berkeley

Georgia Institute of Technology

University of Michigan–Ann Arbor

Purdue University West Lafayette

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Sleak

Christophe Hauser Jayakrishna Menon Yan Shoshitaishvili Ruoyu Wang Giovanni Vigna

We present a novel approach to automatically recover information about the address space layout of remote processes in the presence of Address Space Layout Randomization (ASLR). Our system, dubbed Sleak, performs static analysis and symbolic execution of binary executable programs, and identifies program paths and input parameters leading to partial (i.e., only a few bits) or complete (i.e., the whole address) information disclosure vulnerabilities, revealing addresses of known objects of the target service or application. Sleak takes, as input, the binary executable program, and generates a symbolic expression for each program output that leaks information about the addresses of objects, such as stack variables, heap structures, or function pointers. By comparing these expressions with the concrete output of a remote process executing the same binary program image, our system is able to recover from a few bits to whole addresses of objects of the target application or service. Discovering the address of a single object in the target application is often enough to guess the layout of entire sections of the address space, which can be leveraged by attackers to bypass ASLR.

Executable

Address space

Heap (data structure)

10.1145/3359789.3359820

Cite

Citations (2)

Peer to Peer Hate: Hate Speech Instigators and Their Targets

arXiv (Cornell University) (2018)

Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna Elizabeth Belding

While social media has become an empowering agent to individual voices and freedom of expression, it also facilitates anti-social behaviors including online harassment, cyberbullying, and hate speech. In this paper, we present the first comparative study of hate speech instigators and target users on Twitter. Through a multi-step classification process, we curate a comprehensive hate speech dataset capturing various types of hate. We study the distinctive characteristics of hate instigators and targets in terms of their profile self-presentation, activities, and online visibility. We find that hate instigators target more popular and high profile Twitter users, and that participating in hate speech can result in greater online visibility. We conduct a personality analysis of hate instigators and targets and show that both groups have eccentric personality facets that differ from the general Twitter population. Our results advance the state of the art of understanding online hate speech engagement.

Visibility

Harassment

10.48550/arxiv.1804.04649

Cite

Citations (32)

Exploring Abstraction Functions in Fuzzing

Christopher Salls Aravind Machiry Adam Doupé Yan Shoshitaishvili Christopher Kruegel

Fuzz testing has emerged as the preeminent automated security analysis technique in the real world. To keep up with the shifting security landscape, researchers have innovated the fuzzing process to identify more and more complex vulnerabilities. One innovation is an approach inspired by genetic programming: the fuzzer generates test-cases, evaluates the quality of the test-case, and uses this evaluation to select test-cases for further iterations of the process. While this innovation has impressive results: without a formal, scientific model on which to base these improvements, the field of fuzzing has been explored in an ad hoc way. As a result, it is difficult to understand the relative merit of different techniques. In this paper, we formalize the input evaluation and selection components of fuzzing, borrowing concepts from the field of static analysis, and providing a base for future expansion of and research into fuzzing techniques. In building this formalism, we observed that the impact of different abstraction functions in modern fuzzing techniques is under-explored in prior research. Without a formal base on which to reason about their contributions, researchers of fuzzing techniques have missed the potential for improvements to this critical component of fuzzing approaches. We explore the implications of our formalization-derived observation on the effectiveness of evolutionary fuzzing techniques in the second half of the paper, showing that the application of different abstraction functions, and the use of multiple abstraction functions in tandem, improves state-of-the-art fuzzing techniques.

Fuzz testing

Abstraction

10.1109/cns48642.2020.9162273

Cite

Citations (9)

Gossip

Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security (2017)

Cheng Huang Shuang Hao Luca Invernizzi Jiayong Liu Yong Fang

Domain names play a critical role in cybercrime, because they identify hosts that serve malicious content (such as malware, Trojan binaries, or malicious scripts), operate as command-and-control servers, or carry out some other role in the malicious network infrastructure. To defend against Internet attacks and scams, operators widely use blacklisting to detect and block malicious domain names and IP addresses. Existing blacklists are typically generated by crawling suspicious domains, manually or automatically analyzing malware, and collecting information from honeypots and intrusion detection systems. Unfortunately, such blacklists are difficult to maintain and are often slow to respond to new attacks. Security experts set up and join mailing lists to discuss and share intelligence information, which provides a better chance to identify emerging malicious activities. In this paper, we design Gossip, a novel approach to automatically detect malicious domains based on the analysis of discussions in technical mailing lists (particularly on security-related topics) by using natural language processing and machine learning techniques. We identify a set of effective features extracted from email threads, users participating in the discussions, and content keywords, to infer malicious domains from mailing lists, without the need to actually crawl the suspect websites. Our result shows that Gossip achieves high detection accuracy. Moreover, the detection from our system is often days or weeks earlier than existing public blacklists.

Blacklisting

Blacklist

Botnet

Phishing

Crawling

Cryptovirology

10.1145/3052973.3053017

Cite

Citations (17)

TEEzz: Fuzzing Trusted Applications on COTS Android Devices

2022 IEEE Symposium on Security and Privacy (SP) (2023)

Marcel Busch Aravind Machiry Chad Spensky Giovanni Vigna Christopher Kruegel

Security and privacy-sensitive smartphone applications use trusted execution environments (TEEs) to protect sensitive operations from malicious code. By design, TEEs have privileged access to the entire system but expose little to no insight into their inner workings. Moreover, real-world TEEs enforce strict format and protocol interactions when communicating with trusted applications (TAs), which prohibits effective automated testing.TEEzz is the first TEE-aware fuzzing framework capable of effectively fuzzing TAs in situ on production smartphones, i.e., the TA runs in the encrypted and protected TEE and the fuzzer may only observe interactions with the TA but has no control over the TA's code or data. Unlike traditional fuzzing techniques, which monitor the execution of a program being fuzzed and view its memory after a crash, TEEzz only requires a limited view of the target. TEEzz overcomes key limitations of TEE fuzzing (e.g., lack of visibility into the executed TAs, proprietary exchange formats, and value dependencies of interactions) by automatically attempting to infer the field types and message dependencies of the TA API through its interactions, designing state- and type-aware fuzzing mutators, and creating an in situ, on-device fuzzer.Due to the limited availability of systematic fuzzing research for TAs on commercial-off-the-shelf (COTS) Android devices, we extensively examine existing solutions, explore their limitations, and demonstrate how TEEzz improves the state-of-the-art. First, we show that general-purpose kernel driver fuzzers are ineffective for fuzzing TAs. Then, we establish a baseline for fuzzing TAs using a ground-truth experiment. We show that TEEzz outperforms other blackbox fuzzers, can improve greybox approaches (if TAs source code is available), and even outperforms greybox approaches for stateful targets. We found 13 previously unknown bugs in the latest versions of OPTEE TAs in total, out of which TEEzz is the only fuzzer to trigger three. We also ran TEEzz on popular phones and found 40 unique bugs for which one CVE was assigned so far.

Fuzz testing

10.1109/sp46215.2023.10179302

Cite

Citations (8)

Portrait of a Privacy Invasion

Proceedings on Privacy Enhancing Technologies (2015)

Yan Shoshitaishvili Christopher Kruegel Giovanni Vigna

Abstract The popularity of online social networks has changed the way in which we share personal thoughts, political views, and pictures. Pictures have a particularly important role in the privacy of users, as they can convey substantial information (e.g., a person was attending an event, or has met with another person). Moreover, because of the nature of social networks, it has become increasingly difficult to control who has access to which content. Therefore, when a substantial amount of pictures are accessible to one party, there is a very serious potential for violations of the privacy of users. In this paper, we demonstrate a novel technique that, given a large corpus of pictures shared on a social network, automatically determines who is dating whom, with reasonable precision. More specifically, our approach combines facial recognition, spatial analysis, and machine learning techniques to determine pairs that are dating. To the best of our knowledge, this is the first privacy attack of this kind performed on social networks. We implemented our approach in a tool, called Creepic, and evaluated it on two real-world datasets. The results show that it is possible to automatically extract non-obvious, and nondisclosed, relationships between people represented in a group of pictures, even when the people involved are not directly part of a connected social clique.

Popularity

Clique

Social network (sociolinguistics)

10.1515/popets-2015-0004

Cite

Citations (22)

Extracting probable command and control signatures for detecting botnets

Ali Zand Giovanni Vigna Xifeng Yan Christopher Kruegel

Botnets, which are networks of compromised machines under the control of a single malicious entity, are a serious threat to online security. The fact that botnets, by definition, receive their commands from a single entity can be leveraged to fight them. To this end, one requires techniques that can detect command and control (C&C) traffic, as well as the servers that host C&C services. Given the knowledge of a C&C server's IP address, one can use this information to detect all hosts that attempt to contact such a server, and subsequently disinfect, disable, or block the infected machines. This information can also be used by law enforcement to take down the C&C server.

Botnet

Command and Control

Blocking (statistics)

File server

Ransomware

10.1145/2554850.2554896

Cite

Citations (29)

Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability

Hojjat Aghakhani Dongyu Meng Yu-Xiang Wang Christopher Kruegel Giovanni Vigna

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poison samples are injected into the training dataset. While these poison samples look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label poisoning attack against transfer learning, which creates poison images with their center close to the target image in the feature space. Our attack, Bullseye Polytope, improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end transfer learning, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) when crafting the poison samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without using extra poison samples.

Transferability

Transfer of learning

10.1109/eurosp51992.2021.00021

Cite

Citations (41)

In rDNS We Trust: Revisiting a Common Data-Source’s Reliability

Lecture notes in computer science (2018)

Tobias Fiebig Kevin Borgolte Shuang Hao Christopher Kruegel Giovanni Vigna

IPv4

10.1007/978-3-319-76481-8_10

Cite

Citations (19)

Mobile agents: ten reasons for failure

Giovanni Vigna

Mobile agents have often been advocated as the solution to the problem of designing and implementing distributed applications in a dynamic environment. Mobile agents provide a very appealing, intuitive, and apparently simple abstraction. Unfortunately there are many difficult problems that have to be addressed in order to make mobile agent-based applications work reliably. This paper discusses some of the problems associated with mobile agents and argues that other forms of code mobility can provide similar advantages while raising much fewer issues.

Mobile agent

Abstraction

Code (set theory)

10.1109/mdm.2004.1263077

Cite

Citations (70)