Alan Cao (New York University) and Brendan Dolan-Gavitt (New York University)

On GitHub, open-source developers use the fork feature to create server-side clones and implement code changes separately before creating pull requests. However, such fork repositories can be abused to store and distribute malware, particularly malware that stealthily mines cryptocurrencies.

In this paper, we present an analysis of this emerging attack vector and a system for catching malware in GitHub fork repositories with minimal human effort called Fork Integrity Analysis, implemented through a detection infrastructure called Fork Sentry. By automatically detecting and reverse engineering interesting artifacts extracted from a given repository’s forks, we can generate alerts for suspicious artifacts, and provide a means for takedown by GitHub Trust & Safety. We demonstrate the efficacy of our techniques by scanning 68,879 forks of 35 popular cryptocurrency repositories, leading to the discovery of 26 forked repositories that were hosting malware, and report them to GitHub with seven successful takedowns so far. Our detection infrastructure allows not only for the triaging and alerting of suspicious forks, but also provides continuous monitoring for later potential malicious forks. The code and collected data from Fork Sentry will be released as an open-source project.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 40 [1] => 55 ) ) ) [post__not_in] => Array ( [0] => 8624 ) )

Clarion: Anonymous Communication from Multiparty Shuffling Protocols

Saba Eskandarian (University of North Carolina at Chapel Hill), Dan Boneh (Stanford University)

Read More

[WITHDRAWN] First, Do No Harm: Studying the manipulation of...

Shubham Agarwal (Saarland University), Ben Stock (CISPA Helmholtz Center for Information Security)

Read More

MIRROR: Model Inversion for Deep Learning Network with High...

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University), Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)