Publication

IVD: Automatic Learning and Enforcement of Authorization Rules in Online Social Networks

IEEE Symposium on Security and Privacy (IEEE S&P)


Abstract

Authorization bugs, when present in online social networks, are usually caused by missing or incorrect authorization checks and can allow attackers to bypass the online social network’s protections. Unfortunately, there is no practical way to fully guarantee that an authorization bug will never be introduced—even with good engineering practices—as a web application and its data model become more complex. Unlike other web application vulnerabilities such as XSS and CSRF, there is no practical general solution to prevent missing or incorrect authorization checks.

In this paper we propose Invariant Detector (IVD), a defense-in-depth system that automatically learns authorization rules from normal data manipulation patterns and distills them into likely invariants. These invariants, usually learned during the testing or pre-release stages of new features, are then used to block any requests that may attempt to exploit bugs in the social network’s authorization logic. IVD acts as an additional layer of defense, working behind the scenes, complementary to privacy frameworks and testing.

We have designed and implemented IVD to handle the unique challenges posed by modern online social networks. IVD is currently running at Facebook, where it infers and evaluates daily more than 200,000 invariants from a sample of roughly 500 million client requests, and checks the resulting invariants every second against millions of writes made to a graph database containing trillions of entities. Thus far IVD has detected several high impact authorization bugs and has successfully blocked attempts to exploit them before code fixes were deployed.

Related Publications

All Publications

ICML - July 19, 2021

Making Paper Reviewing Robust to Bid Manipulation Attacks

Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger

IEEE Access Journal (IEEE Access) - August 1, 2021

Coded Machine Unlearning

Nasser Aldaghri, Hessam Mahdavifar, Ahmad Beirami

PLDI - June 16, 2021

Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption

Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T. Lee, Brandon Reagen

HPCA - May 1, 2021

Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference

Brandon Reagen, Wooseok Choi, Yeongil Ko, Vincent T. Lee, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy