Security, Privacy and Abuse Prevention
The Internet and the World Wide Web have brought many changes that provide huge benefits, in particular by giving people easy access to information that was previously unavailable, or simply hard to find. Unfortunately, these changes have raised many new challenges in the security of computer systems and the protection of information against unauthorized access and abusive usage. At Google, our primary focus is the user, and his/her safety. We have people working on nearly every aspect of security, privacy, and anti-abuse including access control and information security, networking, operating systems, language design, cryptography, fraud detection and prevention, spam and abuse detection, denial of service, anonymity, privacy-preserving systems, disclosure controls, as well as user interfaces and other human-centered aspects of security and privacy. Our security and privacy efforts cover a broad range of systems including mobile, cloud, distributed, sensors and embedded systems, and large-scale machine learning.
Recent Publications
AI-powered patching: the future of automated vulnerability fixes
Jan Keller
Jan Nowakowski
Google Security Engineering Technical Report (2024) (to appear)
Preview abstract
As AI continues to advance at rapid speed, so has its ability to unearth hidden security vulnerabilities in all types of software. Every bug uncovered is an opportunity to patch and strengthen code—but as detection continues to improve, we need to be prepared with new automated solutions that bolster our ability to fix those bugs. That’s why our Secure AI Framework (SAIF) includes a fundamental pillar addressing the need to “automate defenses to keep pace with new and existing threats.”
This paper shares lessons from our experience leveraging AI to scale our ability to fix bugs, specifically those found by sanitizers in C/C++, Java, and Go code. By automating a pipeline to prompt Large Language Models (LLMs) to generate code fixes for human review, we have harnessed our Gemini model to successfully fix 15% of sanitizer bugs discovered during unit tests, resulting in hundreds of bugs patched. Given the large number of sanitizer bugs found each year, this seemingly modest success rate will with time save significant engineering effort. We expect this success rate to continually improve and anticipate that LLMs can be used to fix bugs in various languages across the software development lifecycle.
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50 Shades of Support: A Device-Centric Analysis of Android Security Updates
Abbas Acar
Esteban Luques
Harun Oz
Ahmet Aris
Selcuk Uluagac
Network and Distributed System Security (NDSS) Symposium (2024)
Preview abstract
Android is by far the most popular OS with over
three billion active mobile devices. As in any software, uncovering
vulnerabilities on Android devices and applying timely patches
are both critical. Android Open Source Project (AOSP) has
initiated efforts to improve the traceability of security updates
through Security Patch Levels (SPLs) assigned to devices. While
this initiative provided better traceability for the vulnerabilities,
it has not entirely resolved the issues related to the timeliness
and availability of security updates for end users. Recent studies
on Android security updates have focused on the issue of delay
during the security update roll-out, largely attributing this to
factors related to fragmentation. However, these studies fail to
capture the entire Android ecosystem as they primarily examine
flagship devices or do not paint a comprehensive picture of the
Android devices’ lifecycle due to the datasets spanning over a
short timeframe. To address this gap in the literature, we utilize
a device-centric approach to analyze the security update behavior
of Android devices. Our approach aims to understand the security
update distribution behavior of OEMs (e.g., Samsung) by using
a representative set of devices from each OEM and characterize
the complete lifecycle of an average Android device. We obtained
367K official security update records from public sources, span-
ning from 2014 to 2023. Our dataset contains 599 unique devices
from four major OEMs that are used in 97 countries and are
associated with 109 carriers. We identify significant differences
in the roll-out of security updates across different OEMs, device
models/types, and geographical regions across the world. Our
findings show that the reasons for the delay in the roll-out of
security updates are not limited to fragmentation but also involve
OEM-specific factors. Our analysis also uncovers certain key
issues that can be readily addressed as well as exemplary practices
that can be immediately adopted by OEMs in practice.
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FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting
Meenatchi Sundaram Muthu Selva Annamalai
Emiliano De Cristofaro
Network and Distributed System Security (NDSS) Symposium (2024)
Preview abstract
Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookies placed by common web browsers and recent regulations like the GDPR may accelerate the transition. To counter browser fingerprinting, previous work proposed several techniques to detect its prevalence and severity. However, these rely on 1) centralized web crawls and/or 2) computationally intensive operations to extract and process signals (e.g., information-flow and static analysis).
To address these limitations, we present FP-Fed, the first distributed system for browser fingerprinting detection. Using FP-Fed, users can collaboratively train on-device models based on their real browsing patterns, without sharing their training data with a central entity, by relying on Differentially Private Federated Learning (DP-FL). To demonstrate its feasibility and effectiveness, we evaluate FP-Fed’s performance on a set of 18.3k popular websites with different privacy levels, numbers of participants, and features extracted from the scripts. Our experiments show that FP-Fed achieves reasonably high detection performance and can perform both training and inference efficiently, on-device, by only relying on runtime signals extracted from the execution trace, without requiring any resource-intensive operation.
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Rust is a general-purpose programming language designed for performance and safety. Unrecoverable errors (e.g., Divide by Zero) in Rust programs are critical, as they signal bad program states and terminate programs abruptly. Previous work has contributed to utilizing KLEE, a dynamic symbolic test engine, to verify the program would not panic. However, it is difficult for engineers who lack domain expertise to write test code correctly. Besides, the effectiveness of KLEE in finding panics in production Rust code has not been evaluated. We created an approach, called PanicCheck, to hide the complexity of verifying Rust programs with KLEE. Using PanicCheck, engineers only need to annotate the function-to-verify with #[panic_check]. The annotation guides PanicCheck to generate test code, compile the function together with tests, and execute KLEE for verification. After applying PanicCheck to 21 open-source and 2 closed-source projects, we found 61 test inputs that triggered panics; 60 of the 61 panics have been addressed by developers so far. Our research shows promising verification results by KLEE, while revealing technical challenges in using KLEE. Our experience will shed light on future practice and research in program verification.
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Wear's my Data? Understanding the Cross-Device Runtime Permission Model in Wearables
Doguhan Yeke
Muhammad Ibrahim
Habiba Farukh
Abdullah Imran
Antonio Bianchi
Z. Berkay Celik
IEEE Symposium on Security and Privacy (2024) (to appear)
Preview abstract
Wearable devices are becoming increasingly important, helping us stay healthy and connected. There are a variety
of app-based wearable platforms that can be used to manage
these devices. The apps on wearable devices often work with a
companion app on users’ smartphones. The wearable device and
the smartphone typically use two separate permission models
that work synchronously to protect sensitive data. However, this
design creates an opaque view of the management of permission-
protected data, resulting in over-privileged data access without
the user’s explicit consent. In this paper, we performed the first
systematic analysis of the interaction between the Android and
Wear OS permission models. Our analysis is two-fold. First,
through taint analysis, we showed that cross-device flows of
permission-protected data happen in the wild, demonstrating
that 28 apps (out of the 150 we studied) on Google Play
have sensitive data flows between the wearable app and its
companion app. We found that these data flows occur without
the users’ explicit consent, introducing the risk of violating
user expectations. Second, we conducted an in-lab user study
to assess users’ understanding of permissions when subject to
cross-device communication (n = 63). We found that 66.7% of
the users are unaware of the possibility of cross-device sensitive
data flows, which impairs their understanding of permissions in
the context of wearable devices and puts their sensitive data at
risk. We also showed that users are vulnerable to a new class of
attacks that we call cross-device permission phishing attacks on
wearable devices. Lastly, we performed a preliminary study on
other watch platforms (i.e., Apple’s watchOS, Fitbit, Garmin
OS) and found that all these platforms suffer from similar
privacy issues. As countermeasures for the potential privacy
violations in cross-device apps, we suggest improvements in the
system prompts and the permission model to enable users to
make better-informed decisions, as well as on app markets to
identify malicious cross-device data flows.
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Secure by Design at Google
Google Security Engineering (2024)
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This whitepaper provides an overview of Google's approach to secure design.
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