Shape Security, Inc.

Google Inc.

Timothy Dylan Peacock

Justin David Call

Xinran Wang

Yao Zhao

Siying Yang

Sumit Agarwal

Ariya Hidayat

John Edward Davis

Seth Joseph Vargo

Scott Tyler Ellis

Phillip Marvin Tischler

Colin Man

Merrielle Therese Spain

Aspects of the disclosure are directed to a system for classifying software as malicious or benign based on predicting the effect the software has on the platform before the software is actually deployed. A system as described herein can operate in close to real-time to receive, isolate, and classify software as benign or malicious. Aspects of the disclosure provide for accurate classification of malicious programs or scripts even if ostensibly the program appears benign, and vice versa, based on the effect predicted by a machine learning model trained as described herein. The system can also be implemented to isolate and verify incoming scripts or software to the platform, to provide a predicted classification while not substantially impacting processing pipelines involving platform resources or the user experience with the platform in general.

Script classification on computing platform

Aspects of the disclosure provide for an end-to-end system for detecting and responding to known secrets leaked on a computing platform. By securely retrieving secret data from a repository of known secrets, the platform can detect whether potential secrets are leaked in digital documents generated by applications or services of the platform, and compare the potential secrets with known secrets in the repository. The system deterministically and accurately identifies whether secrets were leaked in a digital document, and can also identify both the source of the leakage and the affected users, devices, or groups of users and devices on the platform.

Secret detection on computing platform

Application programming interfaces (APIs) can be unintentionally exposed and allow for potentially undesirable use of corporate resources. An API call filtering system configured to monitor API call requests received via an endpoint and API call responses received via a supporting service of an API or web service. The API call filtering system enables enterprises to improve their security posture by identifying, studying, reporting, and securing their APIs within their enterprise network.

Modifying authentication for an application programming interface

A computer-implemented method includes providing, for use by a third-party, injectable computer code that is capable of being served with other code provided by the third-party to client computing devices; receiving data from client computing devices that have been served the code by the third-party, the data including data that characterizes (a) the client computing devices and (b) user interaction with the client computing devices; classifying the client computing devices as controlled by actual users or instead by automated software based on analysis of the received data from the client computing devices; and providing to the third party one or more reports that characterize an overall level of automated software activity among client computing devices that have been served code by the third party.

Detecting attacks against a server computer based on characterizing user interactions with the client computing device

Application programming interface wall

Detection of client-side malware activity

A computer-implemented method includes receiving, at a computer security server system located between the Internet and a client computing device that makes requests over the Internet, a request for content directed to a particular content server system; forwarding the received request, with the computer security server system, to the particular content server system; receiving code from the particular server system in response to the request; applying a security countermeasure to the received code to created transformed code; providing the transformed code to the client computing device; receiving a communication from the client computing device; and determining that software on the client computing device has attempted to interact with the received code rather than the transformed code.

Distributed polymorphic transformation of served content

Growing community of inventors

San Francisco, CA, United States of America

Timothy Dylan Peacock