Microsoft Technology Licensing, LLC

William Blum

David Alexander Molnar

Benjamin E Nick

Mohit Sewak

Rishabh Singh

Weisheng Li

Patrice Godefroid

Milenko Drinic

Jugal Parikh

Ram Shankar Siva Kumar

Ravi Kiran Reddy Poluri

Yogesh Kant Roy

Marc Greisen

Sharada Shirish Acharya

Daniel L Mace

Michael Abraham Betser

Christian Rudnick

Pak On Chan

Edir Vinicio Garcia Lazo

Marina Polishchuk

Amir Rubin

Cheick Omar Keita

Stanislav Tishkin

Senthil Kumaran Chandran

Mohit Rajpal

Sihong Liu

Jeremias Eichelbaum

Nihal Irmak Pakis

Peter A Bryan

A computer-implemented method of generating a security language query from a user input query includes receiving, at a computer system, an input security hunting user query indicating a user intention; selecting, using a trained machine learning model and based on the input security hunting query, an example user security hunting query and corresponding example security language query; generating, using the trained machine learning model, query metadata from the input security hunting query; generating a prompt, the prompt comprising: the input security hunting user query; the selected example user security hunting query and the corresponding example security language query; and the generated query metadata; inputting the prompt to a large language model; receiving a security language query from the large language model corresponding to the input security hunting query reflective of the user intention.

Generating security language queries

The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Automatic labeling of text data

A user interface (UI) may be used to introduce a message into the cloud computing network. The message may be received by a service associated with the cloud computing network. The message may trigger the service to generate data in response to receiving the message. The generated data may include temporal data that includes the date and time data specifying when the message was received by the service. The generated temporal data may be forwarded to a telemetry store associated with the cloud computing network. A user or report generating operator portal may generate a report that includes the generated temporal data. The generated report may be used to determine if the service associated with the cloud computing network is functioning properly.

Cloud computing network inspection techniques

Apparatus and methods can be implemented to perform software testing or to perform emulated hardware testing using a cloud architecture that can utilize centralized testing technology and can enable scaling up to test for multiple tenants and scaling up to arbitrary numbers of programs tested for each tenant. A user can configure an initial test virtual machine on a cloud platform for a cloud service over a physical network such as the Internet. Components of the cloud architecture can create a set of clones of the initial test virtual machine and inject tools into each clone for testing. Testing of one or more clones of the set can be conducted in an environment isolated from the physical network and isolated from a backend of the cloud service. Additional apparatus, systems, and methods are disclosed.

Cloud architecture for automated testing

Techniques for constrained mutation-based fuzzing are described. Machine accesses an input file of code for testing. Machine performs multiple runs of a fuzzing algorithm using the input file and the code. Each run includes: performing a mutation of one or more bytes of the input file and determining which parts of the code were executed when the code was run with the mutated input file. Machine stores, for each run, an indication of whether the mutation caused execution of a portion of the code which was not executed prior to the mutation, Machine generates heatmap of the input file based on the stored indications. The heatmap maps each of the bytes in the input file to a value indicating whether the mutation of the byte caused execution of the portion of the code for testing which was not executed prior to the mutation. Machine tailors fuzzing algorithm based on heatmap.

Machine learning for constrained mutation-based fuzz testing

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Bellevue, WA, United States of America

William Blum