Abstract: A method includes selecting a fuzzer for execution by each of multiple fuzzing clients during a first time interval of a fuzzing test of computer software code. The method also includes selecting a feedback type for statistics to be reported by the fuzzing clients at an end of the first time interval of the fuzzing test. The method also includes providing an identification of the fuzzer and the feedback type to each of the fuzzing clients. The method also includes obtaining the statistics at the end of the first time interval of the fuzzing test. The method also includes determining one or more rewards based on the statistics. The method also includes adjusting multiple weights in multiple stochastic policies based on the one or more rewards, wherein the weights are used to determine the fuzzer and the feedback type in a subsequent interval of the fuzzing test.

Abstract: A virtual machine that includes a plurality of processes executes on a computer processor. A record-replay file, trace annotations, and an application program interface request are received into the computer processor. The trace annotations and application program interface request are translated into record-replay commands. The record-replay commands capture data from the record-replay file, and the captured data can be accessed via a programmatic interface.

Abstract: A method includes obtaining at least one program slice embedding vector and at least one register vector that are generated based on results from a static analysis tool, the at least one register vector corresponding to the at least one program slice embedding vector. The method also includes using a machine learning model to generate, from the at least one program slice embedding vector and the at least one register vector, at least one probability rating associated with a vulnerability. The method also includes reporting the at least one probability rating for use by the static analysis tool.

Abstract: A virtual machine that includes a plurality of processes executes on a computer processor. A record-replay file, trace annotations, and an application program interface request are received into the computer processor. The trace annotations and application program interface request are translated into record-replay commands. The record-replay commands capture data from the record-replay file, and the captured data can be accessed via a programmatic interface.

Abstract: A method includes obtaining at least one program slice embedding vector and at least one register vector that are generated based on results from a static analysis tool, the at least one register vector corresponding to the at least one program slice embedding vector. The method also includes using a machine learning model to generate, from the at least one program slice embedding vector and the at least one register vector, at least one probability rating associated with a vulnerability. The method also includes reporting the at least one probability rating for use by the static analysis tool.

Abstract: A method includes selecting a fuzzer for execution by each of multiple fuzzing clients during a first time interval of a fuzzing test of computer software code. The method also includes selecting a feedback type for statistics to be reported by the fuzzing clients at an end of the first time interval of the fuzzing test. The method also includes providing an identification of the fuzzer and the feedback type to each of the fuzzing clients. The method also includes obtaining the statistics at the end of the first time interval of the fuzzing test. The method also includes determining one or more rewards based on the statistics. The method also includes adjusting multiple weights in multiple stochastic policies based on the one or more rewards, wherein the weights are used to determine the fuzzer and the feedback type in a subsequent interval of the fuzzing test.

Abstract: A method can include for vulnerability analysis based on input or output points inferred from execution traces can include monitoring application access operations to a memory, monitoring responses from the memory to the access operations, generating execution traces based on the monitored access operations and responses, the execution traces including data identifying an instruction to be performed and a corresponding memory location to access in performing the operation, identifying and recording whether the instruction corresponds to an input or an output to the system based on the generated execution traces, and determining vulnerabilities of the application based on the generated execution traces and identification of the instruction corresponding to the input or output.

Abstract: A method can include for vulnerability analysis based on input or output points inferred from execution traces can include monitoring application access operations to a memory, monitoring responses from the memory to the access operations, generating execution traces based on the monitored access operations and responses, the execution traces including data identifying an instruction to be performed and a corresponding memory location to access in performing the operation, identifying and recording whether the instruction corresponds to an input or an output to the system based on the generated execution traces, and determining vulnerabilities of the application based on the generated execution traces and identification of the instruction corresponding to the input or output.