Abstract: Systems and methods for computing times to remediate for asset vulnerabilities are described herein. In an embodiment, a server computer receives first vulnerability data for a plurality of entities identifying asset vulnerabilities and timing data corresponding to the vulnerability data indicating an amount of time between identification of an asset vulnerability and a result of the asset vulnerability. The server computer identifies a strict subset of the first vulnerability data that belongs to a particular category of a first plurality of categories. The server computer receives second vulnerability data for a particular entity identifying asset vulnerabilities. The server computer identifies a strict subset of the second vulnerability data the belongs to the particular category. Based, at least in part, on the strict subset of the first vulnerability data, the server computer computes a time to remediate the asset vulnerabilities in the strict subset of the second vulnerability data.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Systems and methods for computing times to remediate for asset vulnerabilities are described herein. In an embodiment, a server computer receives first vulnerability data for a plurality of entities identifying asset vulnerabilities and timing data corresponding to the vulnerability data indicating an amount of time between identification of an asset vulnerability and a result of the asset vulnerability. The server computer identifies a strict subset of the first vulnerability data that belongs to a particular category of a first plurality of categories. The server computer receives second vulnerability data for a particular entity identifying asset vulnerabilities. The server computer identifies a strict subset of the second vulnerability data the belongs to the particular category. Based, at least in part, on the strict subset of the first vulnerability data, the server computer computes a time to remediate the asset vulnerabilities in the strict subset of the second vulnerability data.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.

Abstract: Techniques for ranking a set of vulnerabilities of a computing asset and set of remediations for a computing asset, and determining a risk score for one or more computing assets are provided. In one technique, vulnerabilities of computing assets in a customer network are received at a vulnerability intelligence platform. Breach data indicating set of breaches that occurred outside customer network is also received. A subset of the set of vulnerabilities that are most vulnerable to a breach is identified based on the breach data. In another technique, multiple vulnerabilities of a computing asset are determined. A risk score is generated for the computing asset based on the vulnerabilities. In another technique, multiple remediations associated with a risk score and multiple vulnerabilities are identified. The remediations are ordered based on the remediations that would reduce the risk score the most if those remediations were applied to remove the corresponding vulnerabilities.