Patent Description:
During the development of software and applications, the procedure of scanning, analysis and remediation for security vulnerabilities are typically slow and manual. Basic techniques and tools in the art are known to scan and identify for vulnerabilities. However, experts are required to interpret the results, highlight the most relevant vulnerabilities, and suggest fixes. This usually takes a substantial amount of time, and such cybersecurity experts are in short supply. Software developers desire a faster process that can scale to meet demand, and maintain the quality of an expert analysis. Intelligence are desired to more efficiently and effectively scan software applications during their development stage.

<CIT> describes a threat score prediction model generated for assigning a threat score to a software vulnerability.

The foregoing and other objects, features, and advantages for embodiments of the present disclosure will be apparent from the following more particular description of the embodiments as illustrated in the accompanying drawings, in which reference characters refer to the same parts throughout the various views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating principles of the present disclosure.

The present disclosure may be embodied in various forms, including a system, a method, a computer readable medium, or a platform-as-a-service (PaaS) product for scanning and rectifying security vulnerabilities in software applications. In some examples, a technical advantage of the disclosures described herein may include the identification of security vulnerabilities in software applications scanned during their development stage. Another technical advantage may be the reduction of false positives and duplicates in the scan results. Yet another technical advantage may be the analysis of vulnerability root causes. Another technical advantage may include providing additional information to human security analyst to reduce their scope of analysis to increase their efficiency. Technical advantages may include the classification of identified security vulnerabilities, and their automated triage based on machine learning. In certain examples, a technical advantage may include the translation or interpretation of the scan results to determine a remediation of the security vulnerabilities identified by the scan. In an example, a technical advantage may include the presentation of recommendations to software developers via a user interface or scan report in order to enable the secure development of a software application. Accordingly, an exemplary benefit of the present disclosures may include a reduction in time for security analysts to assess vulnerabilities, and an improved confidence in the security of the software application being developed. While inefficient technologies exist that provide security analysts with basic scan results that detect vulnerabilities, a technical advantage of the present disclosures may include an assessment of the scan results and a determination of actual vulnerabilities versus false positives.

<FIG> illustrates an embodiment of such a system <NUM> that may be implemented in many different ways, using various components and modules, including any combination of circuitry described herein, such as hardware, software, middleware, application program interfaces (APIs), and/or other components for implementing the features of the circuitry. The system <NUM> may include a scan engine <NUM>, a vulnerability report engine <NUM>, an extraction engine <NUM>, a format engine <NUM>, a vector engine <NUM>, a classification engine <NUM>, an output engine <NUM>, a review engine <NUM>, and/or a report engine <NUM>. In an embodiment, the steps of the disclosed methods may be implemented by these engines <NUM>-<NUM>.

In an embodiment, the system <NUM> may include a computing device <NUM>, which may include a memory <NUM> and a processor <NUM>. The system <NUM> may also include generated user interfaces (UIs) <NUM>, and Representational State Transfer (REST) APIs <NUM> as shown in <FIG>, that may be adapted to enable communication between components, modules and databases. As discussed below, users may interface with the system <NUM> via the UIs <NUM>. In some embodiments, the memory <NUM> may include the components and modules of the system <NUM>, including the aforementioned engines <NUM>-<NUM>, the UIs <NUM>, and the REST APIs <NUM>. The system <NUM> may also include a source code database <NUM>, a vulnerability report database <NUM>, a security vulnerability database <NUM>, a java code repository or database <NUM>, and/or a trained model database <NUM>. Further, the system <NUM> may include a software-security server <NUM> and a router.

The computing device <NUM>, the databases <NUM>-<NUM>, the software-security server <NUM> and the router may be logically and physically organized in many different ways, in accordance with certain embodiments of the present disclosures. The databases <NUM>-<NUM> may be implemented with different types of data structures (such as linked lists, hash tables, or implicit storage mechanisms), and may include relational databases and/or object-relational databases. The databases <NUM>-<NUM> may be stored in the memory <NUM> of the device <NUM> and/or the software-security server <NUM>, or they may distributed across multiple devices, servers, processing systems, or repositories. For example, the vulnerability report database <NUM> may be configured to communicate with the software-security server <NUM>, and the vulnerability report engine <NUM> and the extraction engine <NUM> may be configured to communicate with the software-security server <NUM>. In certain embodiments, the computing device <NUM> may include communication interfaces, display circuitry, and input/output (I/O) interface circuitry that may be controlled by the processor <NUM> in order to perform the process steps discussed below via the components and modules illustrated in <FIG>. As discussed below, users may interface with the system <NUM> via the UIs <NUM> displayed by the display circuitry.

<FIG> illustrates an embodiment of a scan engine <NUM> configured to scan source code <NUM> stored in a source code database <NUM>. In an embodiment, the computing device <NUM> may include system circuitry that may implement any desired functionality of the system <NUM>. As discussed below, in some embodiments, the scan engine <NUM> may be configured to scan source code <NUM> for security vulnerabilities <NUM>. For example, the scan engine <NUM> may be implemented on an application-scanning client <NUM>, as further discussed below, that may be configured to communicate with a source code database <NUM> that stores source code <NUM> to be scanned by the system <NUM>. In an embodiment, the application-scanning client <NUM> may comprise a computing device <NUM>. Alternatively, the source code database <NUM> may be implemented on the computing device <NUM>, which may be configured to communicate with an application-scanning client <NUM> implemented on another device that may be adapted to communicate with a display <NUM>. In some embodiment, as shown in <FIG>, the scan engine <NUM> may be further configured to generate vulnerability reports <NUM>, and transmit the vulnerability reports <NUM> to the vulnerability report engine <NUM>.

In certain embodiments, as an initial step of the disclosed methods, the scan engine <NUM> may receive a scan request to scan source code <NUM>. In some embodiments, this may be the initial stage of the process where a client or user requests an analysis of source code <NUM> for the detection of security vulnerabilities or threats <NUM> within, or related to, the source code <NUM>. In an example, this initial analysis may be performed by the system <NUM> in conjunction with a code analyzer <NUM>. In certain embodiments, the code analyzer <NUM> in the scan engine <NUM> may be implemented by commercial packages or open source solutions. For example, the code analyzer <NUM> may include scanning tools such as Veracode, HCL App Scan, Checkmarx, and/or Fortify. Generally, the code analyzer <NUM> attempts to protect systems from security flaws in business-critical software applications through the use of vulnerability reports <NUM>. The code analyzer <NUM> may scan source code <NUM> of a software product or application <NUM>, and generate vulnerability reports <NUM>. In certain embodiments, the vulnerability report engine <NUM> may generate vulnerability report <NUM>.

In some embodiments, source code <NUM> for an application <NUM> that is selected, received and/or identified by a client <NUM> may be stored within the source code database <NUM>. This may include the source code <NUM> that the client <NUM> requests to be assessed or analyzed in order to determine if the source code <NUM> includes security vulnerabilities <NUM> that could be deemed as exploitable by a security analyst. In an embodiment, the source code <NUM> may be pushed or transmitted to an application-scanning client <NUM>. The application-scanning client <NUM> may include static application security testing software. In certain embodiments, a user or a client <NUM> may enter, input, submit or transmit source code <NUM> of a software application <NUM> to the application-scanning client <NUM>.

The application-scanning client <NUM> may generate vulnerability reports <NUM> that correspond to the scan of source code <NUM>. Typically, a security analyst may spend an extended period of time reviewing such a file via the application-scanning client <NUM> in order to determine source code <NUM> that may be a security vulnerability/threat <NUM>, and to determine false positives that may be ignored. The vulnerability reports <NUM> may be stored in the software-security server <NUM>. A vulnerability report <NUM> may include scan project code used by the code analyzer <NUM>, which may include a suite of tools used by security professionals to scan enterprise software for security issues. In some embodiments, the vulnerability reports <NUM> may be stored in the vulnerability report database <NUM>, which may include a relational database service (RDS). Vulnerability reports <NUM> that are stored in the vulnerability report database <NUM> may be transmitted to the software-security server <NUM>. In an embodiment, the software-security server <NUM> may be configured to transmit the vulnerability reports <NUM> to the extraction engine <NUM> via a REST API <NUM>, as denoted by the large arrow between the vulnerability report engine <NUM> and the extraction engine <NUM> shown in <FIG>.

<FIG> illustrates an embodiment of a feature extraction process implemented by the extraction engine <NUM>, which may be configured to communicate with the software-security server <NUM>. The feature extraction process of the disclosed methods may include the extraction of features <NUM> from vulnerability reports <NUM> that indicate whether a part of the source code <NUM> may be vulnerable or not based on the vulnerability reports <NUM> generated by the code analyzer <NUM>, and the transmission of the features <NUM> to the format engine <NUM>. This process may include the initial step of receiving (block <NUM>) vulnerability reports <NUM> from the software-security server <NUM> via the REST API <NUM>. Features <NUM> may be retrieved (block <NUM>) that comprise different components of security vulnerabilities <NUM>. In certain embodiments, such retrieved features <NUM> may identify the relevant threat of the security vulnerabilities <NUM> of the source code <NUM> based on the corresponding vulnerability reports <NUM>.

The feature extraction process may also include the step of source code extraction. See block <NUM>. This step may be performed by a source code extractor <NUM>, as shown in <FIG>, which extracts original source code <NUM> from the application <NUM> that was scanned and/or tested. See block <NUM> in <FIG>. The extracted source code <NUM> may comprise the code <NUM> corresponding to the retrieved features <NUM>. As such, the source code extractor <NUM> may be configured to communicate with the source code database <NUM>, either directly or indirectly as shown in <FIG>. In addition, the process may include the step of pushing or transmitting (block <NUM> in <FIG>) security vulnerabilities <NUM> of the extracted source code <NUM> to the vulnerabilities database <NUM>. This transfer may be performed via the format engine <NUM>. Accordingly, all of the security vulnerabilities <NUM> may be detected by the code analyzer <NUM> and the source code <NUM> may be transmitted to, and stored, in the vulnerabilities database <NUM> for further processing by the system <NUM>.

In an embodiment, the format engine <NUM> may format the security vulnerabilities <NUM> received from the source code extractor <NUM> of the extraction engine <NUM> into a format configured to be received by the vulnerabilities database <NUM>. In an example, the received security vulnerabilities <NUM> may be stored in a format compatible with, or usable by, the system <NUM>. The format engine <NUM> may store all the security vulnerabilities <NUM> that were identified by the code analyzer <NUM>, and received from the extraction engine <NUM>, in a format adapted to enable conversion of the security vulnerabilities <NUM> by the system <NUM>. The format may be readable by the system <NUM>. In this format, the cleaned or reformatted vulnerabilities <NUM> may be analyzed via analytics experiments performed by the system <NUM>. The cleaned vulnerabilities <NUM> stored in the vulnerabilities database <NUM> may be adapted for further conversion by the system <NUM>. In certain embodiments, the vulnerabilities database <NUM> may be adapted to transmit the cleaned security vulnerabilities <NUM> to the vector engine <NUM>.

<FIG> illustrates an example of a vector engine <NUM>, and its interactions with the components of other engines <NUM> and <NUM> as denoted by the large arrows between the engines. The vector engine <NUM> may be configured to create feature vectors <NUM> for training machine learning (ML) models <NUM> in order to predict or determine if a security vulnerability <NUM> is actually a threat. The cleaned security vulnerabilities <NUM> may be converted from human readable features <NUM> into a format that can be processed by a machine learning model <NUM>. In some embodiments, abstract syntax trees (AST) may be utilized as a method of breaking down the data for the cleaned security vulnerabilities <NUM> into a format that can be processed by a machine learning model <NUM>. In an embodiment, as discussed below, the tokenizer <NUM> in the vectorising process may be substituted with ASTs <NUM>. A syntax tree <NUM> may comprise a tree representation of the abstract syntactic structure of source code <NUM> written in a programming language. Each node of the tree <NUM> may denote a construct occurring in the source code <NUM>.

As shown in <FIG>, an orchestrator <NUM> of a vector engine <NUM> may receive cleaned vulnerabilities <NUM> from the format engine <NUM>. In some embodiments, the vulnerabilities database <NUM> may be configured to transfer cleaned security vulnerabilities <NUM> to the orchestrator <NUM> via an REST API <NUM>. A vulnerability router <NUM> may be configured to communicate with the orchestrator <NUM>. The vulnerability router <NUM> may scan the list of cleaned vulnerabilities <NUM>, and classify each cleaned vulnerability <NUM> based on the type of security vulnerability <NUM> to which it corresponds. Based on the determined type of vulnerability <NUM> for a classified vulnerability <NUM>, the classified vulnerability <NUM> may be routed in the system <NUM> based on predetermined machine learning rules or programming rules.

In certain embodiments, the vector engine <NUM> may include grammar files <NUM> that may define speech-to-text words, terms and phrases <NUM> which a grammar engine may recognize on a user device <NUM>. Grammar files <NUM> may comprise. cs, and/or. In an embodiment, the terms <NUM> listed in the grammar file <NUM> may be those for which the grammar engine searches and compares against verbal responses. When the grammar engine finds a matching term <NUM>, the grammar engine may execute an associated command or enter the term <NUM> into a field. A lexical analyzer <NUM> may receive a grammar file <NUM> and vulnerability features <NUM>, and perform tokenization via a tokenizer <NUM> in order to return features <NUM> in accordance with certain embodiments.

The tokenizer <NUM> may perform lexical analysis, lexing or tokenization. This may include the process of converting a sequence of characters <NUM> for the cleaned vulnerability <NUM> into a sequence of tokens <NUM>. Tokenized vulnerability features <NUM> may include vulnerabilities <NUM> stored in memory <NUM> in tokenized format, which may comprise such a sequence of tokens <NUM>. The repositories <NUM> may be selected where the targeted source code <NUM> may be hosted. In an embodiment, the repositories <NUM> may be selected based on their size. The hosted code <NUM> may be transmitted to a tokenizer <NUM>, which may include a tool for language recognition. This tokenizer <NUM> may tokenize the repositories <NUM> and generate tokens <NUM>.

In some embodiments, the vector engine <NUM> may include a FastText create model <NUM>, which may include a library for learning of word embeddings and text classification. The FastText create model <NUM> may receive tokens <NUM> and generate a trained embedding model <NUM>. The trained embeddings model <NUM> may include an embedding, which may include a mapping of a discrete, categorical variable to a vector of continuous numbers. In certain embodiments, each cleaned vulnerability <NUM> may be mapped to a vulnerability category <NUM> in order to generate a vulnerability ID <NUM> for each cleaned vulnerability <NUM> mapped to a category <NUM>. In certain embodiments, a vectorizer <NUM> may receive the tokenized vulnerability features <NUM> as input, and may output a single feature vector <NUM>. The feature vectors <NUM> may include all of the output collected from the vectorizer <NUM>. Furthermore, a feature vector can include a link to a source code tree, where relevant source code can be obtained. These feature vectors <NUM> may be transmitted to the classification engine <NUM>.

<FIG> illustrates an embodiment of a classification engine <NUM>, and its interactions with the components of other engines <NUM> and <NUM>, in accordance with certain embodiments of the disclosed systems <NUM>. The feature vectors <NUM> may be utilized as input to the pre-trained ML model <NUM>, predetermined programming rules <NUM> and/or blanket rules <NUM> in order to determine whether the cleaned vulnerability <NUM> is a threat or not. The classification engine <NUM> may determine whether a vulnerability <NUM> is a threat or not through at least three different methods: blanket rules <NUM>, programming rules <NUM> and/or ML models <NUM>. The blanket rules <NUM> and programming rules <NUM> may be applied to automated triaging methods configured to automate the triaging of the vulnerabilities <NUM>. In certain embodiments, blanket rules <NUM> may be applied to vulnerabilities <NUM> routed through the vulnerability router <NUM>, and the ML model <NUM> may not be required. Such a vulnerability <NUM> may be selected based on historical data that consistently indicates that the vulnerability <NUM> is exploitable. As such, it may be reasonable to automatically assume that the identified vulnerability <NUM> may be exploitable again. In some embodiments, programming rules <NUM> may be applied to the vulnerabilities <NUM> transmitted from the vulnerability router <NUM>. The programming rules <NUM> may scan a vulnerability <NUM> in order to detect common patterns that have been identified as a threat. In an embodiment, an AST <NUM> may be processed by the system <NUM> but may be removed when converted. The classification engine <NUM> may also utilize machine learning. A vulnerability <NUM> may be processed by the system <NUM> (e.g., tokenized and vectorized) and the feature vectors <NUM> may be transmitted or inputted into the pre-trained model <NUM>, which may have previously analyzed such feature vectors <NUM>. As more vulnerabilities <NUM> may be converted into feature vectors <NUM>, the system <NUM> may more often utilize the ML model <NUM> because the pre-trained model <NUM> may be more likely to have already determined whether the specific vulnerability <NUM> is exploitable. The exemplary classification engine <NUM> shown in <FIG> may determine whether a vulnerability <NUM> is a threat or not. The classification engine <NUM> may include a deterministic classifier <NUM>, which may implement a classifying algorithm whose resulting behavior may be determined by its initial state and inputs. In an embodiment, the deterministic classifier <NUM> may not be random or stochastic. The classification engine <NUM> may also include a probabilistic classifier <NUM>, which may include a classifier configured to predict a probability distribution over a set of classes. In an embodiment, the probabilistic classifier <NUM> may be based on an observation of an input, rather than only outputting the most likely class to which the observation may belong. In addition, the classification engine <NUM> may include a train classifier <NUM>, which may be configured to be trained based on the feature vectors <NUM>. In some embodiments, the train classifier <NUM> may be configured to train the deterministic classifier <NUM> and/or the probabilistic classifier <NUM>. In certain embodiments, the train classifier <NUM> may be configured to train the trained model <NUM>. Accordingly, the train classifier <NUM> may be adapted to communicate with the trained model <NUM>, which may be included in the output engine <NUM>. Rules (e.g., blanket rules <NUM>) may be transferred to the deterministic classifier <NUM> as a set of rules. For example, blanket rules <NUM> may be implemented if the source code <NUM> is identifiable as being a threat based on historical data that consistently indicates that the vulnerability <NUM> is exploitable.

As shown in <FIG> and <FIG>, the vulnerability router <NUM> may either route the vulnerabilities <NUM> directly to the rule-based deterministic classifier <NUM> or the ML-based probabilistic classifier <NUM> via the vector engine <NUM>. A set of vulnerability types may be associated with the rules <NUM> and <NUM>. The vulnerability router <NUM> may determine a vulnerability type in the input vulnerability scan. When rules <NUM> or <NUM> associated with the determined vulnerability type are identified, the vulnerability router <NUM> may then route that input vulnerability scan to the deterministic classifier <NUM> for processing under the identified and pre-established rules. Otherwise, the vulnerability router <NUM> may route the input vulnerability scan to the probabilistic ML classifier <NUM>. Example embodiments of triage methods for establishing the various rules <NUM> and <NUM> for various types of vulnerabilities are further discussed below in relation to <FIG>.

In some other embodiments, the vulnerabilities <NUM> may be routed to both the rule-based deterministic classifier <NUM> and the ML-based probabilistic classifier <NUM>, and if the determination of whether the vulnerabilities <NUM> are exploitable are inconsistent between the deterministic classifier <NUM> and the ML-based probabilistic classifier <NUM>, an additional arbitration may be performed to determine which classifier is more trustworthy.

An embodiment of the output engine <NUM> is also in <FIG>. The output from the output engine <NUM> may include initial findings received from the trained model <NUM> for the predictions of whether labelled vulnerabilities <NUM> are a threat or not. The trained model <NUM> may be stored in the trained model database <NUM>. In some embodiments, the trained model <NUM> may be transmitted to the probabilistic classifier <NUM>. The classification engine <NUM> may generate a list of labelled vulnerabilities <NUM>, and/or predictions thereof, that may be stored and later reviewed by the system <NUM>.

<FIG> illustrates an embodiment of the review engine <NUM>, its interactions with the components of other engines <NUM>-<NUM> and <NUM>, and exemplary processes implemented by the review engine <NUM>. For example, the review engine <NUM> may be implemented to include a process for an output review (block <NUM>) and a process for a vulnerability review and a model update (block <NUM>). Through these processes, the review engine <NUM> may review the vulnerabilities <NUM> that the system <NUM> determined as being exploitable, and may use such vulnerabilities <NUM> to retrain the model <NUM> for future usage. This review may be transmitted back into the model <NUM> in order to further train the model <NUM>.

The vulnerability review and model update process <NUM> may include the steps of updating vulnerabilities (block <NUM>), retaining a model (block <NUM>), and updating rules (block <NUM>). This process may be configured to update the vulnerabilities database <NUM> with vulnerabilities <NUM> determined to be exploitable for the blanket rules <NUM>. The updated vulnerabilities <NUM> may be transmitted back to the vulnerabilities database <NUM>, which may store the cleaned vulnerabilities <NUM> in the format compatible with the system <NUM>. In order to retrain the model <NUM>, findings may be received from a security analyst (SA) review <NUM>, a data scientist (DS) review <NUM>, and/or a quality assurance (QA) review <NUM>, and a data analysis <NUM> may be performed. Such findings received from the data analysis <NUM> may be transmitted to the orchestrator <NUM> of the vector engine <NUM>. The findings may be utilized to update the blanket rules <NUM>, the model <NUM> and the list of vulnerabilities <NUM>.

The updated blanket rules <NUM> may include rules updated by the findings received from the reviews <NUM>-<NUM> and the data analysis <NUM>. These reviews <NUM>-<NUM> may be performed by a data scientist and/or a security analyst. The data analysis <NUM> may be performed on new data in order to determine an optimal method for updating the blanket rules <NUM> and retraining the model <NUM>. An automated triaging method instance <NUM> may be configure to automate the triaging of vulnerabilities <NUM>. The vulnerability review and model update process <NUM> may be based on the combination of the review results <NUM> received from the security analyst review <NUM>, the data scientist review <NUM>, and/or the quality assurance review <NUM>. The review results <NUM> may be transmitted to the report engine <NUM>.

The report engine <NUM> may be configured to receive the review results <NUM> from the review engine <NUM>. A full report may be generated that may include all the vulnerabilities <NUM> that are actually a threat, as analyzed by a quality assurance review <NUM>. Quality Assurance Labelled Vulnerabilities <NUM> may be generated to include the vulnerabilities <NUM> that have passed through the system <NUM> and assessed by the Quality Assurance review <NUM>. This review <NUM> may be performed by a quality assurance expert. A final report <NUM> may be generated for a client <NUM>, and a HTML Report <NUM> may be generated to report all of the findings in a HTML format.

The final report <NUM> and the HTML Report <NUM> may be displayed via a device <NUM>. The UIs <NUM> may be displayed locally using the display circuitry, or for remote visualization, e.g., as HTML, JavaScript, audio, and video output for a web browser that may be run on a local or remote machine. The UIs <NUM> and the I/O interface circuitry may include touch sensitive displays, voice or facial recognition inputs, buttons, switches, speakers and other user interface elements. Additional examples of the I/O interface circuitry includes microphones, video and still image cameras, headset and microphone input / output jacks, Universal Serial Bus (USB) connectors, memory card slots, and other types of inputs. The I/O interface circuitry may further include magnetic or optical media interfaces (e.g., a CDROM or DVD drive), serial and parallel bus interfaces, and keyboard and mouse interfaces.

In an embodiment, the components and modules for an exemplary system may compartmentalized into nine sections: Scan; Store Reports; Extract Features; Store all vulnerabilities in a canonical format; Create feature vectors, and/or abstract syntax trees; Classification; Initial Output; Review vulnerabilities; and, Final output plus Report generation. This listing of compartmentalized sections are not necessary in chronological order.

In an embodiment, the system <NUM> may include the steps of collecting and using different scan reports. These scan reports may be collected from multiple vendors. The scan reports may include the vulnerability reports <NUM> received from the code analyzer <NUM>, in combination with reports from other vendors for various types of scans. The automated triaging may include a hybrid methodology. For example, the system <NUM> may use rules, filters, machine learning in conjunction with various feature vectors in combination. <FIG> illustrates examples of automated triage methods. Such methods may be trained and validated on various datasets for assessment purposes. <FIG>-(b) illustrates examples of identified issue types and their corresponding percentage of total triage time, the highest remediation priority, and the automated triage method implemented.

In an embodiment, the system <NUM> may include integration of existing toolchains with custom annotated tags/variables so that automated-FPA files can be integrated back to existing toolchains. For example, the system <NUM> may be integrated with extract scan results from an application-scanning tool that may be implemented in memory <NUM> to automatically triage issues and push results back to the application-scanning tool. <FIG> illustrates such a system <NUM>, in accordance with certain embodiments. In an embodiment, the system <NUM> may implement a vulnerability identification prioritization and remediation (ViPR) tool in the memory <NUM>, which may include an integrated repository of data and analysis tools. The system <NUM> may include a frontend <NUM> and an API <NUM>. The frontend <NUM> may communicate with an user, and, the API <NUM> may communicate with the software-security server <NUM>. Further, the system <NUM> may combine and use information from scan reports of both Static application security testing (SAST) and Dynamic application security testing (DAST). The system <NUM> may combine SAST and DAST triage judgements to automatically propose remediation actions in a unified way, e.g. so that one fix may solve both a SAST and DAST issue.

The automated triage rules as shown in <NUM> and <NUM> of <FIG> used for the deterministic classifier <NUM> may be created for each of a predetermined set of types of vulnerabilities. An automated triage rule library may be established for the predetermined set of types of vulnerabilities. Such an automated rule library, for example, may include an automated triage policy (ATP) for each type of vulnerabilities, and may thus be referred to as an ATP rule library. Each ATP may further include one or more automated methods (ATMs) in the form of various triage algorithms that may be invoked by the deterministic classifier <NUM> of <FIG> for assessing an input vulnerability. The assessment output of the deterministic classifier <NUM> may indicate whether the input vulnerability is not exploitable, exploitable, or that the exploitability is uncertain.

As such, the orchestrator <NUM> of <FIG> may first map an input vulnerability (e.g., a data frame from the vulnerability database <NUM> of <FIG>) to either the deterministic classifier <NUM> or the ML probabilistic classifier <NUM> using the vulnerabilities router <NUM> of <FIG>. If the input vulnerability is mapped to the ML classifier <NUM>, the feature vector creation process would be triggered, the feature vectors would be subsequently created for the input vulnerability, and the ML model would be loaded and invoked for processing the feature vectors to classify the input vulnerability. If the input vulnerability is mapped to the deterministic classifier <NUM>, the classification engine <NUM> would further map this input vulnerability to one of the predetermined set of types of vulnerabilities and a corresponding ATP. The ATP and ATMs therein would be called from the ATP rule library and passed along with the data frame of the input vulnerability to the deterministic classifier <NUM> for classification of the input vulnerability.

An example ATP rule library is shown as <NUM> in <FIG>. The ATP rule library <NUM> may include a plurality of ATPs <NUM>, each for a type of the predetermined set of types of vulnerabilities. Each ATP <NUM> may include a set of ATMs <NUM>. Each ATM, for example, may include one or more particular algorithms for deterministic vulnerability classification. As further shown in <NUM> of <FIG>, the mapping of an input vulnerability to a particular ATP may be formed by the vulnerability mapper <NUM>. In some implementations, the vulnerability mapper <NUM> may be part of the ATP rule library. An input vulnerability (e.g., a vulnerability data frame from the vulnerability database <NUM> of <FIG>) may be passed to the ATP rule library <NUM>. The ATP rule library <NUM> may output an ATP and pass the output ATP to the deterministic classifier <NUM>, as shown by the arrow <NUM> of <FIG>.

ATPs <NUM> and ATMs <NUM> for each of the predetermined set of types of vulnerabilities may be created in various manners and loaded into the ATP rule library <NUM>. The predetermined set of types of vulnerabilities may be established based on any methodologies. For example, the predetermined set of type of vulnerabilities may be based on Fortify vulnerability categories and types determined and defined via historical Fortify vulnerability scans and analysis. Each type of vulnerabilities may be associated with a vulnerability identifier (ID). An example for creating an ATP and ATMs for each of the predetermined set of types of vulnerabilities is shown in <NUM> of <FIG>.

The ATP and ATM creation process <NUM> may include a manual triage policy (MTP) generation process and an ATP/ATM generation process for each one of these types of vulnerabilities, as shown by <NUM> and <NUM> of <FIG>, respectively. As shown in <NUM>, the MTP may be specified as a definition of steps as part of improved quality (IQ) guidelines that security analysts (SAs) must take in order to triage (classify) the vulnerability as, for example, "not an issue", "exploitable", and "suspicious. " The MTP for a particular type of vulnerabilities, for example, may be represented by a list of questions that the SAs must check. The list of questions may be organized as a decision tree. In other words, the order in which the questions are asked is determined based on a decision tree. Specifically, what next question to ask in the list depends on the answer and output the previous question in the list. A list of questions and a decision tree may be created for each type of vulnerabilities. An example list of MTP questions for a "resource injection" type of vulnerability (example vulnerability ID of <NUM>) are shown below in Table <NUM>.

Table I above contains both the list of questions and the information about the decision tree for the list of questions. For example, when the answer to the first question in the list may be "out of scope" indicating that there is no issue with this particular vulnerability, the decision tree ends without proceeding further. However, if the answer to the question is a "No" or "Not Sure", then the decision tree proceeds to the next question and question "<NUM>-<NUM>" needs to be answered, as indicated in Table I. If the answer to question "<NUM>-<NUM>" is "Not an Issue", then the decision tree again ends. Otherwise, the decision tree proceeds to the next question and as specified in Table I, question "<NUM>-<NUM>" needs to be answered next. This process proceeds as indicated in the example Table I until the decision tree ends. Table I thus prescribes a conditional sequence of triage steps. Each step poses a question for SAs to answer. The answer to a question decides a next step (either an end of the decision tree or a next question). Table I provides a path to reach a final triage decision.

<FIG> shows and example process of <NUM> of <FIG> for generating the IQ guidelines that may be automated to form the ATPs and ATMs. The process <NUM> may be used to process data sources including contextual data <NUM>, experimental data <NUM>, and computational data <NUM> via an iterative validation (<NUM>), enhancement (<NUM>), encoding (<NUM>), and aggregation (<NUM>) procedure with an output being processed by the reaction module <NUM> to generate the IQ guidelines stored in the database <NUM>. The IQ guidelines are used for the generation of ATPs and ATMs.

Returning to <FIG>, as further shown in <NUM>, once the MTP is created for each type of vulnerabilities, it may then be further determined what can be codified in the MTP to generate automated triage methods (ATMs) for the MTP. In particular, each of the questions in the MTP may correspond to a manual triage method (MTM) that may be converted and codified into an ATM containing automated algorithms (as shown by <NUM> of <FIG>). Each ATM may be codified in a function that may be called by the classification engine <NUM>. An automated triage policy (ATP) corresponding to the MTP may identify the codified ATMs. An example is shown in Table II below.

In some embodiments, as shown by the vulnerability-ATP mapping in <FIG>, the ATP library includes multiple ATPs <NUM>. Each ATP may be associated with a unique identifier and represents a policy as described above. Each type of vulnerability may be associated with one of the ATPs (as shown by the mapping from <NUM> to <NUM> in <FIG>) whereas each ATP may map to one or more types of vulnerabilities (as shown by the mapping from <NUM> to <NUM> in <FIG>, indicating that multiple different types of vulnerabilities may use a same ATP with a same decision tree <NUM>). Each ATP further encapsulates a decision tree as described above and links to one or more ATMs, as shown in <NUM> of <FIG>. Each ATP thus may be embodied as an ordered container of ATMs, as shown in <NUM> and <NUM> of <FIG>. Each ATM corresponds to a step in the decision tree. ATMs are codified and may include various algorithms. An ATM as a callable function may be shared by different ATPs (as shown by the common "ATM_Third_Party" and "ATM_Is-Trust" functions between different ATPs in <NUM> of <FIG>). The ATMs thus may be collected in a unified function library or code repository. Each ATP, when referring to an ATM in a particular step in its decision tree, may identify the ATM by its unique function identifier in the function library or code repository, as shown in <NUM> of <FIG>. Example codes of an ATP integrating a decision tree calling various ATMs are shown below:
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In some embodiments, the output of the classification engine <NUM> of <FIG> above may include the input data frame with some additional columns. For example, one of the additional columns may include the prediction from the classification engine <NUM>. Another additional column may include indication of the classifier (the deterministic classifier <NUM> or the ML probabilistic classifier <NUM>) that is used for the prediction. Another additional column may include information indication the decision tree used in the deterministic classifier. The decision tree used may be identified by the ATP identifier.

The generation of the manual triage policy (MTP) or the decision tree for each of the predetermined set of types of vulnerabilities (<NUM> of <FIG>) may be automated using a separate machine-learning model. For example, a machine learning model may be trained for selecting a list of questions from a question library in a particular order based on historical vulnerability prediction accuracy.

As shown in <FIG>, the method implemented by the system <NUM> may include the step of selecting projects via a user interface <NUM>. See block <NUM>. The frontend <NUM> may request projects (see block <NUM>), and the API <NUM> may transmit such project requests to the software-security server <NUM>. See block <NUM>. As a result, the APO <NUM> may receive projects. See block <NUM>. The frontend <NUM> may be adapted to display the received projects via the user interface <NUM>. See block <NUM>. In some embodiments, one of the displayed projects may be selected via the user interface <NUM>. See block <NUM>. In certain embodiments, the frontend <NUM> may be identify or determine the selected project. See block <NUM>. The API <NUM> may be adapted to extract features for the selected project from the software-security server <NUM>. See block <NUM>. In an embodiment, the API <NUM> may be further adapted to: apply rules (block <NUM>), apply filters (block <NUM>), apply programmed filters (block <NUM>), and/or apply machine learning models (block <NUM>). In addition, the API <NUM> may be adapted to export results to the software-security server <NUM>, in accordance with certain embodiments. See block <NUM>.

In some embodiments, the communication interfaces may include wireless transmitters and receivers (herein, "transceivers") and any antennas used by the transmit-and-receive circuitry of the transceivers. The transceivers and antennas may support WiFi network communications, for instance, under any version of IEEE <NUM>, e.g., <NUM>. 11n or <NUM>. 11ac, or other wireless protocols such as Bluetooth, Wi-Fi, WLAN, cellular (<NUM>, LTE/A). The communication interfaces may also include serial interfaces, such as universal serial bus (USB), serial ATA, IEEE <NUM>, lighting port, I<NUM>C, slimBus, or other serial interfaces. The communication interfaces may also include wireline transceivers to support wired communication protocols. The wireline transceivers may provide physical layer interfaces for any of a wide range of communication protocols, such as any type of Ethernet, Gigabit Ethernet, optical networking protocols, data over cable service interface specification (DOCSIS), digital subscriber line (DSL), Synchronous Optical Network (SONET), or other protocol.

The system circuitry may include any combination of hardware, software, firmware, APIs, and/or other circuitry. The system circuitry may be implemented, for example, with one or more systems on a chip (SoC), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), microprocessors, discrete analog and digital circuits, and other circuitry. The system circuitry may implement any desired functionality of the system <NUM>. As just one example, the system circuitry may include one or more instruction processor <NUM> and memory <NUM>. The memory <NUM> may store, for example, control instructions for executing the features of the system <NUM>. In one implementation, the processor <NUM> may execute the control instructions to carry out any desired functionality for the system <NUM>. Control parameters may provide and specify configuration and operating options for the control instructions and other functionality of the system <NUM>. The system <NUM> may further include various databases or data sources, each of which may be accessed by the system <NUM> to obtain data for consideration during any one or more of the processes described herein.

In an embodiment, a method or system <NUM> for managing software may include the steps of scanning source code of a software product or application <NUM> to detect potential vulnerability issues, and generating an electronic document report listing detected potential vulnerability issues. The method/system may further include the steps of: extracting features from the electronic document report for each potential vulnerability issue; receiving policy data and business rules; comparing the extracted features relative to the policy data and business rules; and, determining a token based on the source code of a potential vulnerability issue. Further, the method/system may include the steps of: determining a vector based on the extracted features of a potential vulnerability issue and based on the token, and selecting one of a plurality of vulnerability-scoring methods based on the vector. In an embodiment, the vulnerability-scoring methods may be a machine learning modelling <NUM> method, a blanket-rules <NUM> automated triaging method, and/or a programming-rules <NUM> automated triaging method. In accordance with certain embodiments, the plurality of vulnerability-scoring methods may include any combination of such methods. The method/system may also include the steps of determining a vulnerability accuracy score based on the vector using the selected vulnerability-scoring method, and displaying the vulnerability accuracy score to a user. In an embodiment, the plurality of machine learning models may include random forest machine learning models.

In certain embodiments, as illustrated in <FIG>, a method or system <NUM> for managing software may include the steps of: obtaining an electronic document listing potential vulnerability issues of a software product (block <NUM>); extracting features from the electronic document for each potential vulnerability issue (block <NUM>); determining a vector based on the extracted features (block <NUM>); selecting one of a plurality of machine-learning modelling methods and automated-triaging methods based on the vector (block <NUM>); and determining a vulnerability accuracy score based on the vector using the selected method (block <NUM>). The method/system may further include the steps of scanning source code of the software product to detect the potential vulnerability issues, and generating the electronic document based on the detected potential vulnerability issues. Further, the method/system may include the steps of: receiving policy data or business rules; comparing the extracted features relative to the policy data or business rules; and, determining a token based on the scanned source code corresponding to at least one of the detected potential vulnerability issues. In some embodiments, the vector may be based on the token. The method/system may also include the step of displaying the vulnerability accuracy score to a user. In an embodiment, the machine learning modelling methods may include random forest machine learning models. In some embodiments, the automated-triaging methods may include blanket-rules automated triaging methods and/or programming-rules automated triaging methods. In certain embodiments, a method or system for accessing software vulnerability may include the steps of: accessing an automated triage rule library comprising a plurality of pre-defined automated triage policies corresponding to a plurality of predetermined vulnerability types, wherein each automated triage policy comprises a decision tree for determining whether one of the predetermined plurality of vulnerability types is exploitable; accessing a machine learning model library for probabilistic determination of whether one of the predetermined plurality of predetermined vulnerability types is exploitable; obtaining an electronic document listing potential vulnerability issues of a software product based on source code of the software product; determining whether the potential vulnerability issues are associated with one of the plurality of predetermined vulnerability types; and when it is determined that the potential vulnerability issues are associated with the one of the plurality of predetermined vulnerability types, determining whether the software product is exploitable based on processing the electronic document using an automated triage policy retrieved from the automated triage rule library associated with the one of the plurality of predetermined vulnerability types and a corresponding decision tree, otherwise determining probabilistically whether the software product is exploitable based on processing the electronic document using a machine learning model from the machine learning model library.

All of the discussion, regardless of the particular implementation described, is exemplary in nature, rather than limiting. For example, although selected aspects, features, or components of the implementations are depicted as being stored in memories, all or part of the system or systems may be stored on, distributed across, or read from other computer readable storage media, for example, secondary storage devices such as hard disks, flash memory drives, floppy disks, and CD-ROMs. Moreover, the various modules and screen display functionality is but one example of such functionality and any other configurations encompassing similar functionality are possible.

The respective logic, software or instructions for implementing the processes, methods and/or techniques discussed above may be provided on computer readable storage media. The functions, acts or tasks illustrated in the figures or described herein may be executed in response to one or more sets of logic or instructions stored in or on computer readable media. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like. In one embodiment, the instructions are stored on a removable media device for reading by local or remote systems. In other embodiments, the logic or instructions are stored in a remote location for transfer through a computer network or over telephone lines. In yet other embodiments, the logic or instructions are stored within a given computer, central processing unit ("CPU"), graphics processing unit ("GPU"), or system.

Claim 1:
A system (<NUM>) for assessing and remedying software vulnerability, comprising:
a memory (<NUM>) to store executable instructions; and
a processor (<NUM>) adapted to access the memory (<NUM>), the processor (<NUM>) further adapted to execute the executable instructions stored in the memory (<NUM>) to:
access an automated triage rule library (<NUM>) comprising a plurality of pre-defined automated triage policies corresponding to a plurality of predetermined vulnerability types, wherein each automated triage policy comprises a decision tree (<NUM>) for determining whether one of the plurality of predetermined vulnerability types is exploitable;
access a machine learning model library for probabilistic determination of whether one of the predetermined plurality of predetermined vulnerability types is exploitable;
obtain an electronic document listing potential vulnerability issues of a software product based on source code of the software product;
determine whether the potential vulnerability issues are associated with one of the plurality of predetermined vulnerability types;
when it is determined that the potential vulnerability issues are associated with the one of the plurality of predetermined vulnerability types, determine whether the software product is exploitable based on processing the electronic document using an automated triage policy retrieved from the automated triage rule library (<NUM>) associated with the one of the plurality of predetermined vulnerability types and a corresponding decision tree (<NUM>), otherwise determine probabilistically whether the software product is exploitable based on processing the electronic document using a machine learning model from the machine learning model library, wherein the decision tree (<NUM>) comprises a set of progressively ordered automated triage methods, wherein each automated triage method in the automated triage policy is configured to generate a triage output when processing the electronic document; and
determine a remediation of the vulnerability issues based on the triage output.