Manage projects

• 1: Findings
• 2: Dependencies
• 3: Endor scores
• 3.1: Activity score factors
• 3.2: Popularity score factors
• 3.3: Security score factors
• 3.4: Code quality score factors
• 4: Call graphs

1 - Findings

A finding is a discovery of significance made following the completion of a scan. Findings result from the default out-of-the-box implementation of rule sets called Finding policies.

View all findings

To view different types of findings associated with all projects or packages in your tenant:

1. From the left sidebar, navigate to Findings.
2. The preset filters help you in locating the findings that matter most to you.
   • Choose Prioritized Findings to view a list of critical vulnerability findings in the last 30 days that have either a reachable function or a reachable dependency, are not test dependencies, and have an available fix.
   • Choose from a list of options under Code Dependenciesto view a list of Vulnerability, Operational, License Risk, or Malware findings.
   • Choose Secrets to find a list of findings related to exposed secrets.
   • Choose from a list of options under CI/CD to view findings related to GitHub Actions, CI/CD Tools, and RSPM.
   • Choose Containers to view container findings.
3. Use Saved Filters to create and save your frequently used searches, helping you save time.
4. Search for findings using basic filters.
5. Toggle Advanced and search for findings using advanced filters.
6. To apply exceptions to findings, select findings and click Actions > Add Exception.
7. To export findings, select the findings, and click Actions > Export Selected or Export All.

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View findings associated with a project

To view the findings associated with a project:

1. From the left sidebar, navigate to Projects.
2. Select the project for which you want to view the findings. The Findings page includes the list of findings specific to the project.
3. Review the list of findings. Click the finding to see its details.

View findings associated with a package

To view the findings associated with a package:

1. From the left sidebar, navigate to My Packages.
2. Select the package for which you want to view the findings. You might need to use the search bar to find your package.
3. Select the version for which you want to view the findings. The Findings page appears with the list of findings specific to the package version.
4. Review the list of findings. Click the finding to see its details.

Finding attributes

Finding attributes are characteristics or properties associated with each discovered issue or result obtained from a scan. These attributes could include the following details and metadata.

View GitHub Action findings

GitHub Actions is a CI/CD platform that allows you to automate your build, test, and deployment pipelines. You can create workflows that build and test pull requests to your repository, or deploy merged pull requests to production. To mitigate vulnerabilities within the supply chain, comprehensive visibility into GitHub Action workflows and their relationships in your repository is crucial. You can then proceed to identify and fix weak points within the system.

When you run an endorctl scan, it detects GitHub Action workflows used in your repository. It proceeds to scan all the repositories included in the detected workflows and creates findings. The GitHub Action is mapped as a package and discovers direct and transitive dependencies.

To view GitHub Action findings:

1. From the left sidebar, navigate to Projects.
2. Search for and select a project and select Findings.
3. Click CI Workflows to view GitHub Actions findings.

Search for findings using basic filters

Use the following basic filters to search for information in your findings.

• C - Findings with critical severity.
• H - Findings with high severity.
• M - Findings with medium severity.
• L - Findings with low severity.
• Category - Choose from CI/CD, Malware, license risks, operational risks, RSPM, secrets, security, supply chain, or vulnerability and view related findings.
• Hide Dismissed - Select to hide dismissed findings. You can view active findings without clutter.
• Attributes - Narrow down the list based on a range of factors such as, if a patch is available, if the vulnerable function is reachable, if the dependency is reachable, if the dependency originates from a current repository or a current tenant, is a test dependency, is a phantom dependency, or if the finding originates from itself, direct, or a transitive dependency.
• EPSS Probability - Choose the Exploit Prediction Scoring System (EPSS) score range.
• All Time - Choose a time range.
• Eco System - Choose from available options to filter based on a language or an ecosystem.

Search for findings using advanced filters

Use advanced filters to create powerful queries that drill deeper into the dataset to fetch results with a specific context.

The Advanced Filters use the GetFinding API call to fetch results.

The following table lists some example attributes, you can use in your custom API calls. See also example combinations below.

Examples

Show all findings of critical vulnerability and high severity that have a fix available, with a reachable function and EPSS score greater than 10%

spec.level in ["FINDING_LEVEL_CRITICAL","FINDING_LEVEL_HIGH"] and spec.finding_tags contains ["FINDING_TAGS_FIX_AVAILABLE"] and spec.finding_tags contains ["FINDING_TAGS_REACHABLE_FUNCTION"] and spec.finding_metadata.vulnerability.spec.epss_score.probability_score > 0.1

Show vulnerabilities for a specific project

spec.finding_categories contains ["FINDING_CATEGORY_VULNERABILITY"] and spec.project_uuid == "660e2bc48c7d4e60a5fc692f"

Show vulnerabilities for a specific language in a specific project

spec.finding_categories contains ["FINDING_CATEGORY_VULNERABILITY"] and spec.ecosystem in ["ECOSYSTEM_PYPI"] and spec.project_uuid == "660e2bc48c7d4e60a5fc692f"

Save search

You can save the advanced search filters that you created to fetch curated search results. You can easily access the target results and save time.

After typing in the query in the Advance Filter, enter a title in the field on the top right corner and click the Save icon or Save New Filter.

Saved queries are visible in the drop-down list.

Search for exceptions

Findings that are associated with exception policies do not trigger notifications.

To search for findings that are associated with exceptions,

1. From the left sidebar, navigate to Projects.
2. Search for and select a project, and select Findings.
3. From the DEPENDENCY tab, choose Basic Filters.
4. Click Exceptions and toggle Show Exceptions.
5. You can search for a specific exception policy name, reason, or expiry range to filter the relevant results.

Manage findings

See Finding policies for details on how to configure findings.

Act on findings

See Action policies for details on how to define and trigger workflows based on findings that meet a given set of criteria.

Export findings

Users can export finding details to a CSV file for offline analysis.

1. From the left sidebar, navigate to Projects.
2. Search for and select a project and select Findings.
3. Search for findings using advanced or basic filters.
4. Click Export Findings and select the fields that you want to include in the CSV file.
5. Click Export to CSV. The file is downloaded to your system.

Apply exception to findings

Add an exception policy to prevent this finding from triggering action policies in future scans.

1. From the left sidebar, navigate to Projects.
2. Search for and select a project, and select Findings.
3. Search for findings using advanced or basic filters.
4. Click a finding and from Actions choose Add Exception. See Create exception policy for details on how to create and apply exceptions.

2 - Dependencies

You can view project dependencies discovered in your tenant. Additionally, you can search for dependencies using specific criteria or apply predefined filters to find relevant results.

• From the left sidebar, navigate to Dependencies.
• Use the search bar to enter search criteria and to search for dependencies.
• Click Add Filter to filter out dependencies based on specific criteria.
• Click Export Dependencies to export the list of filtered dependencies in a CSV file for offline analysis. You can choose the columns to include in your CSV file from the following fields.
  • UUID of the project
  • Ecosystem of the project such as Maven, npm, PyPI, GO, Nuget, or more
  • Name of the dependency
  • Version of the dependency
  • Tags associated with the dependency
  • Reachability of the dependency
  • Is Direct which indicates if the dependency is direct or transitive
  • License information such as file, name, type, URL, and license text from the source code that aligns with a known license’s text
  • Endor scores such as activity, quality, popularity, and security scores
  • Package version name that indicates the fully qualified name of the root package version
  • Package version UUID that indicates the root package’s UUID
  • Project name that indicates the qualified package name of the root package
  • Project UUID that indicates the UUID of the root package

3 - Endor scores

Endor Labs collects and analyzes a large amount of information about open-source and customer packages and uses it to compute scores. Every open source and private package is scored across different dimensions that capture both security and operation aspects of a risk.

Types of scores

Scores provide a high-level, easy-to-understand metric of how well a package does based on factors such as security, activity, popularity, and code quality.

Endor Labs scores are categorized into:

• Security: Indicates the number of security-related issues a package may have such as known vulnerabilities, following security best practices when developing code, and the results of static code analysis. Packages with lower security scores can be expected to have many security-related issues when compared with packages with higher scores. See the factors affecting the security score for more details.
• Activity: Indicates the level of development activity for a package as observed through the source code management system. Packages with higher activity scores will be more active and presumably better maintained when compared to packages with a lower activity score. See the factors affecting the activity score for more details.
• Popularity: Indicates how widely a package is used in its ecosystem by tracking both source code management system metrics (for example, the number of stars in GitHub) as well as counting how many other packages import it. A package with a high popularity score indicates that it is used widely. See the factors affecting the popularity score for more details.
• Code Quality: Indicates how well the package complies with best practices for code development and includes the results of static code analysis of that package’s source code. A package with a higher quality score has fewer code issues. See the factors affecting the code quality score for more details.

Input data for score calculation

Endor Labs performs score computation for various entities such as:

• Repository score - A repository has a score that captures overall repository activity and properties that span multiple versions of the code.
• Repository version score - A repository version has a score that captures details that are specific to a version of the code.
• Package version score - A package version that captures the details that are specific to a package version.

The scoring algorithm considers the following input parameters while calculating the scores:

• Data from a version control system such as Git that provides information about files, versions and their contents.
• Data from a source code management system such as GitHub that provides information about the development activities on a project like issues, pull requests, and more.
• Data from Package managers that provide information about the properties of a package, for example, license, releases and metadata like the number of stars.
• Data from Vulnerabilities that provide information about known security issues in a package.
• Data from Static code analysis tools that provide information about specific issues in the source code of the package.

Score range

The scores for each category range between 0 and 10. For example, a score of 5 indicates inconclusive analysis and the package is neutral. A score higher than 5 indicates that the package mostly has positive factors while a score lower than 5 indicates negative factors. A score of 10 indicates that the package meets all the positive conditions, while a score of 0 indicates that a package meets all negative conditions.

3.1 - Activity score factors

Activity scores indicate the level of activity associated with a repository. Activity information is based on metadata gathered from a code hosting and version control system such as GitHub. Higher levels of activity can mean that the repository is well maintained and will continue to be in the future.

The following factors have a positive contribution to the activity score:

• Activity from corporate affiliated accounts that indicate that the project can have reliable backing and support.
• Activity from reputable accounts indicates that the repository is well-maintained. An account is considered reputable if it participates in multiple open-source projects and has a high rating on a source control system such as GitHub.
• Consistent and continuous commit activity over longer periods of time indicates that the project is active.
• The repository has frequent releases indicating a commitment to maintaining and supporting the codebase.
• Activity in the form of comments on issues, show there is engagement in the project.
• A high ratio of issues opened by external contributors indicates that the project is active.
• More issues being closed than opened indicates that the project is active.
• The repository keeps releasing updates to earlier release trains, this is a sign of a commitment to maintaining and supporting the users of the project.
• When a repository belongs to an organization there is a lower risk of it getting abandoned in the future
• Recent issue and commit activity means the project is active.
• Configuring topics is an indication that the repository is well-maintained

The following factors have a negative contribution to the activity score:

• Archived repositories are not active and have a low score.
• A high ratio of rejected pull requests indicates that the project may not be actively developed.
• The Lack of recent issue activity may indicate that the project is not actively used.
• Significantly more pull requests being submitted than merged indicates that the project may not be maintained.
• The repository does not have any recent releases and this could mean that it is not actively maintained.
• When a repository is personal there is a higher risk of it getting abandoned in the future
• If the majority of the repository’s activity comes from a tiny number of accounts, the project could be at risk if these accounts cannot continue their contributions.

3.2 - Popularity score factors

Popularity scores indicate how popular is the repository. Popularity information is based on metadata gathered from a code hosting and version control system such as GitHub. Popular repositories are more likely to be maintained.

The following factors have a positive contribution to the popularity score:

• Many reputable contributors affiliated with the project indicates that the project is reliable. An account is considered reputable if it participates in multiple open-source projects and has a high rating on GitHub.
• The project includes many stars indicating an interest in the project.
• Having subscribers indicates interest in the project.
• Includes many subscribers
• Includes a high number of dependent projects.
• Includes many forks.
• Some released artifacts of the repository are downloaded many times indicating the project is popular.

The following factors have a negative contribution to the activity score:

• Low fork count may indicate a lack of interest in the project.
• Few subscribers may mean a lack of interest in the project.
• Few stars may mean a lack of interest in the project.

3.3 - Security score factors

Security scores indicate the level of compliance with security best practices as well as vulnerability information for the repository that includes open and fixed vulnerabilities. Vulnerability information is based on OSV.dev data and Endor Lab’s vulnerability database.

The following factors have a positive contribution to the security score:

• Critical and high severity vulnerabilities were discovered in the past in the repository but have now been fixed. This indicates that the code base is properly maintained.
• A SECURITY.md file highlighting security-related information is a sign of repository maturity.
• A high volume of commits related to vulnerabilities may indicate that the project has many security issues but also that they are actively being addressed. A commit is considered vulnerability-related if it mentions a CVE in its commit message.
• No vulnerabilities ever discovered in a repository indicate that there are no known security issues in this codebase.
• Recently fixed vulnerabilities indicate that the repository has lower security risk and is well maintained.

The following factors have a negative contribution to the security score:

• This package has high activity from invalid accounts.
• This package calls any of the following sensitive APIs more often than an average package.
  • Access to environment information like environment variables, user and host names. Some of this information may be security sensitive, such as environment variables with API keys.
  • Read or write access to the file system. This can be dangerous in combination with user-provided input, for example,, lead to path traversal vulnerabilities.
  • Start of operating system processes. This can be dangerous in combination with user-provided input, as it can lead to command or parameter injection vulnerabilities.
  • Dynamic programming techniques like introspection, reflection or dynamic code execution through eval() type of functions or script engines.
  • Network functions, for example, to open connections or listen for incoming connection requests. This can be dangerous in combination with user-provided input, for example,, lead to data leakage or the load of data from untrusted sources.
  • Cryptographic or encoding/decoding functions. Depending on the specific functions used and the data being processed, export control regulations may apply to downstream users.
• This package contains code patterns or shows behaviors that are known to be used by malware. While this is not a guarantee that this package is malicious, a review of the related code is recommended.
• A high fraction of critical vulnerabilities among the discovered vulnerabilities indicates an elevated security risk and potentially systematic security issues with this codebase.
• A high fraction of high-fix priority vulnerabilities among the discovered vulnerabilities indicates an elevated security risk and that the repository needs immediate maintenance. A vulnerability is considered a high priority based on our analysis.
• A high fraction of high severity or critical vulnerabilities among the discovered vulnerabilities indicates an elevated security risk and potentially systematic security issues with this codebase.
• Taking more time to fix critical vulnerabilities discovered in a repository indicates a lack of regular maintenance. The analysis only considers vulnerabilities associated with this repository and not its dependencies.
• A high fraction of releases with high severity vulnerabilities indicates an elevated security risk and potentially systematic security issues with this codebase.
• The package has many unmerged vulnerability-related pull requests means that the project is not actively maintained and may have security issues.
• The repository includes recently discovered vulnerabilities indicating that the repository’s security risk is increasing.
• A high number of critical or unfixed vulnerabilities discovered in a repository indicates an elevated security risk and potentially systematic security issues with this codebase.

3.4 - Code quality score factors

Code quality scores provide a view of code quality and adherence to best practices in a repository. Code quality information is based on metadata gathered from a code hosting and version control system such as GitHub and from the source code in the repository.

The following factors have a positive contribution to the code quality score:

• Activity from bot accounts shows that the project is using automation for some development tasks
• The repository has reached 1.0 release status indicating the first major release milestone and is a sign of maturity
• The project includes test code
• Attaching labels to issues allows for better tracking of issue activity in the project
• The repository has multiple files that cover basic operational aspects of the project and this shows a strong emphasis on best practices
• A large fraction of the commits in this repository are verified; this shows that security best practices are followed
• Pull requests from dependency management bot accounts indicate that the project is using automation to keep its dependencies up to date
• Attaching labels to pull requests helps organize the development activity in the project
• Pull requests from bot accounts indicate that the project is using automation for development tasks
• A large faction of the commits in this repository is associated with a pull request; this shows that development best practices are followed
• The repository has released signed artifacts which is a sign of mature security operations
• The use of continuous integration is a sign of good developer practices
• Using GitHub templates to manage issues shows that the development work in the repository is well-organized
• The repository includes badges
• Displaying the Code Coverage badge means that the repository is using code coverage tools in its development process
• Displaying the Core Infra Best Practices badge means that the repository has met a number of best practices requirements
• The repository includes documentation making it easier to understand and use
• The repository has files that cover basic operational aspects of the project and this shows an emphasis on best practices
• The repository uses CI and a high fraction of commits pass the CI checks which is a sign of good code quality
• Displaying the OSSF scorecards badge means that the repository strives to meet the OSSF scorecard checks

The following factors have a negative contribution to the code quality score:

• This package has many instances of likely incorrect code that is associated with coding issues and potential bugs
• This package has many instances of questionable code warnings that are associated with coding issues and potential bugs
• This project has a high number of indirect dependencies compared to the number of direct dependencies; this additional code increases the cost of building the project and its supply chain risk.
• The repository has many major releases in a short amount of time, this is a sign of high churn and potential instability
• Packages where the package manager license information does not agree with the license information found in the code require additional review
• Packages with multiple licenses require extra effort to determine their exact license status
• Multiple unpinned dependencies can significantly increase the risk of a codebase since packages can be updated at any moment
• Many unreachable direct dependencies unnecessarily increase the size of the codebase and the cost of building it
• The project does not have an automated build system
• The repository does not have any of the files that typically explain the basic operational aspects of the project, this may be an indication that the project is not well-maintained
• Packages or source code without license information or a restrictive license can create operational risk
• This release is old and has been superseded by multiple newer releases, it should not be used
• The repository has releases that do not follow the SemVer standard, this goes against best practices
• When a repository contains binary files it is harder to analyze and assess its functionality and risks
• Lack of access to the source code of the project dramatically limits visibility in its quality and adherence to best practices
• The repository has an unusually fast first release

4 - Call graphs

Endor Labs has developed a systematic approach to conduct call graph analysis. Here is a structured overview:

• Scope Expansion: Traditional methods of static analysis are typically capable of analyzing a single project at a time. Endor Labs, however, has expanded its scope to include not only the client projects but also their dependencies, often comprising over 100 packages.

• Enhanced Dependency Analysis: Endor Labs employs static call graphs to conduct detailed dependency analysis, enabling a comprehensive understanding of how different external components interact within client projects. By leveraging these call graphs, Endor Labs aims to minimize false positives and more accurately identify the specific locations of problems in dependencies.

• Multiple Data Sources: To enrich the analysis, Endor Labs uses both source code and binary artifacts. This ensures swift results without a heavy reliance on test coverage.

• Benchmarking for Continuous Improvement: Endor Labs maintains accuracy and relevance by using dynamic call graphs internally to benchmark and refine static call graphs, thereby actively identifying and addressing gaps.

• Scalability: Addressing the challenge of scalability, Endor Labs generates call graphs not only for each project release but also for all its dependencies. This approach enables effective management of large projects with multiple versions, ensuring that the analysis is both relevant and applicable across the entire spectrum of client dependency sets.

For more information, see Visualizing the Impact of Call Graphs on Open Source Security.

Endor Labs UI provides visualizations of call graphs annotated with vulnerability data and simplified into informative call paths. This empowers developers to identify and address problematic invocations of vulnerable methods efficiently.

View call paths

The Endor Labs UI enables you to view call paths, representing the sequences of functions invoked during program execution.

To view call paths in the Endor Labs UI:

1. Navigate to the left sidebar and click My Packages.

2. Expand the package of interest and select a version by clicking the respective row.

3. In the findings list view, expand a specific finding, and click See Call Paths.