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Rule Evaluation in Depth

When evaluating a rule, Minder executes the rule in multiple phases. This page contains detailed documentation about the execution of each phase. Minder performs rule evaluation across all relevant policies when it detects that an entity has changed.

Ingest​

The ingest phase in Minder is where the system gathers and processes information about entities before policy evaluation. Here's a breakdown:

Input Sources:​

The primary input is the Entity object representing the entity that the policy rules apply to. The Entity objects have specific properties based on the type of entity:

Ingest Types and Their Outputs​

Each rule can specify the type of data which should be ingested. Minder works to cache ingested data across all rule evaluations, so multiple rules which ingest the same data should only need to fetch it once. The ingestion types produce the following data for rule evaluatino:

  1. REST Ingest (rest)

    Entity Types: all types

    Data Content: Calls a REST endpoint specified in the ingest type, and returns a parsed JSON object from the response

  2. Git Ingest (git)

    Entity_Types: PRs and repos

    Data Content: This is only useful with the Rego evaluation engine. It provides a filesystem view of the current repository contents; this is required for using the Rego fs methods.

  3. Dependency Ingest (deps)

    Entity_Types: PRs and repos

    Data Content: Ingests the set of libraries used by recognized package managers, using the osv-scalibr library. When used to ingest a pull request, the deps ingest can provide new, new_and_updated, or all dependencies from the repository after the proposed change.

  4. Artifact Ingest (artifact)

    Entity_Types: artifact

    Data Content: Fetches details about about container build attestations, including signature data, branch and repository information, and GitHub runner environment.

  5. Diff Ingest (diff)

    Entity_Types: PR only

    Data Content: Provides a set of diffs describing the changes proposed in the PR. This works with the homoglyphs and vulncheck evaluators. The diff can evaluate full (all files) and dep (dependency changes) for these evaluators.

Evaluate​

The evaluation phase in Minder evaluates the ingested data according to the specified rule type and parameters. Here's how it works:

Evaluation Process​

Minder takes the following inputs for rule evaluation:

  • Ingested data from the ingest phase
  • Rule profile parameters from the params and def clauses.
  • Entity properties as string key-value pairs (e.g., github/default_branch=main)

The system then evaluates the rule using one of these engines:

  1. Rego Evaluation (rego)

    See the documentation on writing rules for Rego for more details on the Rego evaluation engine. This engine has the most flexibility and supports sophisticated logic, but also has a higher learning curve.

    • Uses Open Policy Agent (OPA) Rego language
    • Supports two evaluation modes:
      • deny-by-default: Rule fails unless allow := true is set
      • constraints: Rule fails if the violations array contains any entries. violations elements must be an object which contains a msg key, e.g. violations := [{"msg": "..."}, {"msg": "..."}]
    • Produces output via the output property in the Rego code

  2. JQ Evaluation (jq)

    See the documentation on writing rules in JQ for more details on the JQ evaluation engine. This engine allows you to easily extract structured data and compare it with known or expected values.

    • Processes JSON data using jq queries
    • Returns evaluation results based on the query output

  3. Vulncheck Evaluation (vulncheck)

    See the documentation on checking vulnerabilities for more details on the vulncheck engine. The vulncheck engine is a custom-coded engine which evaluates software dependencies against the Open Source Vulnerabilities (OSV) database. With improvements to the deps ingestion type, this evaluator can largely be replaced with rego evaluation and data sources.

    • Requires the use of the diff ingestion type with specified ecosystems, and only operates in a pull_request context
    • Uses a custom parser for dependency files, and can suggest line-level comments on vulnerable libraries
    • Immediately applies comments highlighting new vulnerable libraries when evaluated against a pull request.

  4. Homoglyph Evaluation (homoglyph)

    This rule evaluation engine attempts to detect malicious Unicode sequences as described in the Trojan Source attack. The homoglyphs evaluator can detect two different types of attack:

    • invisible_characters: using byte order characters to attempt to confusingly display characters and comments
    • mixed_scripts: mixing identical-appearance characters from different alphabets (for example, to use two variables with seemingly-identical names)

    The homoglyph evaluator only operates in a pull_request context using a full diff.

The evaluation engine determines if the rule passes, fails or should be skipped (for example, because the resource is not the correct type). If rule passes or is skipped, the entity is compliant with the profile, and no further evaluation is done. If the rule evaluation fails and remediation or alerting is enabled, output data from the rule evalution may be passed to the following steps.

Remediate​

A rule can optionally define a remediation action to take when rule evaluation fails. The goal of remediation is to change the state of the entity so that it it now passes the rule evaluation. The remediation phase can optionally use inputs from the evaluation phase to determine what actions to take.

Remediation Inputs​

Remediation actions have access to:

  • The Entity object representing the resource being evaluated
  • The Profile object containing rule parameters and definitions
  • For pull request and REST call remediations, output from the rule evaluation is available in EvalResultOutput.

Remediation Types​

Minder supports three remediation actions:

  1. Pull Request (pull_request)

    The Pull Request remediation type creates or updates GitHub pull requests to implement fixes automatically. The pull requests are authored by the Minder bot, and will need to be reviewed and merged by the project maintainers.

    Pull requests support the following content modification types:

    • minder.content: define a list of file paths and contents which should be updated. Currently only supports the replace action on files. Both path and content may use Go templates to parameterize their outputs
    • minder.actions.replace_tags_with_sha: uses the Stacklok/frizbee library to resolve Git and OCI tag references to SHA digests where detected in the repository. The only parameter to this action is a list of resources which should not be resolved to digests (implicitly trusting the release process for the resource)
    • minder.yq.evaluate: applies a yq expression to files selected by a list of patterns. Each element in the patterns list is represented as: {"type": "glob", "pattern": "file/path/*"}. glob is currently the only supported pattern type. This action supports using Go templates in the expression but not in the pattern selection.

    If the content modification produces a diff in the repository, Minder will open and manage a pull request against the branch used in the git ingest, or the default branch if a different ingestion was used. The pull request includes the following fields which support Go templates:

    • title: PR title
    • body: PR description

    The following data is available to fill in template contents in title, body, and for the minder.content action:

    • Entity contains the same entity information available during rule evaluation
    • Profile contains the profile data supplied in the def field
    • Params contains the profile data supplied in the params field
    • EvalResultOutput contains the output data from the rule evaluation step

  2. REST Call (rest)

    The REST remediation calls the specified endpoint using the defined HTTP method, passing the supplied body. Currently, headers are not supported.

    endpoint, method and body support Go template parameters, with the following data:

    • Entity contains the same entity information available during rule evaluation
    • Profile contains the profile data supplied in the def field
    • Params contains the profile data supplied in the params field
    • EvalResultOutput contains the output data from the rule evaluation step

  3. GitHub Branch Protection (gh_branch_protect)

    The branch protection remediation takes a Go-templated JSON object in patch to merge with the existing protection branch protection settings. Branch protection has been implemented as a special remediation due to the peculiarities of the GitHub branch protection API. Repository rulesets are a newer feature which avoids many of these peculiarities.

    The patch string supports Go template parameters and needs to output a JSON object which will be merged with the existing branch protection settings. The following data is available within the Go template context:

    • Entity contains the same entity information available during rule evaluation
    • Profile contains the profile data supplied in the def field
    • Params contains the profile data supplied in the params field

API-driven remediations (rest and gh_branch_protect) will generally take effect immediately on the targeted entity; pull_request remediations will need to be merged before they take effect. Minder will ensure that at most one pull request is open at a time for a particular rule applied to a specific entity.

Alert Types​

When remediation has completed, Minder will execute any alerts defined for the rule type. Minder currently defines two alert types, which operate similarly to the remediation actions except that alerts do not directly attempt to fix the detected problem, but rather notify human maintainers to allow them to evaluate the solution.

Alert Inputs​

Because alerts are primarily indended to drive later human behavior, they have limited processing functionality. Alerts have access to:

  • Whether a remediation rule is defined for security_advisory alerts
  • Output from the rule evaluation as EvalResultOutput for pull_request_comments

Alert Types​

  1. Pull Request Comments (pull_request_comment)

    Instructs Minder to comment on new and updated pull requests. Minder will ensure that each PR in a repository has at most one comment per rule type evaluated, and will delete the PR comment if the pull request rule evaluation succeeds in the future (for example, because the flagged issue was addressed).

    Pull request comments have a single review_message parameter, which contains a Go templated markdown string with the following contents from the rule evaluation:

    • EvalErrorDetails contains any detailed error messages from the rule evaluation execution
    • EvalResultOutput contains the output data from the rule evaluation step

    The pull_request_comment alert type is only valid on pull request entities.

  2. GitHub Security Alerts (security_alert)

    This rule instructs Minder to create a private security vulnerability report through the GitHub API, assuming that private vulnerability reporting is enabled for the repository. These reports require administrator permission to view. When the security alert is no longer active because the rule evaluation passes or is skipped, Minder will automatically close the security vulnerability.

    The contents of the security advisory are currently hard-coded, and include the following details:

    • Rule evaluation error message
    • Repository name
    • Profile and rule name which was
    • Rule severity from the rule type definition
    • Any [guidance] content from the rule type definition