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The best way to prioritize secret scanning alerts is to filter by active secrets — these are secrets GitHub has confirmed are still valid and could be exploited. This allows security teams to focus on high-risk exposures that require immediate attention.

Sorting by time or filtering by custom patterns won’t help with risk prioritization directly.

You must enable secret scanning before defining custom patterns. Secret scanning provides the foundational capability for detecting exposed credentials, and custom patterns build upon that by allowing organizations to specify their own regex-based patterns for secrets unique to their environment.

Without enabling secret scanning, GitHub will not process or apply custom patterns.

The GitHub Code Scanning API includes endpoints that allow you to:

List alerts for a repository (filtered by branch, state, or tool) — useful for monitoring security over time.

Get a single alert by its ID to inspect its metadata, status, and locations in the code.

However, GitHub does not support modifying the severity of alerts via API — severity is defined by the scanning tool (e.g., CodeQL). Likewise, alerts cannot be deleted via the API; they are resolved by fixing the code or dismissing them manually.

When dealing with a data flow issue in a code scanning alert, clicking on "Show paths" provides a detailed view of the data's journey through the code. This includes the source of the data, the path it takes, and where it ends up (the sink). This information is crucial for understanding how untrusted data might reach sensitive parts of your application and helps in identifying where to implement proper validation or sanitization.​

When a vulnerability is detected, GitHub shows a warning that includes a brief description of the vulnerability. This typically covers the name of the CVE (if available), a short summary of the issue, severity level, and potential impact. The message also links to additional advisory data from the GitHub Advisory Database.

This helps developers understand the context and urgency of the vulnerability before applying the fix.

For compiled languages, CodeQL performs extraction by monitoring the normal build process. This means it watches your usual build commands (like make, javac, or dotnet build) and extracts the relevant data from the actual build steps being executed. CodeQL uses this information to construct a semantic database of the application.

This approach ensures that CodeQL captures a precise, real-world representation of the code and its behavior as it is compiled, including platform-specific configurations or conditional logic used during build.

A Dependabot alert is marked as resolved only after the related pull request is merged into the repository. This indicates that the vulnerable dependency has been officially replaced with a secure version in the active codebase.

Simply generating a PR or passing checks does not change the alert status; merging is the key step.

The dependency graph in a repository is built by parsing manifest and lock files (like package.json, pom.xml, requirements.txt). It helps GitHub detect dependencies and cross-reference them with known vulnerability databases for alerting.

It is specific to each repository and does not show org-wide or cross-repo summaries.

Dependabot alerts are generated as soon as GitHub detects a known vulnerability in one of your dependencies. GitHub does this by analyzing your repository’s dependency graph and matching it against vulnerabilities listed in the GitHub Advisory Database. Once a match is found, the system raises an alert automatically without waiting for a PR or manual action.

This allows organizations to proactively mitigate vulnerabilities as early as possible, based on real-time detection.

By default, no repositories receive Dependabot alerts unless configuration is explicitly enabled. GitHub does not enable Dependabot alerts automatically for any repositories unless:

The feature is turned on manually

It's configured at the organization or enterprise level via security policies

This includes public, private, and enterprise-owned repositories — manual activation is required.

The correct place to look is the SECURITY.md file. This file provides contributors and security researchers with instructions on how to responsibly report vulnerabilities. It may include contact methods, preferred communication channels (e.g., security team email), and disclosure guidelines.

This file is considered a GitHub best practice and, when present, activates a “Report a vulnerability” button in the repository’s Security tab.

Comprehensive and Detailed Explanation:

To receive Dependabot alert notifications for a repository, you can utilize the following Watch settings:

Custom setting: Allows you to tailor your notifications, enabling you to subscribe specifically to security alerts, including those from Dependabot.​

All Activity setting: Subscribes you to all notifications for the repository, encompassing issues, pull requests, and security alerts like those from Dependabot.​

The Participating and @mentions setting limits notifications to conversations you're directly involved in or mentioned, which may not include security alerts. The Ignore setting unsubscribes you from all notifications, including critical security alerts.​

GitHub Docs

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GitHub Docs

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The best proactive control is push protection. It scans for secrets during a git push and blocks the commit before it enters the repository.

Other options (like CODEOWNERS or security managers) help with oversight but do not prevent secret leaks.

Making a repo public would increase the risk, not reduce it.

Validity checks — where GitHub verifies if a secret is still active — are available for partner patterns only. These are secrets issued by GitHub's trusted partners (like AWS, Slack, etc.) and have APIs for GitHub to validate token activity status.

Custom patterns and high entropy patterns do not support automated validity checks.

Query suite definitions in CodeQL are stored using the .qls file extension. A query suite defines a collection of queries to be run during an analysis and allows for grouping them based on categories like language, security relevance, or custom filters.

In contrast:

.ql files are individual queries.

.qll files are libraries used by .ql queries.

.yml is used for workflows, not query suites.

Comprehensive and Detailed Explanation:

Dependency review is triggered by specific events in GitHub workflows:​

pull_request: When a pull request is opened, synchronized, or reopened, GitHub can analyze the changes in dependencies and provide a dependency review.​

workflow_dispatch: This manual trigger allows users to initiate workflows, including those that perform dependency reviews.​

The trigger and commit options are not recognized GitHub Actions events and would not initiate a dependency review.​

To detect and block vulnerable dependencies before merge, developers should use the Dependency Review GitHub Action in their pull request workflows. It scans all proposed dependency changes and flags any packages with known vulnerabilities.

This is a preventative measure during development, unlike Dependabot, which reacts after the fact.

If a secret is intentionally used in a test environment and poses no real-world security risk, you may close the alert with the reason "used in tests". This helps reduce noise and clarify that the alert was reviewed and accepted as non-critical.

Just being in a test file isn't enough unless its purpose is purely for testing.

In a dependabot.yml configuration file, package-ecosystem is a required key. It defines the package manager being used in that update configuration (e.g., npm, pip, maven, etc.).

Without this key, Dependabot cannot determine how to analyze or update dependencies. Other keys like rebase-strategy or commit-message are optional and used for customizing behavior.

To exclude .txt and .md files from triggering workflows on pull requests to the main branch:

on: defines the event (e.g., pull_request)

pull_request: is the trigger

paths-ignore: is the key used to ignore file patterns

Example YAML:

yaml

CopyEdit

on:

pull_request:

branches:

- main

paths-ignore:

- '*.md'

- '*.txt'

Using paths: would include only specific files instead — not exclude. paths-ignore: is correct here.

When you identify that a code scanning alert is a false positive—such as when your code uses a custom sanitization method not recognized by the analysis—you should dismiss the alert with the reason "false positive." This action helps improve the accuracy of future analyses and maintains the relevance of your security alerts.​

As per GitHub's documentation:​

"If you dismiss a CodeQL alert as a false positive result, for example because the code uses a sanitization library that isn't supported, consider contributing to the CodeQL repository and improving the analysis."​

By dismissing the alert appropriately, you ensure that your codebase's security alerts remain actionable and relevant.

Comprehensive and Detailed Explanation:

By default, users with write access to a repository have the ability to merge pull requests, including those created by Dependabot for security updates. This access level allows contributors to manage and integrate changes, ensuring that vulnerabilities are addressed promptly.​

Users with only read access cannot merge pull requests, and enterprise administrators do not automatically have merge rights unless they have write or higher permissions on the specific repository.​