Vulnerability Spoiler Alert Action

Know about security patches before the CVE drops.

A reusable GitHub Action that monitors open-source repositories and uses Claude AI to detect when commits are patching security vulnerabilities - often before a CVE is even assigned.

Features

• AI-Powered Detection - Uses Claude to analyze commit diffs for security patterns
• Proof of Concept Required - Only alerts when a concrete exploit can be demonstrated
• PR Context Aware - Analyzes PR descriptions and labels for security indicators
• Automatic Issue Creation - Creates detailed issues with vulnerability analysis
• State Management - Tracks analyzed commits to avoid duplicates
• Configurable - Choose which repositories to monitor, where to create issues, and more

Quick Start

name: Vulnerability Monitor

on:
  schedule:
    - cron: '0 */6 * * *'  # Every 6 hours
  workflow_dispatch:

permissions:
  contents: write
  issues: write

jobs:
  monitor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: spaceraccoon/vulnerability-spoiler-alert-action@v1
        with:
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
          repositories: |
            [
              {"owner": "expressjs", "repo": "express"},
              {"owner": "lodash", "repo": "lodash"}
            ]

      - name: Commit state changes
        run: |
          git config user.name "github-actions[bot]"
          git config user.email "github-actions[bot]@users.noreply.github.com"
          git add .vulnerability-spoiler-state.json
          git diff --staged --quiet || git commit -m "Update vulnerability monitor state"
          git push

Inputs

Input	Required	Default	Description
anthropic-api-key	Yes	-	Anthropic API key for Claude
github-token	No	${{ github.token }}	GitHub token for API access
repositories	Yes	-	JSON array of repos to monitor
state-file	No	.vulnerability-spoiler-state.json	Path to state file
create-issues	No	true	Whether to create GitHub issues
issue-repo	No	Current repo	Where to create issues (owner/repo)
model	No	claude-sonnet-4-20250514	Claude model to use
max-commits	No	50	Max commits to analyze per repo per run

Outputs

Output	Description
vulnerabilities-found	Number of vulnerabilities detected
issues-created	Number of issues created
analyzed-commits	Total commits analyzed
results	JSON array of detected vulnerabilities

Usage Examples

Basic Usage

- uses: spaceraccoon/vulnerability-spoiler-alert-action@v1
  with:
    anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
    repositories: '[{"owner": "facebook", "repo": "react"}]'

Monitor Multiple Repositories

- uses: spaceraccoon/vulnerability-spoiler-alert-action@v1
  with:
    anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
    repositories: |
      [
        {"owner": "expressjs", "repo": "express"},
        {"owner": "lodash", "repo": "lodash"},
        {"owner": "axios", "repo": "axios"},
        {"owner": "chalk", "repo": "chalk"}
      ]

Create Issues in a Different Repository

- uses: spaceraccoon/vulnerability-spoiler-alert-action@v1
  with:
    anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
    repositories: '[{"owner": "expressjs", "repo": "express"}]'
    issue-repo: 'my-org/security-alerts'

Detection Only (No Issues)

- uses: spaceraccoon/vulnerability-spoiler-alert-action@v1
  id: scan
  with:
    anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
    repositories: '[{"owner": "expressjs", "repo": "express"}]'
    create-issues: 'false'

- name: Process results
  if: steps.scan.outputs.vulnerabilities-found > 0
  run: |
    echo "Found ${{ steps.scan.outputs.vulnerabilities-found }} vulnerabilities!"
    echo "${{ steps.scan.outputs.results }}" | jq .

Send to Slack

- uses: spaceraccoon/vulnerability-spoiler-alert-action@v1
  id: scan
  with:
    anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
    repositories: '[{"owner": "expressjs", "repo": "express"}]'
    create-issues: 'false'

- name: Notify Slack
  if: steps.scan.outputs.vulnerabilities-found > 0
  uses: slackapi/slack-github-action@v1
  with:
    payload: |
      {
        "text": "🚨 Detected ${{ steps.scan.outputs.vulnerabilities-found }} potential vulnerabilities!"
      }
  env:
    SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK }}

How Detection Works

1. Fetches new commits from each monitored repository since the last run
2. Retrieves PR context including title, description, and labels
3. Analyzes with Claude AI using a carefully crafted prompt that:
   • Looks for security-relevant code patterns
   • Requires a concrete proof of concept exploit
   • Classifies severity based on impact
4. Creates issues with full vulnerability details

What Gets Flagged

The action only flags commits where Claude can demonstrate a concrete exploit:

• Input validation/sanitization fixes
• Authentication/authorization patches
• Injection vulnerability fixes (SQL, XSS, Command, etc.)
• Path traversal protections
• Memory safety improvements

What Gets Ignored

• General code quality improvements
• Defensive coding without security impact
• Performance optimizations
• Refactoring

State Management

The action tracks which commits have been analyzed using a state file. This file should be committed back to your repository to persist across runs.

- name: Commit state changes
  run: |
    git config user.name "github-actions[bot]"
    git config user.email "github-actions[bot]@users.noreply.github.com"
    git add .vulnerability-spoiler-state.json
    git diff --staged --quiet || git commit -m "Update vulnerability monitor state"
    git push

Permissions

The action requires these permissions:

permissions:
  contents: write  # To commit state file
  issues: write    # To create issues

Setup

1. Get an API key from console.anthropic.com
2. Add it as a repository secret named ANTHROPIC_API_KEY
3. Create a workflow file as shown in the examples above
4. Enable the workflow

Security Considerations

Prompt Injection Risk

This action sends commit diffs, commit messages, and pull request metadata from monitored repositories to an LLM for analysis. Because these inputs are controlled by the authors of the monitored repositories, they are inherently attacker-controllable. A malicious actor can craft commit messages, PR descriptions, or code diffs that contain adversarial instructions designed to manipulate the LLM's output. This is known as prompt injection and it cannot be fully prevented by the action itself.

Prompt injection could be used to:

• Suppress real detections — instructing the model to always return isVulnerabilityPatch: false, causing the action to silently miss actual vulnerability patches.
• Generate false positives — forcing the model to flag benign commits as vulnerabilities with fabricated descriptions and proof-of-concept exploits.
• Influence issue content — since the model's output is used to create GitHub issues, a manipulated response could include misleading or socially-engineered content in your issue tracker.

Recommended Mitigations

1. Treat all results as advisory. Never take automated action (e.g., blocking deployments, sending public notifications) based solely on this action's output without human review.
2. Restrict the issue repository. Use a private or internal repository for created issues (issue-repo input) so that injected content is not publicly visible.
3. Use create-issues: false for untrusted sources. When monitoring repositories you do not control, disable automatic issue creation and review the results output programmatically or manually instead.
4. Apply least-privilege tokens. Use a GitHub token scoped to only the permissions the action needs. Do not use tokens with broad org-level access.
5. Review issues before acting. Any vulnerability details, proof-of-concept exploits, or severity ratings in created issues originate from LLM analysis of external data and should be independently verified.

Limitations

• Only analyzes public repositories (or private repos your token can access)
• Large diffs are truncated to fit API limits
• AI analysis may have false positives/negatives — all results should be treated as advisory and verified by a human (see Security Considerations)
• Rate limited by GitHub and Anthropic APIs

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Disclaimer

This tool is for defensive security research only. Always follow responsible disclosure practices.