gh-bump

AI-powered Dependabot security fix agent. Uses an LLM to analyze your code and fix dependency vulnerabilities — no hardcoded patterns, works with any ecosystem. Runs as a CLI or GitHub Action.

Use at your own risk. Always review generated PRs before merging.

How it works

1. Fetches open Dependabot alerts via GitHub API
2. Reads your manifest files (package.json, pyproject.toml, go.mod, etc.) through the GitHub Contents API
3. Sends the alert details + file contents to an LLM, which determines the minimal fix
4. Commits the fix via the Git tree/blob API (no clone needed)
5. Creates a PR with an AI-generated description
6. Optionally merges, tags, and creates a release

Because the fix logic is driven by an LLM rather than hardcoded patterns, gh-bump works with any ecosystem — npm, pip, Go, Rust, Ruby, PHP, Maven, NuGet, and anything else the LLM understands.

--full-run is a separate local npm release workflow. It checks the local package, updates the lockfile, bumps the patch version, runs available verification scripts, and publishes only when --publish is also set.

Architecture

┌──────────────────────────────────────────────────────────────────┐
│ Entrypoint (CLI or GitHub Action)                                │
│  src/cli.ts   — parses flags, resolves env vars                  │
│  src/action.ts — reads Action inputs via @actions/core           │
└──────────────┬───────────────────────────────────────────────────┘
               │  Config
               ▼
┌──────────────────────────────────────────────────────────────────┐
│ Agent Loop  (src/core/agent.ts)                                  │
│  1. Fetch alerts                                                 │
│  2. Group by manifest                                            │
│  3. For each group → call LLM with tools → parse FixPlan         │
│  4. Commit files, create PR, optionally merge/tag/release        │
└────┬───────────────┬─────────────────────┬───────────────────────┘
     │               │                     │
     ▼               ▼                     ▼
┌──────────┐  ┌────────────┐  ┌────────────────────────────────────┐
│ GitHub   │  │ LLM Client │  │ Prompts (src/core/prompts.ts)      │
│ Client   │  │ (llm.ts)   │  │  SYSTEM_PROMPT — agent persona     │
│ (github  │  │  Vercel AI │  │  buildAlertPrompt() — alert detail │
│  .ts)    │  │  SDK       │  └────────────────────────────────────┘
└──────────┘  └────────────┘

Step-by-step Execution

1. Fetch Dependabot Alerts

GitHubClient.listAlerts() calls GET /repos/{owner}/{repo}/dependabot/alerts with state=open and an optional severity filter. It paginates through all results.

const alerts = await gh.listAlerts(repo, config.severity);

2. Group Alerts by Manifest

Alerts are grouped by dependency.manifest_path (e.g. package.json, requirements.txt). Each group becomes one PR — this avoids conflicts from parallel file edits.

const groups = groupAlertsByManifest(alertsToProcess);
// Map<string, DependabotAlert[]>
// e.g. { "package.json": [alert1, alert2], "backend/requirements.txt": [alert3] }

3. Build Prompt + Call LLM

For each manifest group, the agent:

1. Builds a prompt with structured alert details (GHSA ID, severity, package name, vulnerable range, patched version, manifest path).
2. Gives the LLM two tools so it can explore the repo before proposing a fix:
   • read_file — reads any file from the repo via the Contents API.
   • list_directory — lists directory entries to discover project structure.
3. Calls generateText() from the Vercel AI SDK. The SDK handles the tool-use loop automatically — the LLM can call read_file / list_directory multiple times before producing its final answer.

const response = await llm.call({
  system: SYSTEM_PROMPT,   // agent persona + JSON output format
  prompt,                  // structured alert details
  tools,                   // { read_file, list_directory }
});

The LLM returns a JSON FixPlan:

{
  "files": [
    { "path": "package.json", "content": "...full updated content..." }
  ],
  "prTitle": "fix(deps): upgrade lodash to 4.17.21 (CVE-2021-23337)",
  "prBody": "## Security Fix\n\nUpgrades lodash from ...",
  "commitMessage": "fix(deps): upgrade lodash to 4.17.21"
}

4. Commit Without Cloning

gh-bump never clones the repo. It uses the Git Data API (tree/blob) to create commits server-side:

createBlob()  →  for each file, upload content as a base64 blob
createTree()  →  new tree referencing the blobs + the parent tree
createCommit()→  commit pointing to the new tree
updateRef()   →  fast-forward the branch ref to the new commit

This is handled in GitHubClient.commitFiles().

5. Create PR

GitHubClient.createPR() opens a pull request from the fix branch into the default branch, using the LLM-generated title and body.

6. Merge / Tag / Release (optional)

If --merge (or --release / --tag, which imply merge):

• Merge: GitHubClient.mergePR() with the configured method (squash/merge/rebase).
• Tag: Extracts a semver version from the PR title and creates a lightweight git tag.
• Release: Creates a GitHub release with auto-generated release notes.

LLM Providers

The LLM layer (src/core/llm.ts) is provider-agnostic via the Vercel AI SDK:

Provider	Default Model	API Key
github	gpt-4o	GITHUB_TOKEN (no extra key)
openai	gpt-4o	OPENAI_API_KEY or AI_API_KEY
anthropic	claude-sonnet-4-20250514	ANTHROPIC_API_KEY or AI_API_KEY

The github provider hits the GitHub Models endpoint (models.inference.ai.azure.com) using the same GITHUB_TOKEN you already need for the API.

Key Design Decisions

• No repo clone. Everything happens through the GitHub API — file reads, commits, PRs. Fast, no git binary needed.
• Manifest-only fixes. Updates version constraints in manifest files but NOT lockfiles. Lockfile regeneration is left to CI or Dependabot.
• npm transitive safety. For package.json fixes, gh-bump rejects plans that add new direct dependencies and prefers existing direct dependency updates or package-manager overrides for transitive alerts.
• One PR per manifest. Alerts on the same manifest are batched into a single PR to avoid merge conflicts.
• Dry-run first. --dry-run shows what the agent would do without creating branches, commits, or PRs, including dependency version changes and override changes when package.json is involved.
• Cost guardrails. --max-alerts (default 10) and --max-llm-calls (default 20) cap how much work and spend a single run can do.

Build System

esbuild bundles the TypeScript source into two ESM entrypoints:

src/cli.ts    →  dist/cli.js      (CLI binary)
src/action.ts →  dist/action.js   (GitHub Action)

Both bundles include a createRequire banner for CJS module compatibility (some dependencies use require).

npm run build         # bundle with esbuild
npm run build:check   # type-check only (tsc --noEmit)

Local Full Run

Use --full-run from an npm package root when you want a local release preparation workflow instead of Dependabot alert fixing.

# Preview the local release workflow without running shell commands
gh-bump --full-run --dry-run

# Update package-lock.json, bump patch version, and run available checks
gh-bump --full-run

# Run the same workflow and publish to npm
gh-bump --full-run --publish

Without --publish, --full-run explicitly skips npm publish. Execute mode requires a clean git worktree before mutating files. npm authentication is checked only when --publish is set.

Requirements

• Node.js 20+
• A GitHub token with repo + security_events scope
• An AI API key (OpenAI or Anthropic)

CLI Usage

# Install
npm install -g gh-bump

# Or run directly
npx gh-bump --repo=owner/repo --dry-run

Options

Option	Description
--repo=OWNER/REPO	Target repository (required)
--full-run	Run local npm update, patch version, checks, and optional publish
--publish	Publish to npm during --full-run
--severity=LEVEL	Minimum severity: critical, high, medium, low
--dry-run	Preview changes without applying them
--merge	Merge PR after creation
--merge-method=METHOD	Merge method: squash (default), merge, rebase
--release	Create GitHub release after merge (implies --merge)
--tag	Create git tag after merge (implies --merge)
--provider=PROVIDER	AI provider: github (default), openai, anthropic
--model=MODEL	AI model override
--max-alerts=N	Max alerts to process per run (default: 10)
--max-llm-calls=N	Max LLM calls per run, cost guardrail (default: 20)
--verbose	Enable debug output

Environment variables

Variable	Description
GITHUB_TOKEN	GitHub token (required)
AI_API_KEY	AI provider API key (not needed for github provider)
OPENAI_API_KEY	OpenAI API key (fallback if AI_API_KEY not set)
ANTHROPIC_API_KEY	Anthropic API key (fallback if AI_API_KEY not set)
GH_BUMP_LLM_RESPONSE_FILE	Optional debug path for writing the raw LLM response

Examples

# Local release dry-run: see every planned phase and command
gh-bump --full-run --dry-run

# Local release without publishing
gh-bump --full-run

# Local release and npm publish
gh-bump --full-run --publish

# Dry-run: see what would be fixed
gh-bump --repo=myorg/myapp --dry-run

# Use gh cli to set the token
GITHUB_TOKEN="$(gh auth token)" \
  gh-bump --repo=myorg/myapp --dry-run

# Dry-run a larger batch and inspect the raw LLM response if needed
GH_BUMP_LLM_RESPONSE_FILE=/tmp/gh-bump-response.json \
  gh-bump --repo=myorg/myapp --dry-run --max-alerts=50

# Fix critical alerts and merge
gh-bump --repo=myorg/myapp --severity=critical --merge

# Fix, merge, tag, and release
gh-bump --repo=myorg/myapp --merge --release --tag

# Use GitHub Models (no separate AI key needed)
gh-bump --repo=myorg/myapp --provider=github

# Use Anthropic instead of OpenAI
gh-bump --repo=myorg/myapp --provider=anthropic --model=claude-sonnet-4-20250514

GitHub Action

Use gh-bump as a scheduled GitHub Action to automatically fix Dependabot alerts:

name: Fix Dependabot Alerts
on:
  schedule:
    - cron: "0 6 * * 1"  # Every Monday at 06:00 UTC
  workflow_dispatch:

permissions:
  contents: write
  pull-requests: write
  security-events: read

jobs:
  fix-alerts:
    runs-on: ubuntu-latest
    steps:
      - name: Fix Dependabot alerts
        uses: soderlind/gh-bump@main
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          ai-provider: github
          severity: high
          merge: true

Action inputs

Input	Required	Default	Description
github-token	yes	${{ github.token }}	GitHub token
ai-provider	no	github	AI provider: github, openai, or anthropic
ai-api-key	no	—	API key (not needed for github provider)
ai-model	no	—	Model name override
repo	no	current repo	Target repository
severity	no	all	Minimum severity
dry-run	no	false	Preview mode
merge	no	false	Merge PRs after creation
merge-method	no	squash	Merge method
release	no	false	Create release (implies merge)
tag	no	false	Create tag (implies merge)
max-alerts	no	10	Max alerts per run
max-llm-calls	no	20	Cost guardrail

Action outputs

Output	Description
prs-created	Number of PRs created
alerts-fixed	Number of alerts processed
pr-urls	JSON array of PR URLs
summary	Human-readable summary

Dry-run Output

--dry-run performs the same alert fetching, manifest prefetching, LLM call, FixPlan parsing, and deterministic validation as a real run. It stops before creating branches, commits, or pull requests.

For package.json changes, the dry-run output includes a compact plan summary:

[DRY-RUN]   Would update: package.json
[DRY-RUN]   package.json: devDependencies changed: electron ^40.0.0 -> ^40.8.5, vite ^7.3.1 -> ^7.3.2
[DRY-RUN]   package.json: overrides added: axios 1.15.0, follow-redirects 1.16.0, lodash 4.18.0
[DRY-RUN]   PR title: fix(deps): update vulnerable npm dependencies

If an LLM proposes unsafe package.json changes, such as adding new direct npm dependencies for transitive alerts or editing lockfiles, gh-bump rejects the plan before dry-run success, commit, or PR creation.

GitHub Token Permissions

For fine-grained PAT, enable:

• Dependabot alerts: Read
• Contents: Read and write
• Pull requests: Read and write
• Metadata: Read

Source Map

src/
├── cli.ts              CLI entrypoint (parseArgs, env resolution)
├── action.ts           GitHub Action entrypoint (@actions/core)
└── core/
    ├── agent-safety.ts Agent safety helpers (branch names, edit scope, call budget)
    ├── agent.ts        Agent loop (fetch → group → LLM → commit → PR)
    ├── config.ts       Shared CLI and Action config normalization
    ├── fix-plan-summary.ts Dry-run FixPlan summary generation
    ├── fix-plan.ts     LLM FixPlan schema parsing
    ├── fix-plan-validation.ts Deterministic FixPlan safety validation
    ├── github.ts       GitHubClient (Octokit wrapper)
    ├── local-release.ts Local npm full-run workflow
    ├── manifest.ts     Lockfile-to-manifest mapping and alert grouping
    ├── llm.ts          Provider-agnostic LLM client (Vercel AI SDK)
    ├── prompts.ts      System prompt + alert prompt builder
    ├── types.ts        Shared TypeScript interfaces
    └── log.ts          Structured logging with CI detection

FAQ

What ecosystems does it support?

Any ecosystem the LLM understands. It reads your actual manifest files and determines the fix, rather than running ecosystem-specific commands. This means npm, pip, Go, Rust, Ruby, PHP, Java, C#, and more all work without special handling.

Does it update lockfiles?

No. The agent updates version constraints in manifest files (package.json, pyproject.toml, etc.). Lockfile regeneration should be handled by your CI pipeline or Dependabot.

How does it handle npm transitive vulnerabilities?

For npm alerts where the vulnerable package is not already a direct dependency, gh-bump asks the LLM to use a parent dependency update or package-manager override instead of adding the transitive package as a new direct dependency. It also validates the returned FixPlan and rejects package.json plans that add new direct entries to dependencies, devDependencies, optionalDependencies, or peerDependencies.

How can I debug malformed LLM responses?

Set GH_BUMP_LLM_RESPONSE_FILE to write the full raw model response to disk. This is useful when a provider returns malformed JSON or when you want to inspect the exact FixPlan before opening an issue.

How much does the AI cost per run?

Typically $0.01–0.10 per alert, depending on the model and file sizes. Use --max-llm-calls as a cost guardrail.

Can I use it without AI?

No. The LLM is core to how gh-bump works — it replaces all the hardcoded ecosystem detection and fix patterns from v1.

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

AI Contribution Attribution

When AI tools contribute to development, proper attribution helps track the evolving role of AI in the development process. Contributions should include an Assisted-by tag:

Assisted-by: Claude:claude-opus-4-20250514

License

MIT

---

Automate the boring stuff. Fix security alerts at scale.

📦 Source: soderlind/gh-bump · Edit on GitHub