# GitHub Actions Security Auditor — Workflow Risk Monitor (`bin_ai_tools/my-actor-3`) Actor

Audit public GitHub Actions workflows with zizmor. Track new, unchanged, and resolved security findings across repeated scans, with exact rules, severity, locations, and remediation documentation.

- **URL**: https://apify.com/bin\_ai\_tools/my-actor-3.md
- **Developed by:** [Bin Bin](https://apify.com/bin_ai_tools) (community)
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 workflow security findings

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## GitHub Actions Security Auditor — Workflow Risk Monitor

Monitor public GitHub repositories for GitHub Actions security findings and see what changed since the previous successful scan.

This Actor uses [`zizmor`](https://github.com/zizmorcore/zizmor) as the static-analysis engine and adds an Apify product layer for scheduled/batch scanning, normalized Dataset output, persistent per-repository baselines, and `NEW / STILL / RESOLVED` change tracking.

### What it solves

A one-time workflow security scan tells you what exists now. This Actor is designed for repeat monitoring:

- `NEW` — a finding appears that was not in the previous successful baseline.
- `STILL` — the same stable finding is still present.
- `RESOLVED` — a previous finding disappeared.

Stable fingerprints intentionally ignore row/column offsets, so ordinary line movement should not create fake new findings.

### Input

```json
{
  "repositories": [
    "actions/checkout",
    "https://github.com/zizmorcore/zizmor"
  ],
  "minSeverity": "low",
  "includeStill": true
}
```

#### Fields

- `repositories` — 1–20 **public** GitHub repositories. Use `owner/repo` or a repository-root GitHub URL.
- `minSeverity` — `informational`, `low`, `medium`, or `high`. Default: `low`.
- `includeStill` — when `false`, Dataset only emits `NEW` and `RESOLVED`; state still tracks all current findings.

No GitHub token is required in v0.1.

### Output

Each Dataset row contains the normalized finding plus monitoring state:

```json
{
  "repository": "owner/repo",
  "status": "NEW",
  "rule": "template-injection",
  "severity": "high",
  "confidence": "high",
  "workflowFile": ".github/workflows/ci.yml",
  "route": "jobs.build.steps[0]",
  "title": "code injection via template expansion",
  "documentationUrl": "https://docs.zizmor.sh/...",
  "findingKey": "sha256:...",
  "checkedAt": "2026-09-16T00:00:00.000Z"
}
```

`OUTPUT` summarizes repository successes/failures, current findings, NEW/STILL/RESOLVED counts, and current findings by severity.

### Safety model

The Actor downloads a public GitHub repository archive into an Actor-controlled temporary directory and statically scans it with `zizmor`.

It does **not**:

- run repository workflows,
- run `npm install` or other package installs from the target repository,
- execute binaries or scripts from the target repository,
- auto-fix workflows,
- classify a repository or author as malicious.

A security finding means a static rule matched and should be reviewed in context. It is not a malware verdict.

### State

A named key-value store, `github-actions-security-auditor-state`, stores the previous successful normalized findings for each canonical repository slug. Failed repository scans do not erase their previous baselines.

First successful scan: all current findings are `NEW`.

Later successful scans: findings become `STILL`, newly appearing fingerprints become `NEW`, and disappeared fingerprints become `RESOLVED`.

### Current limitations

- Public GitHub repositories only.
- GitHub public archive/API limits can affect very large batches or frequent schedules.
- No organization-wide repository discovery.
- No private repository authentication.
- No automatic remediation or pull requests.
- v0.1 is the Apify validation product only; n8n/MCP/API/CLI adapters are intentionally not included until real user demand exists.

### Attribution

Security detection is powered by [`zizmor`](https://github.com/zizmorcore/zizmor), an MIT-licensed open-source GitHub Actions security analysis tool. 007 uses `zizmor` as an external dependency and does not copy or reimplement its audit rules.

# Actor input Schema

## `repositories` (type: `array`):

Add 1–20 public GitHub repositories as owner/repo slugs or full https://github.com/owner/repo URLs.

## `minSeverity` (type: `string`):

Only ask zizmor to return findings at or above this severity.

## `includeStill` (type: `boolean`):

When enabled, Dataset includes STILL findings as well as NEW and RESOLVED changes.

## Actor input object example

```json
{
  "repositories": [
    "actions/checkout"
  ],
  "minSeverity": "low",
  "includeStill": true
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `report` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "repositories": [
        "actions/checkout"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("bin_ai_tools/my-actor-3").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "repositories": ["actions/checkout"] }

# Run the Actor and wait for it to finish
run = client.actor("bin_ai_tools/my-actor-3").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "repositories": [
    "actions/checkout"
  ]
}' |
apify call bin_ai_tools/my-actor-3 --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bin_ai_tools/my-actor-3"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/aIQa5qadQLNAIFfhO/builds/NYZFuQemhR9lnnhEb/openapi.json