# Crypto Hacks & Exploits Database (`foxlabs/crypto-hacks-database`) Actor

Searchable database of crypto hacks & exploits from DefiLlama (540+ incidents). Filter by date, min amount lost, chain, technique, classification, target type and bridge-hack flag; sort by date or size. Flat CSV/Excel, no API key, no proxy.

- **URL**: https://apify.com/foxlabs/crypto-hacks-database.md
- **Developed by:** [Berkan Kaplan](https://apify.com/foxlabs) (community)
- **Categories:** Developer tools, News
- **Features:** Limited permissions
- **Stats:** 15 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.50 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## Crypto Hacks & Exploits Database 🛡️

**foXLabs crypto series:** [Token unlocks](https://apify.com/foxlabs/token-unlocks-calendar) · [Funding rates](https://apify.com/foxlabs/crypto-funding-rates) · [DeFi volume & fees](https://apify.com/foxlabs/defillama-volume-fees) · [Stablecoins](https://apify.com/foxlabs/stablecoins-monitor)

🎉 Turn crypto exploit history into a clean, structured database — no login, no API key, one row per incident, with the project, amount lost, technique, chain and date.
Built for security, DeFi risk and crypto research teams.

### 🔍 What is the Crypto Hacks & Exploits Database — and when should you use it?

Give this actor project names or chains and it returns matching incidents from public crypto hack / exploit records — as clean, deduplicated rows you can filter, export or feed to an AI agent. Every run reads the source live.

**Use it when you need:** a project’s exploit history; hacks by technique or chain; or a timeline of incidents.

**Use something else when:** you need live protocol metrics — this is historical exploit records, not analytics.

### 🤖 Use with AI agents

**Already on the Apify MCP server?** Ask for this Actor by name: `foxlabs/crypto-hacks-database`.

**Your agent can pay for its own runs.** This Actor is pay-per-event with agentic payments, so an agent can discover it, run it and settle the bill over **x402 (USDC on Base)** or **Skyfire** — no Apify account or API token of its own. Billing is the same either way: per delivered record, never for errors.

Otherwise paste this into Claude, ChatGPT, Cursor or any MCP-enabled assistant:

```
I want to pull crypto exploit company records using the Apify Actor `foxlabs/crypto-hacks-database`.
Input: `sinceDate`, `minAmountUsd`, `chain`, `technique` and more — see the Input table below. `maxResults` caps how many results are returned.
Start with: {"minAmountUsd":1000000,"maxResults":1000}
Ask me what to look up, run the Actor, then summarise the rows as a table.
```

The machine-readable API, MCP config and OpenAPI definition live at `apify.com/foxlabs/crypto-hacks-database.md`.

### 📋 Overview

Everything you need to turn public crypto hack / exploit records into clean, structured data — in one actor, with no login, cookies or API key.

**Why teams pick this actor:**

- ✅ **Whole feed, one call** — name or ID in, matching incidents out.
- 🧹 **No empty-promise columns** — only fields this registry actually fills; degenerate columns are removed.
- 🔗 **Stable identifiers** — every row carries the source's own IDs, ready to join across runs and to other Fox Labs actors.
- 💰 **Per-row pricing** — a minimal price per delivered row, no subscription.
- 🤖 **Agent-ready** — MCP + x402 agentic payments.

### ✨ Features

- 🔍 **Name or ID lookup** — relevance-ranked name search or exact registry-ID lookup.
- 🏢 **Full entity profile** — status, legal form, formation date, address and the registry’s own contact fields.
- 🧹 **Clean schema** — deduplicated camelCase rows, ready for CSV/Excel/JSON.

### 🎬 Quick Start

```bash
curl -X POST "https://api.apify.com/v2/acts/foxlabs~crypto-hacks-database/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"minAmountUsd":1000000,"maxResults":1000}'
```

### 🚀 Getting Started (3 steps)

1. **Choose your targets** — project names or chains.
2. **Set the cap** — `maxResults` limits how many results are returned.
3. **Run and export** — get a clean dataset as JSON, CSV or Excel.

### 📥 Input

```json
{"minAmountUsd":1000000,"maxResults":1000}
```

| Field | Type | Description |
|---|---|---|
| `sinceDate` | string | Only include hacks on or after this date (YYYY-MM-DD). Leave empty for all time (540+ incidents back to ~2011). |
| `minAmountUsd` | integer | Only include hacks where at least this much (USD) was lost. 0 = no filter. |
| `chain` | string | Keep only hacks involving this chain. Exact chain label, case-insensitive — use DefiLlama's own spelling: "Ethereum", "BSC", "Solana", "Arbitrum", "Base",… |
| `technique` | string | Keep only hacks whose attack technique contains this text (substring, case-insensitive) — e.g. "Private Key", "Reentrancy", "Flashloan", "Oracle", "Phishing",… |
| `classification` | string | Exact classification filter (e.g. "Protocol Logic", "Infrastructure", "Ecosystem", "Rugpull"). Leave empty for all. |
| `targetType` | string | Exact target type filter (e.g. "DeFi Protocol", "CEX", "Wallet", "Token", "Gaming"). For bridge hacks use the bridge-hacks flag below — "Bridge" is not a target… |
| `bridgeHackOnly` | boolean | Return only cross-chain bridge hacks. |
| `onlyReturnedFunds` | boolean | Return only incidents where DefiLlama records recovered/returned funds (~29 incidents). DefiLlama's recovery data is a USD amount and is inconsistent for some… |
| `sortBy` | string | Order by most recent or by largest amount lost. |
| `maxResults` | integer | Cap the number of incident rows returned. |

### 📤 Output

One row per result, saved to the dataset. Every row carries `scrapedAt`. Lookups that cannot be completed are reported in the run log rather than silently dropped.

| Field | Description |
|---|---|
| `rank` | Rank |
| `dateIso` | Date Iso |
| `name` | Name |
| `amountUsd` | Amount Usd |
| `amountUsdCompact` | Amount Usd Compact |
| `chainsText` | Chains Text |
| `classification` | Classification |
| `technique` | Technique |
| `targetType` | Target Type |
| `bridgeHack` | Bridge Hack |
| `returnedFunds` | Returned Funds |
| `returnedFundsUsd` | Returned Funds Usd |
| `returnedFundsUsdCompact` | Returned Funds Usd Compact |
| `language` | Language |
| `defillamaId` | Defillama Id |
| `generatedAtIso` | Generated At Iso |

### 💼 Use cases

**1. Risk screening** — check a project’s exploit history.
*Input:* project names. *Output:* incidents + amounts. *Use:* a risk assessment.

**2. Threat research** — analyse exploits by technique.
*Input:* chains or keywords. *Output:* incidents + techniques. *Use:* a threat report.

**3. Insurance / audit** — size losses in a category.
*Input:* chains. *Output:* incidents + amounts. *Use:* quantify risk.

### 🔗 Integration

**JavaScript / Node.js**

```javascript
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('foxlabs/crypto-hacks-database').call({"minAmountUsd":1000000,"maxResults":1000});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);
```

**Python**

```python
from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')
run = client.actor('foxlabs/crypto-hacks-database').call(run_input={"minAmountUsd":1000000,"maxResults":1000})
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)
```

Written for `apify-client` 3 for Python, where `call()` returns a run object; with version 1 or 2 it returns a dict — write `run['defaultDatasetId']` there.

**Automation (n8n / Zapier / Make):** schedule or webhook → HTTP request to the actor API with your input → handle the JSON dataset → push to a sheet, CRM or dashboard.

### 📊 Pricing

Pay-per-event: **per delivered record**. Empty or failed lookups are never billed. [View current pricing.](https://apify.com/foxlabs/crypto-hacks-database)

### ❓ FAQ

**Do I need an account, login or API key?** No. This reads public crypto hack / exploit records.

**What do I search by?** Project names or chains.

**How current is the data?** Every run queries the source live, so results are as fresh as the registry.

**What does each row cover?** One incident: the project, amount lost, exploit technique, chain and date.

**Can I export to CSV / Excel / JSON?** Yes — directly from the Apify dataset.

### 🐛 Troubleshooting

- **Fewer rows than expected** — raise `maxResults`, or refine the input.
- **A name returns an unexpected entity** — it matched a similar registered name; search the exact registry ID.
- **No rows for a name** — try the entity’s exact legal name or its registry ID.

### ⚖️ Is it legal to scrape this data?

This actor reads publicly reported crypto hack and exploit records. Results can still contain personal data (e.g. a person’s name); personal data is protected by the GDPR and similar laws, so only process it with a legitimate basis. See Apify’s blog post on the legality of web scraping.

### 🤝 Support & contact

- 🌐 **Website:** [data.foxlabs.com.tr](https://data.foxlabs.com.tr)
- 📧 **Email:** info@foxlabs.com.tr
- 🐛 **Issues:** open a ticket in the Actor’s **Issues** tab
- 🧰 **More clean B2B data actors:** [Fox Labs on Apify](https://apify.com/foxlabs)

### Changelog

#### 0.1.18 — 2026-10-08 — README: the Python example works with the current Apify client

- The README's Python example read the results with `run['defaultDatasetId']`. With the current Apify client for Python (version 3) `call()` returns a run object, not a dict, and that line raised `TypeError: 'Run' object is not subscriptable` — after the run itself had finished. The example now reads `run.default_dataset_id`; with client version 1 or 2, keep `run['defaultDatasetId']`. Checked with apify-client 3.2.1, 2.5.1 and 1.12.2.
- No code, output field or pricing change.

#### 0.1.17 — 2026-09-20 — README examples corrected against the real input schema

- **The README's code examples did not match this Actor.** They used `queries` and `maxResultsPerQuery` — keys that do not exist in this Actor's input schema — with a placeholder value, and the input table listed those same phantom fields. Anyone who copied the AI-agent, cURL, JavaScript or Python example got a failing run. Every example now uses the real schema and matches the Console prefill: `{"minAmountUsd":1000000,"maxResults":1000}`
- The input table is regenerated from `input_schema.json`, so it lists the fields the Actor actually accepts.
- Removed claims carried over from the same generator template where present: "formation / status monitoring", "a canonical registry record for KYB and due diligence", "every row carries `query`", and `industry` described as a NACE code.
- No code, output field or pricing change.

#### 0.1 — 2026-09-07

- **Enabled AI-agent payments (x402) + rebuilt the README to the full standard** (What-is / when, AI-agents + x402 agentic payments + MCP, Overview, Features, Use cases, Integration, FAQ, Troubleshooting, Support & contact).

#### 0.0

- Initial release: data from public crypto hack / exploit records by name or registry ID.

# Changelog

This Actor's version history is a separate document: https://apify.com/foxlabs/crypto-hacks-database/changelog.md

# Actor input Schema

## `sinceDate` (type: `string`):

Only include hacks on or after this date (YYYY-MM-DD). Leave empty for all time (540+ incidents back to ~2011).

## `minAmountUsd` (type: `integer`):

Only include hacks where at least this much (USD) was lost. 0 = no filter.

## `chain` (type: `string`):

Keep only hacks involving this chain. Exact chain label, case-insensitive — use DefiLlama's own spelling: "Ethereum", "BSC", "Solana", "Arbitrum", "Base", "Bitcoin". (It's "BSC", not "BNB Chain".) Leave empty for all.

## `technique` (type: `string`):

Keep only hacks whose attack technique contains this text (substring, case-insensitive) — e.g. "Private Key", "Reentrancy", "Flashloan", "Oracle", "Phishing", "Access Control". Leave empty for all.

## `classification` (type: `string`):

Exact classification filter (e.g. "Protocol Logic", "Infrastructure", "Ecosystem", "Rugpull"). Leave empty for all.

## `targetType` (type: `string`):

Exact target type filter (e.g. "DeFi Protocol", "CEX", "Wallet", "Token", "Gaming"). For bridge hacks use the bridge-hacks flag below — "Bridge" is not a target type. Leave empty for all.

## `bridgeHackOnly` (type: `boolean`):

Return only cross-chain bridge hacks.

## `onlyReturnedFunds` (type: `boolean`):

Return only incidents where DefiLlama records recovered/returned funds (~29 incidents). DefiLlama's recovery data is a USD amount and is inconsistent for some large hacks, so treat it as a DefiLlama-sourced signal and verify individually.

## `sortBy` (type: `string`):

Order by most recent or by largest amount lost.

## `maxResults` (type: `integer`):

Cap the number of incident rows returned.

## Actor input object example

```json
{
  "sinceDate": "",
  "minAmountUsd": 1000000,
  "chain": "",
  "technique": "",
  "classification": "",
  "targetType": "",
  "bridgeHackOnly": false,
  "onlyReturnedFunds": false,
  "sortBy": "amount",
  "maxResults": 1000
}
```

# Actor output Schema

## `dataset` (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 = {
    "minAmountUsd": 1000000,
    "sortBy": "amount",
    "maxResults": 1000
};

// Run the Actor and wait for it to finish
const run = await client.actor("foxlabs/crypto-hacks-database").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 = {
    "minAmountUsd": 1000000,
    "sortBy": "amount",
    "maxResults": 1000,
}

# Run the Actor and wait for it to finish
run = client.actor("foxlabs/crypto-hacks-database").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 '{
  "minAmountUsd": 1000000,
  "sortBy": "amount",
  "maxResults": 1000
}' |
apify call foxlabs/crypto-hacks-database --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,foxlabs/crypto-hacks-database"
        }
    }
}
```

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/mZ0L7126YkurQRLad/builds/Yr9BzrnSf6C6iMvyh/openapi.json