# EPSS Exploit Prediction Scoring System Scraper (`parseforge/epss-exploit-prediction-scraper`) Actor Scrape EPSS (Exploit Prediction Scoring System) scores from FIRST.org. Returns the 30-day probability and percentile rank of CVE exploitation. Filter by CVE ID(s), date, history window, or minimum score. - **URL**: https://apify.com/parseforge/epss-exploit-prediction-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Developer tools, Business, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $24.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/main/banners/banner-default.jpg?raw=true) ## ๐Ÿ“ˆ EPSS Exploit Prediction Scraper > ๐Ÿš€ **Export FIRST.org EPSS exploit-prediction scores in seconds.** Pull the daily probability that a CVE will be exploited in the next 30 days, plus its global percentile rank, with optional historical time-series for trend analysis. No sign-up, no token, no manual ingestion. > ๐Ÿ•’ **Last updated:** 2026-05-15 ยท **๐Ÿ“Š 8 fields** per record ยท **๐Ÿ“ˆ 240,000+ scored CVEs** ยท **๐ŸŽฏ Daily refresh** ยท **๐Ÿ“… Up to 730-day history** The **EPSS Exploit Prediction Scraper** pulls scores from the FIRST.org Exploit Prediction Scoring System and returns **8 normalised fields per record**, including the CVE ID, the EPSS score (probability between 0 and 1 of exploitation in the next 30 days), the percentile rank against every other scored CVE, a derived severity band (Critical / High / Medium / Low), the score date, and an optional historical time series. EPSS is the de facto data-driven prioritisation signal used by mature security teams worldwide and is recommended by the U.S. federal vulnerability disclosure community as a complement to CVSS. The system covers **240,000+ scored CVEs refreshed daily** and offers up to **730 days of historical scores per CVE** for trend tracking. This Actor makes that data downloadable as CSV, Excel, JSON, or XML in minutes. Filters apply at the source, so you skip pagination, rate-limit handling, and time-series flattening entirely. | ๐ŸŽฏ Target Audience | ๐Ÿ’ก Primary Use Cases | |---|---| | Security teams, vulnerability managers, threat intel analysts, SOC engineers, risk modellers, security tool builders, ML researchers | EPSS-based patch prioritisation, risk scoring, vendor scorecards, CVE trend analysis, SIEM enrichment, ML features for vulnerability prediction | --- ### ๐Ÿ“‹ What the EPSS Exploit Prediction Scraper does Several workflows in a single Actor: - ๐Ÿ“ˆ **Daily corpus export.** Pull every scored CVE for the latest available date. - ๐Ÿ†” **Single CVE score.** Look up one CVE by ID for a quick triage check. - ๐Ÿ“ฆ **Batch CVE scores.** Pass a comma-separated list of CVE IDs and get every score in one run. - ๐Ÿ“… **Specific date.** Fetch scores for a historical date (YYYY-MM-DD). - ๐Ÿ•ฐ๏ธ **Time-series window.** Return up to 730 days of historical scores per CVE for trend analysis. - ๐ŸŽš๏ธ **Threshold filters.** Restrict to CVEs above a minimum EPSS score (e.g. โ‰ฅ 0.5) or above a percentile (e.g. top 5%). - ๐Ÿ“Š **Flatten time series.** Toggle to emit one row per (cve, date) for spreadsheets and SQL. Each record includes the CVE identifier, the canonical detail URL, the EPSS probability, the percentile rank, a derived severity band, the score date, and an optional nested or flattened time series. > ๐Ÿ’ก **Why it matters:** CVSS tells you how bad a CVE could be in theory; EPSS tells you how likely it is to actually be exploited in the next 30 days. Combining the two is the modern standard for patch prioritisation. Building your own EPSS ingestion means handling pagination, daily refreshes, and the optional time-series shape. This Actor skips all of that and gives you a clean, downloadable dataset. --- ### ๐ŸŽฌ Full Demo _๐Ÿšง Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded EPSS dataset._ --- ### โš™๏ธ Input
InputTypeDefaultBehavior
cveIdstring""Single CVE or comma-separated list (e.g. CVE-2021-44228,CVE-2023-50164). Empty = full corpus.
dateYYYY-MM-DD""Score date. Empty = latest available.
daysBackintegernullReturn up to N days of historical scores per CVE (max 730).
minEpssScorestring (float)""Threshold 0-1. Example: 0.5 = 50% probability of exploitation.
minPercentilestring (float)""Threshold 0-1. Example: 0.95 = top 5% of scored CVEs.
flattenTimeSeriesbooleanfalseOutput one row per (cve, date) instead of nested time series.
maxItemsinteger10Records to return. Free plan caps at 10, paid plan at 1,000,000.
**Example: top 1% of CVEs by EPSS for the latest date.** ```json { "minPercentile": "0.99", "maxItems": 500 } ```` **Example: 90-day history for the Log4Shell family, flattened.** ```json { "cveId": "CVE-2021-44228,CVE-2021-45046,CVE-2021-45105", "daysBack": 90, "flattenTimeSeries": true } ``` > โš ๏ธ **Good to Know:** EPSS scores are updated daily. A score of 0.95 means a 95% modelled probability of exploitation in the next 30 days. EPSS does not replace CVSS; the two answer different questions and work best together. *** ### ๐Ÿ“Š Output Each record contains **8 fields**. Download the dataset as CSV, Excel, JSON, or XML. #### ๐Ÿงพ Schema | Field | Type | Example | |---|---|---| | ๐Ÿ†” `cve` | string | `"CVE-2021-44228"` | | ๐Ÿ”— `url` | string | `"https://www.first.org/epss/"` | | ๐Ÿ“ˆ `epss` | number | null | `0.97432` | | ๐Ÿ“Š `percentile` | number | null | `0.99987` | | ๐Ÿšฆ `severity` | string | null | `"Critical"` | | ๐Ÿ“… `date` | YYYY-MM-DD | `"2026-05-14"` | | ๐Ÿ•ฐ๏ธ `timeSeries` | object\[] | null | `[{ "date": "2026-05-13", "epss": 0.974, "percentile": 0.99987 }, ...]` | | ๐Ÿ•’ `scrapedAt` | ISO 8601 | `"2026-05-15T00:00:00.000Z"` | #### ๐Ÿ“ฆ Sample record
๐Ÿ”ฅ Top-of-the-list: Log4Shell (CVE-2021-44228) with 30-day history ```json { "cve": "CVE-2021-44228", "url": "https://www.first.org/epss/", "epss": 0.97432, "percentile": 0.99987, "severity": "Critical", "date": "2026-05-14", "timeSeries": [ { "date": "2026-05-13", "epss": 0.97412, "percentile": 0.99987 }, { "date": "2026-05-12", "epss": 0.97365, "percentile": 0.99986 }, { "date": "2026-05-11", "epss": 0.97331, "percentile": 0.99985 } ], "scrapedAt": "2026-05-15T00:00:00.000Z" } ```
*** ### โœจ Why choose this Actor | | Capability | |---|---| | ๐Ÿ“ˆ | **Authoritative source.** Pulls directly from the FIRST.org EPSS catalogue, the de facto exploit-prediction model. | | ๐ŸŽฏ | **Daily probabilities.** 30-day exploitation probability between 0 and 1, refreshed every day. | | ๐Ÿ“Š | **Percentile ranks.** Compare any CVE against every other scored entry in the corpus. | | ๐Ÿ•ฐ๏ธ | **Up to 730 days of history.** Track score evolution over time for individual CVEs. | | ๐Ÿšฆ | **Derived severity bands.** Critical / High / Medium / Low buckets ready for dashboards. | | ๐Ÿ“ | **Flatten or nest.** Toggle between long-format (one row per cve+date) and wide-format outputs. | | ๐Ÿšซ | **No sign-up.** Works with public exploit-prediction data. No login or token needed. | > ๐Ÿ“Š EPSS is the modern data-driven complement to CVSS. Owning a clean local feed is a multiplier for every patch-management and risk-scoring workflow. *** ### ๐Ÿ“ˆ How it compares to alternatives | Approach | Cost | Coverage | Refresh | Filters | Setup | |---|---|---|---|---|---| | **โญ EPSS Exploit Prediction Scraper** *(this Actor)* | $5 free credit, then pay-per-use | **240,000+ scored CVEs** | **Daily** | CVE list, date, score, percentile | โšก 2 min | | Commercial threat-intel feeds | $10,000+/year | Curated subset | Streaming | Many | โณ Days | | Manual CSV dumps | Free | Full | Stale by next day | None | ๐Ÿข Hours | | Self-built ingestion | Engineering time | Full | Custom | Custom | ๐Ÿ› ๏ธ Weeks | Pick this Actor when you want EPSS scores ready to merge with your CVE inventory. *** ### ๐Ÿš€ How to use 1. ๐Ÿ“ **Sign up.** [Create a free account with $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) (takes 2 minutes). 2. ๐ŸŒ **Open the Actor.** Go to the EPSS Exploit Prediction Scraper page on the Apify Store. 3. ๐ŸŽฏ **Set input.** Pick a CVE list, threshold, or date, then set `maxItems`. 4. ๐Ÿš€ **Run it.** Click **Start** and let the Actor collect your data. 5. ๐Ÿ“ฅ **Download.** Grab your results in the **Dataset** tab as CSV, Excel, JSON, or XML. > โฑ๏ธ Total time from signup to downloaded dataset: **3-5 minutes.** No coding required. *** ### ๐Ÿ’ผ Business use cases
#### ๐Ÿ›ก๏ธ Vulnerability Management - EPSS-aware patch prioritisation queues for IT and SOC - Risk scores combining CVSS severity with EPSS probability - Daily delta reports flagging CVEs whose EPSS rose sharply - Tier-1 vs tier-2 ticket routing based on percentile thresholds #### ๐Ÿ” Threat Intelligence & Research - Track EPSS climbers and fallers in near real time - Build vendor risk scorecards based on average EPSS - Map EPSS evolution against active campaigns and zero-days - Correlate EPSS jumps with KEV additions and disclosures
#### ๐Ÿค– Risk Modelling & ML - Feature inputs for vulnerability-prediction ML models - Backtest patch prioritisation policies against historical EPSS - Train custom severity models on EPSS time series - A/B-test patch SLAs against EPSS bucketing #### ๐Ÿ“Š Executive Reporting - Board dashboards mixing CVSS, EPSS, and KEV signals - Insurance underwriting models incorporating exploit probability - Customer-facing trust pages with EPSS-aware patch SLAs - Quarterly risk-posture reports
*** ### ๐Ÿ”Œ Automating EPSS Exploit Prediction Scraper Control the scraper programmatically for scheduled runs and pipeline integrations: - ๐ŸŸข **Node.js.** Install the `apify-client` NPM package. - ๐Ÿ **Python.** Use the `apify-client` PyPI package. - ๐Ÿ“š See the [Apify documentation](https://docs.apify.com/) for full details. The [Apify Schedules feature](https://docs.apify.com/platform/schedules) lets you trigger this Actor on any cron interval. Daily refreshes line up perfectly with the EPSS publication cadence. *** ### ๐ŸŒŸ Beyond business use cases Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.
#### ๐ŸŽ“ Research and academia - EPSS calibration and validation studies - Joint CVSS + EPSS prioritisation research - Coursework on quantitative vulnerability management - Reproducible studies with cited, versioned dataset pulls #### ๐ŸŽจ Personal and creative - Hobbyist EPSS dashboards for home-lab security research - Newsletter research on the riskiest CVEs of the week - Portfolio projects that show off security data engineering - Personal alerting bots for vendors you actually use
#### ๐Ÿค Non-profit and civic - Local government IT teams tracking EPSS evolution - Civic-tech projects mapping risk for critical infrastructure - Investigative journalism on exploit-prediction trends - Educational outreach for risk-based prioritisation #### ๐Ÿงช Experimentation - Train ML models that predict EPSS jumps - Prototype agent pipelines that summarise EPSS climbers - Test SIEM rules against historical EPSS waves - Build dashboards on top of live EPSS feeds
*** ### ๐Ÿค– Ask an AI assistant about this scraper Open a ready-to-send prompt about this ParseForge actor in the AI of your choice: - ๐Ÿ’ฌ [**ChatGPT**](https://chat.openai.com/?q=How%20do%20I%20use%20the%20EPSS%20Exploit%20Prediction%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿง  [**Claude**](https://claude.ai/new?q=How%20do%20I%20use%20the%20EPSS%20Exploit%20Prediction%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ” [**Perplexity**](https://perplexity.ai/search?q=How%20do%20I%20use%20the%20EPSS%20Exploit%20Prediction%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ…’ [**Copilot**](https://copilot.microsoft.com/?q=How%20do%20I%20use%20the%20EPSS%20Exploit%20Prediction%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) *** ### โ“ Frequently Asked Questions #### ๐Ÿงฉ What is EPSS? EPSS (Exploit Prediction Scoring System) is a daily-updated model from FIRST.org that estimates the probability a CVE will be exploited in the wild in the next 30 days. It is the modern data-driven complement to CVSS. #### ๐Ÿ“ˆ What does the score mean? The EPSS score is a probability between 0 and 1. A score of 0.95 means a 95% modelled probability of exploitation in the next 30 days. The percentile field tells you where the CVE ranks against every other scored entry. #### ๐Ÿ” How often is the dataset refreshed? EPSS publishes new scores every day. Every run reflects the corpus as of the latest available date. #### ๐Ÿšฆ What are the severity bands? The Actor derives a `Critical` / `High` / `Medium` / `Low` band from the EPSS score: > 0.95 is Critical, โ‰ฅ 0.5 is High, โ‰ฅ 0.1 is Medium, otherwise Low. The bands are convenience labels; raw scores remain in the dataset for custom thresholds. #### ๐Ÿ•ฐ๏ธ How far back does the history go? Up to 730 days per CVE via the `daysBack` input. The full historical corpus is available from the source with continuous coverage since the model went live. #### ๐Ÿ†š EPSS vs CVSS - which should I use? Both. CVSS rates technical severity if a vulnerability is exploited; EPSS estimates the likelihood of exploitation. Mature programmes combine the two: a Critical CVSS with a high EPSS gets fast-tracked, while a Critical CVSS with a low EPSS can be patched on the regular cycle. #### ๐Ÿงฎ Can I get one row per (cve, date) instead of nested time series? Yes. Set `flattenTimeSeries: true` to emit one row per CVE per date. This is the most spreadsheet- and SQL-friendly shape for trend analysis. #### โฐ Can I schedule regular runs? Yes. Use Apify Schedules to run this Actor on any cron interval. A daily schedule lines up with the EPSS publication cadence. #### โš–๏ธ Is this data legal to use? EPSS is published under permissive open licensing by FIRST.org. You should review the source license for your specific application but raw scores are public. #### ๐Ÿ’ณ Do I need a paid Apify plan to use this Actor? No. The free Apify plan is enough for testing and small runs (10 records per run). A paid plan lifts the limit and gives you scheduling, higher concurrency, and larger datasets. #### ๐Ÿ†˜ What if I need help? Our support team is here to help. Contact us through the Apify platform or use the Tally form linked below. *** ### ๐Ÿ”Œ Integrate with any app EPSS Exploit Prediction Scraper connects to any cloud service via [Apify integrations](https://apify.com/integrations): - [**Make**](https://docs.apify.com/platform/integrations/make) - Automate multi-step workflows - [**Zapier**](https://docs.apify.com/platform/integrations/zapier) - Connect with 5,000+ apps - [**Slack**](https://docs.apify.com/platform/integrations/slack) - Get EPSS alerts in your security channels - [**Airbyte**](https://docs.apify.com/platform/integrations/airbyte) - Pipe EPSS data into your warehouse - [**GitHub**](https://docs.apify.com/platform/integrations/github) - Trigger runs from commits and releases - [**Google Drive**](https://docs.apify.com/platform/integrations/drive) - Export datasets straight to Sheets You can also use webhooks to trigger downstream actions when a run finishes. Push fresh EPSS data into your ticketing system, or alert your team in Slack when a CVE jumps into the top percentile. *** ### ๐Ÿ”— Recommended Actors - [**๐Ÿ›ก๏ธ NIST NVD CVE Scraper**](https://apify.com/parseforge/nist-nvd-cve-scraper) - Official NVD catalogue with CVSS v4/v3/v2 scores - [**๐Ÿšจ CISA KEV Scraper**](https://apify.com/parseforge/cisa-kev-scraper) - Known Exploited Vulnerabilities catalogue with due dates - [**๐Ÿ™ GitHub Security Advisories Scraper**](https://apify.com/parseforge/github-security-advisories-scraper) - GHSA + CVE advisories with patched versions - [**๐Ÿ“ฆ OSV Vulnerabilities Scraper**](https://apify.com/parseforge/osv-vulnerabilities-scraper) - Open source vulnerabilities across 30+ ecosystems - [**๐Ÿ”ฌ CIRCL CVE Scraper**](https://apify.com/parseforge/circl-cve-scraper) - CIRCL Luxembourg CVE catalogue with CWE and CAPEC > ๐Ÿ’ก **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for more security and reference-data scrapers. *** **๐Ÿ†˜ Need Help?** [**Open our contact form**](https://tally.so/r/BzdKgA) to request a new scraper, propose a custom data project, or report an issue. *** > **โš ๏ธ Disclaimer:** this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by FIRST.org or the EPSS Special Interest Group. All trademarks mentioned are the property of their respective owners. Only publicly available exploit-prediction data is collected. # Actor input Schema ## `cveId` (type: `string`): Single CVE or comma-separated list (e.g. 'CVE-2021-44228,CVE-2023-50164'). Leave empty to scrape the full daily corpus. ## `date` (type: `string`): Fetch EPSS scores for this specific date (YYYY-MM-DD). Defaults to the latest available scores. ## `daysBack` (type: `integer`): Return up to N days of historical scores per CVE. Use to track score evolution. ## `minEpssScore` (type: `string`): Only include CVEs with EPSS >= this threshold (0.0 - 1.0). Example: 0.5 = 50% probability of exploitation in next 30 days. ## `minPercentile` (type: `string`): Only include CVEs in the top percentile (0.0 - 1.0). Example: 0.95 = top 5%. ## `flattenTimeSeries` (type: `boolean`): When daysBack is set, output one row per (cve, date) instead of nesting time-series inside each CVE. Easier for spreadsheets and SQL. ## `maxItems` (type: `integer`): Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000 ## Actor input object example ```json { "cveId": "", "date": "", "minEpssScore": "", "minPercentile": "", "flattenTimeSeries": false, "maxItems": 10 } ``` # Actor output Schema ## `overview` (type: `string`): Overview of scraped data ## `fullData` (type: `string`): Complete dataset # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "cveId": "", "date": "", "minEpssScore": "", "minPercentile": "", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/epss-exploit-prediction-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "cveId": "", "date": "", "minEpssScore": "", "minPercentile": "", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("parseforge/epss-exploit-prediction-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "cveId": "", "date": "", "minEpssScore": "", "minPercentile": "", "maxItems": 10 }' | apify call parseforge/epss-exploit-prediction-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/epss-exploit-prediction-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "EPSS Exploit Prediction Scoring System Scraper", "description": "Scrape EPSS (Exploit Prediction Scoring System) scores from FIRST.org. Returns the 30-day probability and percentile rank of CVE exploitation. Filter by CVE ID(s), date, history window, or minimum score.", "version": "0.0", "x-build-id": "hxraNWCHY9oddVv2M" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~epss-exploit-prediction-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-epss-exploit-prediction-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~epss-exploit-prediction-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-epss-exploit-prediction-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~epss-exploit-prediction-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-epss-exploit-prediction-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "cveId": { "title": "CVE ID(s)", "type": "string", "description": "Single CVE or comma-separated list (e.g. 'CVE-2021-44228,CVE-2023-50164'). Leave empty to scrape the full daily corpus.", "default": "" }, "date": { "title": "Score Date", "type": "string", "description": "Fetch EPSS scores for this specific date (YYYY-MM-DD). Defaults to the latest available scores.", "default": "" }, "daysBack": { "title": "History Window (days)", "minimum": 1, "maximum": 730, "type": "integer", "description": "Return up to N days of historical scores per CVE. Use to track score evolution." }, "minEpssScore": { "title": "Minimum EPSS Score", "type": "string", "description": "Only include CVEs with EPSS >= this threshold (0.0 - 1.0). Example: 0.5 = 50% probability of exploitation in next 30 days.", "default": "" }, "minPercentile": { "title": "Minimum Percentile", "type": "string", "description": "Only include CVEs in the top percentile (0.0 - 1.0). Example: 0.95 = top 5%.", "default": "" }, "flattenTimeSeries": { "title": "Flatten Time Series", "type": "boolean", "description": "When daysBack is set, output one row per (cve, date) instead of nesting time-series inside each CVE. Easier for spreadsheets and SQL.", "default": false }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```