# JailbreakBench LLM Robustness Leaderboard Scraper (`jungle_synthesizer/jailbreakbench-llm-robustness-leaderboard-scraper`) Actor Scrapes the public JailbreakBench leaderboard tracking attack-success-rate for jailbreak techniques (PAIR, GCG, AIM, and more) against open- and closed-source LLMs, with and without defenses (SmoothLLM, perplexity filter, etc). Snapshot each run to track technique-vs-model ASR movement over time. - **URL**: https://apify.com/jungle\_synthesizer/jailbreakbench-llm-robustness-leaderboard-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## JailbreakBench LLM Robustness Leaderboard Scraper Scrape the [JailbreakBench](https://jailbreakbench.github.io/) leaderboard — the open academic robustness benchmark tracking jailbreak attack success rate (ASR) against open- and closed-source LLMs, with and without defenses. This actor returns one row per (model, attack technique, defense) combination, stamped with a per-run snapshot date so repeated runs build a time-series of ASR movement. ### What You Get Each record contains: - **Leaderboard** — always `jailbreakbench` (the source benchmark) - **Leaderboard group** — `open_source` or `closed_source`, depending on which JailbreakBench table the row came from - **Model** and **model provider** — target LLM (e.g. `Vicuna-13B`, `GPT-4-0125-Preview`) and its organization, inferred from a static prefix map (Meta, LMSYS, OpenAI, Anthropic, Google, Mistral AI, Alibaba, Cohere); left empty when the model isn't recognized rather than guessed - **Attack method** and **attack method (full name)** — short code (e.g. `PAIR`, `GCG`, `AIM`) plus the full published technique name - **Attack success rate** — ASR percentage reported by JailbreakBench for this row - **Defense** — defense mechanism applied during the evaluation (e.g. `SmoothLLM`, `Perplexity filter`, `None`) - **Dataset** — always `JBB-Behaviors`, the behavior dataset JailbreakBench evaluates against - **Metric type** — always `attack` (JailbreakBench evaluates defenses as attack outcomes against a defended model, not as a separate metric) - **Threat model** — access assumption the attack makes (e.g. `Black-box access`) - **Average queries** — average number of queries the attack used to succeed, as published (`-` when not reported for that technique) - **Submission date** — date the result was published/updated on the leaderboard - **Paper URL** and **jailbreak artifacts URL** — links to the technique's paper and published attack transcripts, when available - **Snapshot date** — this run's capture date (`YYYY-MM-DD`), the time-series anchor for tracking ASR movement across runs - **Source URL** and **scraped at** — exact page fetched and per-record scrape timestamp `refusal_rate` and `benign_refusal_rate` are included in the schema for parity with the broader jailbreak-benchmark data model but are always empty for JailbreakBench — the source only reports a single ASR figure per row, not a separately-measured refusal rate. ### Scope This actor scrapes **JailbreakBench only**. JailbreakBench's own leaderboard page names it as the reference source; adjacent benchmarks (HarmBench, TrustLLM) are not included — HarmBench in particular renders its leaderboard client-side from a heavier SPA shell and would need a materially different (browser-based) build. ### Usage #### Basic run (both leaderboards, default 10 records) No configuration needed — just run with default settings. #### Full leaderboard Set `maxItems` to a high number (e.g., 200) to get every row across both tables (~96 rows total as of this actor's build). #### Filter by leaderboard Use the `leaderboardType` input to scrape just one table: ```json { "leaderboardType": "open_source", "maxItems": 100 } ```` ### Input Schema | Field | Type | Default | Description | |-------|------|---------|-------------| | `leaderboardType` | enum | `both` | Which leaderboard table(s) to scrape: `both`, `open_source`, or `closed_source` | | `maxItems` | integer | 10 | Maximum total records to return | ### Output Sample ```json { "leaderboard": "jailbreakbench", "leaderboard_group": "open_source", "model": "Vicuna-13B", "model_provider": "LMSYS", "attack_method": "PAIR", "attack_method_full": "Prompt Automatic Iterative Refinement (PAIR)", "attack_success_rate": 69, "refusal_rate": "", "defense": "None", "benign_refusal_rate": "", "dataset": "JBB-Behaviors", "metric_type": "attack", "threat_model": "Black-box access", "average_queries": "34", "submission_date": "12 Oct 2023", "paper_url": "https://arxiv.org/abs/2310.08419", "jailbreak_artifacts_url": "https://github.com/JailbreakBench/artifacts/blob/main/attack-artifacts/PAIR/black_box/vicuna-13b-v1.5.json", "snapshot_date": "2026-07-13", "source_url": "https://jailbreakbench.github.io/tables/table_open_source.html", "scraped_at": "2026-07-13T07:57:28.824Z" } ``` ### Technical Notes - **Two-URL crawl, no pagination.** JailbreakBench's homepage doesn't carry the table data itself — the leaderboard sections load client-side via jQuery from two static HTML fragment pages, which this actor fetches directly: `tables/table_open_source.html` and `tables/table_closed_source.html`. - **No proxy required** — GitHub Pages static hosting with no anti-bot measures. - **Fast** — a full run across both tables completes in a few seconds. - **Time-series by design.** Run this actor periodically (e.g. weekly) to build a history of ASR movement per technique/model/defense combination — each run's `snapshot_date` is the time-series anchor. This is a small, always-complete-in-one-run crawl; there's no large-crawl resume workflow to manage. ### Use Cases - Track jailbreak/robustness leaderboard movement over time for specific models, attacks, or defenses - Compare open-source vs. closed-source model resilience to published jailbreak techniques - Feed AI-safety or guardrail-vendor evaluation pipelines with structured ASR data - Research and academic benchmarking of LLM robustness trends # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `resumeCursor` (type: `string`): Leave empty for a fresh crawl. To CONTINUE a previous run where it stopped — without paying again for records you already received — paste the `resumeCursor` value from that run's Output (the run's OUTPUT key). Resume promptly: the previous run's data expires with your account's retention window (free tier: your ~10 most recent runs). ## `leaderboardType` (type: `string`): Which JailbreakBench leaderboard table(s) to scrape. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "leaderboardType": "both", "maxItems": 10 } ``` # Actor output Schema ## `results` (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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "leaderboardType": "both", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/jailbreakbench-llm-robustness-leaderboard-scraper").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "leaderboardType": "both", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/jailbreakbench-llm-robustness-leaderboard-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "leaderboardType": "both", "maxItems": 10 }' | apify call jungle_synthesizer/jailbreakbench-llm-robustness-leaderboard-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/jailbreakbench-llm-robustness-leaderboard-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "JailbreakBench LLM Robustness Leaderboard Scraper", "description": "Scrapes the public JailbreakBench leaderboard tracking attack-success-rate for jailbreak techniques (PAIR, GCG, AIM, and more) against open- and closed-source LLMs, with and without defenses (SmoothLLM, perplexity filter, etc). Snapshot each run to track technique-vs-model ASR movement over time.", "version": "0.1", "x-build-id": "vs5dtzdlF75Czl119" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~jailbreakbench-llm-robustness-leaderboard-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-jailbreakbench-llm-robustness-leaderboard-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/jungle_synthesizer~jailbreakbench-llm-robustness-leaderboard-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-jailbreakbench-llm-robustness-leaderboard-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/jungle_synthesizer~jailbreakbench-llm-robustness-leaderboard-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-jailbreakbench-llm-robustness-leaderboard-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", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "resumeCursor": { "title": "Resume cursor (continue a previous run)", "type": "string", "description": "Leave empty for a fresh crawl. To CONTINUE a previous run where it stopped — without paying again for records you already received — paste the `resumeCursor` value from that run's Output (the run's OUTPUT key). Resume promptly: the previous run's data expires with your account's retention window (free tier: your ~10 most recent runs)." }, "leaderboardType": { "title": "Leaderboard", "enum": [ "both", "open_source", "closed_source" ], "type": "string", "description": "Which JailbreakBench leaderboard table(s) to scrape.", "default": "both" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape" } } }, "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 } } } } } } } } } } ```