> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://hiddenlayer.ferndocs.com/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://hiddenlayer.ferndocs.com/_mcp/server. # Scan a remote model POST https://api.hiddenlayer.ai/scan/v3/jobs Content-Type: application/json; charset=utf-8 Reference: https://hiddenlayer.ferndocs.com/api-reference/llm-proxy-api/jobs/request ## Authentication - `Authorization` header (bearer token, required) — Bearer authentication of the form `Bearer `, where token is your auth token. ## Servers - `https://api.hiddenlayer.ai` (ProdUs, default) - `https://api.eu.hiddenlayer.ai` (ProdEu) ## Request ### Body (application/json; charset=utf-8) This endpoint expects a ScanJobRequest. - `inventory` (ScanJobRequestInventory, required) - `access` (ScanJobRequestAccess, required) — Access method for the location of files associated with the scan - `scan_id` (string, optional) — unique identifier for the scan - `status` (enum, optional) — Status of the scan - Allowed values: `pending`, `running`, `done`, `failed`, `canceled` ## Response ### 201 Scan job request was accepted - `inventory` (ScanJobInventory, required) - `scan_id` (string, optional) — unique identifier for the scan - `status` (enum, optional) — Status of the scan - Allowed values: `pending`, `running`, `done`, `failed`, `canceled` ## Errors ### 400 Bad Request Error The request failed due to a client error, with one or more of the following possible causes: 1. The request required a tenant_id field, which was missing. 2. The request was malformed syntactically or semantically. - `any` ### 422 Unprocessable Entity Error Validation Error - `errors` (list of ProblemDetailsErrorsItems, required) — Error details - `type` (string, optional) — https://www.rfc-editor.org/rfc/rfc9457.html#name-type - `title` (string, optional) — https://www.rfc-editor.org/rfc/rfc9457.html#name-title - `detail` (string, optional) — https://www.rfc-editor.org/rfc/rfc9457.html#name-detail - `instance` (string, optional) — https://www.rfc-editor.org/rfc/rfc9457.html#name-instance ## Types ### ScanJobRequestInventory - `model_name` (string, required) — Name of the model - `model_version` (string, required) — If you do not provide a version, one will be generated for you. - `requesting_entity` (string, required) — Entity that requested the scan - `scan_target` (ScanJobRequestInventoryScanTarget, optional) — Specifies what to scan. Must provide at least one of: deep_scan with file location details, provider_details, or both. - `request_source` (enum, optional) — Identifies the system that requested the scan - Allowed values: `Hybrid Upload`, `API Upload`, `Integration`, `UI Upload`, `AI Asset Discovery` - `origin` (string, optional) — Specifies the platform or service where the model originated before being scanned - `requested_scan_location` (string, optional, deprecated) — **DEPRECATED**: Use `scan_target` instead. Location of files to be scanned. Maintained for backwards compatibility. If both `requested_scan_location` and `scan_target` are provided, `scan_target` takes precedence. ### ScanJobRequestAccess Access method for the location of files associated with the scan - `source` (enum, optional) - Allowed values: `LOCAL`, `AWS_PRESIGNED`, `AWS_IAM_ROLE`, `AZURE_BLOB_SAS`, `AZURE_BLOB_AD`, `GOOGLE_SIGNED`, `GOOGLE_OAUTH`, `HUGGING_FACE`, `NONE` ### ScanJobInventory - `model_name` (string, required) — Name of the model - `model_version` (string, required) — If you do not provide a version, one will be generated for you. - `requesting_entity` (string, required) — Entity that requested the scan - `scan_target` (ScanJobInventoryScanTarget, optional) — Specifies what to scan. Must provide at least one of: deep_scan with file location details, provider_details, or both. - `request_source` (enum, optional) — Identifies the system that requested the scan - Allowed values: `Hybrid Upload`, `API Upload`, `Integration`, `UI Upload`, `AI Asset Discovery` - `origin` (string, optional) — Specifies the platform or service where the model originated before being scanned - `requested_scan_location` (string, optional, deprecated) — **DEPRECATED**: Use `scan_target` instead. Location of files to be scanned. Maintained for backwards compatibility. If both `requested_scan_location` and `scan_target` are provided, `scan_target` takes precedence. ### ProblemDetailsErrorsItems - `code` (string, optional) - `detail` (string, optional) - `pointer` (list of string, optional) — array of JSON Pointers ### ScanJobRequestInventoryScanTarget Specifies what to scan. Must provide at least one of: deep_scan with file location details, provider_details, or both. ### ScanJobInventoryScanTarget Specifies what to scan. Must provide at least one of: deep_scan with file location details, provider_details, or both. ## Examples **Request** ```json { "inventory": { "model_name": "keras-tf-2025-05-27", "model_version": "1.0.0", "requesting_entity": "string" } } ``` **Response** ```json { "inventory": { "model_name": "keras-tf-2025-05-27", "model_version": "1.0.0", "requesting_entity": "string", "scan_target": null, "request_source": "Hybrid Upload", "origin": "Hugging Face", "requested_scan_location": "/files-to-scan" }, "scan_id": "00000000-0000-0000-0000-000000000000", "status": "pending" } ``` **SDK Code** ```python import requests url = "https://api.hiddenlayer.ai/scan/v3/jobs" payload = "{\n \"inventory\": {\n \"model_name\": \"keras-tf-2025-05-27\",\n \"model_version\": \"1.0.0\",\n \"requesting_entity\": \"string\"\n }\n}" headers = { "Authorization": "Bearer ", "Content-Type": "application/json; charset=utf-8" } response = requests.post(url, data=payload, headers=headers) print(response.json()) ``` ```javascript const url = 'https://api.hiddenlayer.ai/scan/v3/jobs'; const options = { method: 'POST', headers: { Authorization: 'Bearer ', 'Content-Type': 'application/json; charset=utf-8' }, body: '{\n "inventory": {\n "model_name": "keras-tf-2025-05-27",\n "model_version": "1.0.0",\n "requesting_entity": "string"\n }\n}' }; try { const response = await fetch(url, options); const data = await response.json(); console.log(data); } catch (error) { console.error(error); } ``` ```go package main import ( "fmt" "strings" "net/http" "io" ) func main() { url := "https://api.hiddenlayer.ai/scan/v3/jobs" payload := strings.NewReader("{\n \"inventory\": {\n \"model_name\": \"keras-tf-2025-05-27\",\n \"model_version\": \"1.0.0\",\n \"requesting_entity\": \"string\"\n }\n}") req, _ := http.NewRequest("POST", url, payload) req.Header.Add("Authorization", "Bearer ") req.Header.Add("Content-Type", "application/json; charset=utf-8") res, _ := http.DefaultClient.Do(req) defer res.Body.Close() body, _ := io.ReadAll(res.Body) fmt.Println(res) fmt.Println(string(body)) } ``` ```ruby require 'uri' require 'net/http' url = URI("https://api.hiddenlayer.ai/scan/v3/jobs") http = Net::HTTP.new(url.host, url.port) http.use_ssl = true request = Net::HTTP::Post.new(url) request["Authorization"] = 'Bearer ' request["Content-Type"] = 'application/json; charset=utf-8' request.body = "{\n \"inventory\": {\n \"model_name\": \"keras-tf-2025-05-27\",\n \"model_version\": \"1.0.0\",\n \"requesting_entity\": \"string\"\n }\n}" response = http.request(request) puts response.read_body ``` ```java import com.mashape.unirest.http.HttpResponse; import com.mashape.unirest.http.Unirest; HttpResponse response = Unirest.post("https://api.hiddenlayer.ai/scan/v3/jobs") .header("Authorization", "Bearer ") .header("Content-Type", "application/json; charset=utf-8") .body("{\n \"inventory\": {\n \"model_name\": \"keras-tf-2025-05-27\",\n \"model_version\": \"1.0.0\",\n \"requesting_entity\": \"string\"\n }\n}") .asString(); ``` ```php request('POST', 'https://api.hiddenlayer.ai/scan/v3/jobs', [ 'body' => '{ "inventory": { "model_name": "keras-tf-2025-05-27", "model_version": "1.0.0", "requesting_entity": "string" } }', 'headers' => [ 'Authorization' => 'Bearer ', 'Content-Type' => 'application/json; charset=utf-8', ], ]); echo $response->getBody(); ``` ```csharp using RestSharp; var client = new RestClient("https://api.hiddenlayer.ai/scan/v3/jobs"); var request = new RestRequest(Method.POST); request.AddHeader("Authorization", "Bearer "); request.AddHeader("Content-Type", "application/json; charset=utf-8"); request.AddParameter("application/json; charset=utf-8", "{\n \"inventory\": {\n \"model_name\": \"keras-tf-2025-05-27\",\n \"model_version\": \"1.0.0\",\n \"requesting_entity\": \"string\"\n }\n}", ParameterType.RequestBody); IRestResponse response = client.Execute(request); ``` ```swift import Foundation let headers = [ "Authorization": "Bearer ", "Content-Type": "application/json; charset=utf-8" ] let postData = NSData(data: "{ "inventory": { "model_name": "keras-tf-2025-05-27", "model_version": "1.0.0", "requesting_entity": "string" } }".data(using: String.Encoding.utf8)!) let request = NSMutableURLRequest(url: NSURL(string: "https://api.hiddenlayer.ai/scan/v3/jobs")! as URL, cachePolicy: .useProtocolCachePolicy, timeoutInterval: 10.0) request.httpMethod = "POST" request.allHTTPHeaderFields = headers request.httpBody = postData as Data let session = URLSession.shared let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in if (error != nil) { print(error as Any) } else { let httpResponse = response as? HTTPURLResponse print(httpResponse) } }) dataTask.resume() ```