Secret leaks often occur when a sensitive piece of authentication data is stored with the source code of an application. Considering the source code is intended to be deployed across multiple assets, including source code repositories or application hosting servers, the secrets might get exposed to an unintended audience.
In most cases, trust boundaries are violated when a secret is exposed in a source code repository or an uncontrolled deployment environment. Unintended people who don’t need to know the secret might get access to it. They might then be able to use it to gain unwanted access to associated services or resources.
The trust issue can be more or less severe depending on the people’s role and entitlement.
Azure provide the ability to deploy models to online HTTP REST endpoints for use in real-time inferencing. Real-time inferencing is the process of using a deployed model and other transformations logics to predict values in real-time.
To access these endpoints, a user must authenticate with the service, using API keys, AML Tokens or Mantra ID.
Hardcoding an API key in code allows an attacker to freely use the service without any restrictions, while business logic might assume that the access is restricted to the owner of the API key.
Below are some real-world scenarios that illustrate some impacts of an attacker exploiting the key.
Using a leaked secret, an attacker may be able to make hundreds or thousands of authenticated calls to an online service. It is common for online services to enforce a rate limit to prevent their servers from being overwhelmed.
If an attacker is able to exceed a user-based rate limit, they may be able to cause a denial of service for the user. If this continues over a long period of time, the user may also be subject to additional fees or may have their account terminated.
Financial losses can occur when a secret is used to access a paid third-party-provided service and is disclosed as part of the source code of client applications. Having the secret, each user of the application will be able to use it without limit to use the third party service to their own need, including in a way that was not expected.
This additional use of the secret will lead to added costs with the service provider.
Moreover, when rate or volume limiting is set up on the provider side, this additional use can prevent the regular operation of the affected application. This might result in a partial denial of service for all the application’s users.
Revoke the secret
Revoke any leaked secrets and remove them from the application source code.
Before revoking the secret, ensure that no other applications or processes are using it. Other usages of the secret will also be impacted when the secret is revoked.
Use a secret vault
A secret vault should be used to generate and store the new secret. This will ensure the secret’s security and prevent any further unexpected disclosure.
Depending on the development platform and the leaked secret type, multiple solutions are currently available.
In general, prefer using Managed Identities or Service Principals to access Azure services. These identities are managed by Azure and do not require managing secrets.
data = {}
body = str.encode(json.dumps(data))
url = 'http://d93d04c2-2739-4bc1-96b6-example.westeurope.azurecontainer.io/score'
# Replace this with the primary/secondary key, AMLToken, or Microsoft Entra ID token for the endpoint
api_key = '2JcAqoFpeQZCXpiHFAHnCvLYbbSu51C5' # Noncompliant
if not api_key:
raise Exception("A key should be provided to invoke the endpoint")
headers = {'Content-Type':'application/json', 'Authorization':('Bearer' + api_key)}
req = urllib.request.Request(url, body, headers)
data = {}
body = str.encode(json.dumps(data))
url = 'http://d93d04c2-2739-4bc1-96b6-example.westeurope.azurecontainer.io/score'
# Replace this with the primary/secondary key, AMLToken, or Microsoft Entra ID token for the endpoint
api_key = os.getenv("PRIMARY_AZURE_ML_KEY")
if not api_key:
raise Exception("A key should be provided to invoke the endpoint")
headers = {'Content-Type':'application/json', 'Authorization':('Bearer' + api_key)}
req = urllib.request.Request(url, body, headers)
And retrieve them with the az CLI tool:
PRIMARY_AZURE_ML_KEY=$(az ml online-endpoint get-credentials -n $ENDPOINT_NAME --query primaryKey -o tsv ) SCORING_URL=$(az ml online-endpoint show -n $ENDPOINT_NAME --query scoring_uri -o tsv ) SWAGGER_URL=$(az ml online-endpoint show -n $ENDPOINT_NAME --query openapi_uri -o tsv )