Threat Intelligence Database

Feast is the open source feature store for AI and machine learning. Prior to 0.65.0, .github/workflows/pr_integration_tests.yml uses pull_request_target with the synchronize event and preserves ok-to-test, approved, or lgtm labels across newly pushed commits, allowing a fork contributor to obtain approval for a benign revision and then run changed code from refs/pull/${{ github.event.pull_request.number }}/merge through privileged make targets. The job exposes GCP, AWS, and Snowflake credentials to that code, enabling runner code execution, credential disclosure, and possible access to downstream cloud resources. An external label-removal integration could mitigate the condition, but no repository workflow provided that protection. This issue is fixed in version 0.65.0.

Feast through 0.66.0 fails to verify JWT token signatures before establishing user identity, allowing attackers to bypass all role-based access control by presenting an unverified token with a hardcoded claim value. Attackers can obtain trusted internal identity and gain unchecked read and write access to all entities, feature views, data sources, and permission policies on the server.

Feast before 0.63.0 contains an unsafe deserialization vulnerability that allows unauthenticated or unauthorized attackers to achieve remote code execution by sending a crafted gRPC request to the registry server. The user_defined_function.body field of an OnDemandFeatureView spec is decoded from base64 and passed to dill.loads() before any authorization check is performed, enabling attackers to embed a malicious serialized Python object with an arbitrary __reduce__ method to execute OS commands as the feast service account.