MLflow 信息泄露漏洞

MLFlow is an open-source platform that simplifies machine learning development. It includes features for tracking experiments, packaging code for reproducible runs, and sharing and deploying models. However, MLFlow has a vulnerability related to information leakage. This vulnerability stems from...

SUSE CVE-2026-31866

flagd is a feature flag daemon with a Unix philosophy. Prior to 0.14.2, flagd exposes OFREP /ofrep/v1/evaluate/... and gRPC evaluation.v1, evaluation.v2 endpoints for feature flag evaluation. These endpoints are designed to be publicly accessible by client applications. The evaluation context...

CVE-2026-31866

flagd is a feature flag daemon with a Unix philosophy. Prior to 0.14.2, flagd exposes OFREP /ofrep/v1/evaluate/... and gRPC evaluation.v1, evaluation.v2 endpoints for feature flag evaluation. These endpoints are designed to be publicly accessible by client applications. The evaluation context...

CVE-2026-31866 Allocation of Resources Without Limits or Throttling in flagd

flagd is a feature flag daemon with a Unix philosophy. Prior to 0.14.2, flagd exposes OFREP /ofrep/v1/evaluate/... and gRPC evaluation.v1, evaluation.v2 endpoints for feature flag evaluation. These endpoints are designed to be publicly accessible by client applications. The evaluation context...

CVE-2026-31866 Allocation of Resources Without Limits or Throttling in flagd

flagd is a feature flag daemon with a Unix philosophy. Prior to 0.14.2, flagd exposes OFREP /ofrep/v1/evaluate/... and gRPC evaluation.v1, evaluation.v2 endpoints for feature flag evaluation. These endpoints are designed to be publicly accessible by client applications. The evaluation context...

Allocation of Resources Without Limits or Throttling

Overview Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the evaluation endpoints, including /ofrep/v1/evaluate/flags/flagKey, /ofrep/v1/evaluate/flags, and various gRPC methods. An attacker can cause memory exhaustion and process...

Allocation of Resources Without Limits or Throttling

Overview Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the evaluation endpoints, including /ofrep/v1/evaluate/flags/flagKey, /ofrep/v1/evaluate/flags, and various gRPC methods. An attacker can cause memory exhaustion and process...

Allocation of Resources Without Limits or Throttling

Overview Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the evaluation endpoints, including /ofrep/v1/evaluate/flags/flagKey, /ofrep/v1/evaluate/flags, and various gRPC methods. An attacker can cause memory exhaustion and process...

Allocation of Resources Without Limits or Throttling

Overview Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the evaluation endpoints, including /ofrep/v1/evaluate/flags/flagKey, /ofrep/v1/evaluate/flags, and various gRPC methods. An attacker can cause memory exhaustion and process...

flagd Vulnerable to Allocation of Resources Without Limits or Throttling

Details flagd exposes OFREP /ofrep/v1/evaluate/... and gRPC evaluation.v1, evaluation.v2 endpoints for feature flag evaluation. These endpoints are designed to be publicly accessible by client applications. The evaluation context included in request payloads is read into memory without any size...

Allocation of Resources Without Limits or Throttling

Overview Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the evaluation endpoints, including /ofrep/v1/evaluate/flags/flagKey, /ofrep/v1/evaluate/flags, and various gRPC methods. An attacker can cause memory exhaustion and process...

Tencent TFace 代码问题漏洞

Tencent TFace is a deep learning research platform focusing on face recognition from China's Tencent Tencent. Tencent TFace suffers from a code issue vulnerability that stems from a lack of validation of user-supplied data in eval endpoints, which could lead to deserialization of untrustworthy da...