CVE-2025-0508

What it actually does

The affected SageMaker Python SDK workflow component uses MD5 to derive identifiers for workflow or pipeline configurations. Because MD5 is collision-prone, two different workflow configurations can theoretically produce the same hash value. If that hash is used to determine whether prior workflow results can be reused, a colliding configuration may cause the SDK or pipeline logic to treat distinct inputs as equivalent, leading to incorrect reuse, replacement, or mis-association of workflow artifacts. The primary impact is integrity loss in machine-learning pipeline execution; confidentiality and availability are not the main concerns. Exploitation is considered difficult because practical abuse requires creating or inducing colliding workflow inputs that affect the hash-reuse path.

Why it matters to the business

Organizations using affected SageMaker workflow functionality may receive incorrect machine-learning pipeline outputs, stale artifacts, or results generated from the wrong configuration. This can undermine trust in model training, data processing, validation, deployment decisions, and auditability of ML workflows. The issue is not known to be exploited in the wild, is not listed in CISA KEV, has low EPSS likelihood, and requires high attack complexity, but it is important for environments where model integrity, regulated data processing, reproducibility, or automated deployment decisions are business-critical. The listed package data also includes Kubernetes with a fixed version of 1.27.0-alpha.1; security teams should verify whether that product mapping is relevant to their dependency inventory before treating it as in-scope for this SageMaker-specific weakness.

How an attack unfolds

An attacker first needs influence over workflow definitions, pipeline configuration, parameters, or artifacts that contribute to the SDK's MD5-derived workflow identifier. They then craft or introduce a different configuration that produces the same MD5 hash as an existing workflow state. When the affected workflow component evaluates the new configuration, it may incorrectly conclude that previous results are valid for the new configuration and reuse, replace, or associate outputs under the wrong workflow identity. The attacker’s goal is not typically code execution; it is to manipulate the integrity of ML pipeline outcomes by causing incorrect cached results, stale processing outputs, or unintended workflow replacement. Because generating useful MD5 collisions in the required structured inputs is non-trivial and exploit maturity is reported as none, opportunistic exploitation is unlikely, but targeted manipulation is plausible where attackers or untrusted contributors can affect pipeline definitions.

How to fix it

Upgrade affected dependencies to fixed versions. For aws/sagemaker-python-sdk from source, use commit dcdd99f911e8b1a05d19cf1ad939b0fefae47864 or a release that includes that fix. If Kubernetes appears in dependency inventory because of this advisory data, verify applicability and upgrade k8s.io/kubernetes to 1.27.0-alpha.1 or later where relevant. Rebuild and redeploy ML workflow containers, notebooks, build images, and automation environments that vendor or pin the affected SDK. Regenerate workflow caches or invalidate previously derived workflow identifiers where result reuse could affect integrity. For high-assurance pipelines, force fresh execution of critical workflows after upgrading, especially for model training, evaluation, feature generation, and deployment gates. If immediate upgrade is not possible, restrict who can alter pipeline definitions or workflow parameters, disable or reduce trust in cached result reuse where feasible, require code review for workflow changes, and compare pipeline configuration digests using a collision-resistant hash such as SHA-256 in surrounding validation controls.

How to confirm the fix worked

Confirm installed versions or pinned commits for aws/sagemaker-python-sdk across development workstations, CI/CD images, notebook images, workflow runners, and production ML automation. Verify that the deployed SDK includes commit dcdd99f911e8b1a05d19cf1ad939b0fefae47864 or a later fixed release. Review dependency lockfiles, container SBOMs, requirements files, and build logs for affected package versions. Execute representative SageMaker workflows after upgrade and confirm that distinct workflow configurations no longer share MD5-based cache or workflow identifiers. Where practical, create two intentionally different pipeline configurations and verify that result reuse is not based on an MD5-only identifier. Confirm that historical cached workflow artifacts used for important decisions are invalidated or re-executed. For Kubernetes dependency findings, validate whether k8s.io/kubernetes is truly present and relevant; if so, confirm version 1.27.0-alpha.1 or later.

Typical attack path

1. Identify an organization or project using affected aws/sagemaker-python-sdk workflow or pipeline functionality.
2. Gain the ability to submit, modify, or influence SageMaker pipeline definitions, workflow parameters, configuration files, or generated artifacts used in workflow hashing.
3. Analyze how the affected SDK serializes workflow configuration data before applying MD5.
4. Create or induce a distinct workflow configuration that collides with the MD5 hash of an existing workflow state.
5. Submit the colliding workflow configuration through normal pipeline creation or update mechanisms.
6. Trigger pipeline execution or result lookup so the system reuses or replaces results associated with the colliding hash.
7. Leverage the incorrect workflow association to produce misleading pipeline outputs, stale artifacts, or erroneous model-processing outcomes.

How to detect it

There are no public, reliable indicators of active exploitation for this issue. Detection should focus on integrity anomalies and suspicious workflow-change patterns. Review CloudTrail management events for SageMaker API activity such as pipeline creation, pipeline updates, execution starts, and pipeline definition changes, especially from unusual principals, source IPs, automation roles, or times. Examine SageMaker pipeline execution history for cases where different pipeline definitions, parameters, or input artifacts resulted in unexpected cache hits, reused outputs, or unchanged result identifiers. Review source control history for modifications to workflow definitions, pipeline parameters, artifact paths, cache behavior, or hash/key-generation logic. Compare historical pipeline configuration snapshots against produced artifacts and model versions to identify mismatches. In CI/CD and notebook environments, search build logs and dependency lockfiles for affected aws/sagemaker-python-sdk versions or source installs before the fixed commit. For forensic review, correlate pipeline changes with model training outputs, evaluation metrics, artifact timestamps, and deployment approvals to identify cases where a changed workflow did not produce a corresponding fresh execution. Because exploitation would resemble legitimate workflow updates, high-confidence detection generally requires baseline knowledge of expected pipeline definitions and cache behavior rather than a single signature.

What your security team should do

• Inventory all uses of aws/sagemaker-python-sdk in notebooks, ML pipelines, CI/CD jobs, containers, and shared base images.
• Prioritize remediation for ML workflows where pipeline output affects model promotion, production deployment, regulated reporting, fraud decisions, safety decisions, or customer-facing automation.
• Upgrade aws/sagemaker-python-sdk to a version containing commit dcdd99f911e8b1a05d19cf1ad939b0fefae47864 and rebuild dependent runtime images.
• Invalidate or re-run critical cached SageMaker pipeline results produced by affected versions when integrity matters.
• Restrict write access to pipeline definitions, workflow configuration repositories, and automation that can submit SageMaker workflow changes.
• Add review controls for changes to workflow parameters, cache keys, artifact naming, and result-reuse behavior.
• Search repositories and images for direct MD5 use in workflow identity, cache-key, or artifact-selection logic beyond the affected SDK.
• Monitor SageMaker pipeline update and execution activity for unexpected configuration changes followed by suspicious result reuse.
• Treat the Kubernetes affected-product entry as requiring verification before action; remediate only if the dependency is confirmed in the organization’s software inventory.
• Document the integrity risk and remediation status for ML governance, audit, and model risk management stakeholders.