CVE-2026-1777: Amazon SageMaker Python SDK RCE Flaw

CVE-2026-1777 Overview

A sensitive data exposure vulnerability exists in the Amazon SageMaker Python SDK before versions v3.2.0 and v2.256.0. The vulnerability occurs because the ModelBuilder HMAC signing key is included in the cleartext response elements of the DescribeTrainingJob function. An attacker with permissions to both call this API and modify objects in the Training Jobs S3 output location may have the ability to upload arbitrary artifacts which are executed the next time the Training Job is invoked.

Critical Impact

Attackers with specific AWS permissions can leverage exposed HMAC signing keys to upload malicious artifacts that execute during subsequent Training Job invocations, potentially compromising ML pipelines and training infrastructure.

Affected Products

• Amazon SageMaker Python SDK versions prior to v3.2.0
• Amazon SageMaker Python SDK versions prior to v2.256.0
• Systems utilizing ModelBuilder with DescribeTrainingJob API calls

Discovery Timeline

• 2026-02-02 - CVE CVE-2026-1777 published to NVD
• 2026-02-03 - Last updated in NVD database

Technical Details for CVE-2026-1777

Vulnerability Analysis

This vulnerability is classified under CWE-319 (Cleartext Transmission of Sensitive Information). The core issue lies in the improper handling of cryptographic secrets within the SageMaker Python SDK's ModelBuilder component. When the DescribeTrainingJob API function is called, the response inadvertently includes the HMAC signing key in cleartext, exposing critical authentication material to any entity with API access permissions.

The exposure of HMAC signing keys creates a significant security concern because these keys are fundamental to ensuring the integrity and authenticity of artifacts within the training pipeline. An attacker who obtains the signing key can forge valid signatures for malicious payloads, effectively bypassing the integrity verification mechanisms designed to protect the training workflow.

Root Cause

The root cause is insufficient protection of sensitive cryptographic material in API responses. The DescribeTrainingJob function returns the ModelBuilder HMAC signing key as part of its response payload without proper redaction or encryption. This represents a design flaw where sensitive secrets are included in diagnostic or informational API responses that should only contain metadata about training jobs.

Attack Vector

Exploitation of this vulnerability requires a multi-step attack chain. First, an attacker must have AWS IAM permissions to call the DescribeTrainingJob API. Second, they need write access to the S3 bucket designated as the Training Job's output location. With these permissions in place, the attacker can:

1. Call the DescribeTrainingJob API to extract the cleartext HMAC signing key
2. Use the extracted key to sign malicious artifacts with valid signatures
3. Upload these signed malicious artifacts to the S3 output location
4. Wait for the Training Job to be invoked, at which point the malicious artifacts execute

This attack vector is network-based and does not require user interaction. The requirement for elevated AWS permissions (specifically IAM access to the DescribeTrainingJob API and S3 write permissions) limits the attack surface to insider threats or scenarios where AWS credentials have already been compromised.

Detection Methods for CVE-2026-1777

Indicators of Compromise

• Unexpected or unauthorized calls to the DescribeTrainingJob API, particularly from unusual source IPs or principals
• Anomalous modifications to artifacts in Training Job S3 output buckets that don't correlate with legitimate training activities
• Training Job executions exhibiting unexpected behavior, network connections, or resource utilization patterns
• CloudTrail logs showing correlation between DescribeTrainingJob calls and subsequent S3 PUT operations to training output locations

Detection Strategies

• Enable and monitor AWS CloudTrail logging for all sagemaker:DescribeTrainingJob API calls
• Implement S3 bucket access logging and monitor for write operations from unexpected IAM principals
• Deploy SentinelOne Singularity Cloud Security to detect suspicious API activity patterns and anomalous resource access
• Configure alerts for any modification to Training Job S3 output buckets outside of active training windows

Monitoring Recommendations

• Establish baseline patterns for DescribeTrainingJob API usage and alert on deviations
• Monitor SageMaker Training Job execution logs for signs of malicious artifact execution
• Implement continuous integrity verification for artifacts in Training Job S3 buckets
• Use AWS GuardDuty in conjunction with SentinelOne for comprehensive cloud threat detection

How to Mitigate CVE-2026-1777

Immediate Actions Required

• Upgrade Amazon SageMaker Python SDK to version v3.2.0 or later (or v2.256.0 or later for the 2.x branch)
• Audit AWS IAM policies to restrict DescribeTrainingJob API access to only essential principals
• Review CloudTrail logs for any unauthorized access to DescribeTrainingJob API prior to patching
• Verify integrity of existing artifacts in Training Job S3 output locations

Patch Information

AWS has released patched versions of the SageMaker Python SDK that address this vulnerability. The fix is available in versions v3.2.0 and v2.256.0. Organizations should update their SDK dependencies immediately. For detailed information, refer to the AWS Security Bulletin 2026-004 and the GitHub Security Advisory GHSA-rjrp-m2jw-pv9c.

Workarounds

• Implement strict IAM policies using least-privilege principles to limit access to the DescribeTrainingJob API
• Enable S3 bucket versioning and Object Lock on Training Job output buckets to prevent unauthorized modifications
• Use AWS VPC endpoints to restrict SageMaker API access to trusted network boundaries
• Deploy additional integrity verification mechanisms independent of the SDK's built-in signing

# Upgrade SageMaker Python SDK to patched version
pip install --upgrade sagemaker>=3.2.0

# Or for 2.x branch compatibility
pip install --upgrade "sagemaker>=2.256.0,<3.0.0"

# Verify installed version
pip show sagemaker | grep Version