CVE-2026-83551 in Sagemaker Python Sdk
Summary
by MITRE • 09/01/2026
Cleartext storage of sensitive information in the @step and @remote decorator pipeline component in Amazon SageMaker Python SDK before v3.11.0 and v2.256.0 might allow an authenticated remote user to extract the HMAC signing key from SageMaker DescribePipeline API responses and forge valid integrity signatures for specially crafted function payloads, achieving code execution in another user's pipeline execution context within the same AWS account.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Analysis
by VulDB Data Team • 09/01/2026
The vulnerability identified involves the cleartext storage of sensitive cryptographic material within specific components of the Amazon SageMaker Python SDK prior to versions 3.11.0 and v2.256.0. This flaw is situated in the pipeline component logic, specifically affecting the step and remote decorators which are used by developers to define and execute machine learning workflows. The core technical issue arises from how these components handle authentication tokens and signing keys during their operational lifecycle. Instead of securely managing or obfuscating the HMAC signing key required for integrity verification, the SDK stores this sensitive information in plaintext within memory structures that can be accessed via standard API calls. This design oversight creates a significant security gap where authenticated users with access to pipeline metadata can retrieve credentials intended only for internal service communication.
From an operational perspective, this vulnerability allows any user who has been granted permission to describe or inspect pipelines within the same AWS account to extract the HMAC signing key through SageMaker DescribePipeline API responses. The extraction process does not require elevated privileges beyond standard read access to pipeline definitions, making it relatively easy for a malicious actor with legitimate but limited access to exploit this weakness. Once the attacker obtains the signing key, they can generate valid integrity signatures for arbitrary function payloads. This capability effectively bypasses the security controls designed to ensure that only authorized and verified code executes within the SageMaker environment. The ability to forge these signatures means an attacker can inject malicious logic into a pipeline execution context without detection by the platform's built-in validation mechanisms.
The impact of this vulnerability is severe, as it leads directly to unauthorized code execution in another user’s pipeline execution context. By crafting specially designed payloads that include forged integrity signatures, an attacker can execute arbitrary commands or scripts under the identity and permissions associated with the targeted pipeline. This represents a critical breach of isolation between different users’ workloads within the same AWS account. The consequences extend beyond simple data exposure; they encompass potential privilege escalation if the compromised pipeline runs with high-privilege roles, unauthorized access to sensitive datasets processed by the pipeline, and complete compromise of the machine learning infrastructure’s integrity. Attackers could use this vector to exfiltrate training data, manipulate model outputs for adversarial purposes, or establish persistent backdoors within the cloud environment.
This issue aligns closely with CWE-312 Cleartext Storage of Sensitive Information, as it involves the improper handling and storage of cryptographic keys in a readable format that is accessible via API responses. Furthermore, the exploitation technique maps to ATT&CK Tactic TA0005 Defense Evasion and specifically Technique T1610 Drop Web Shell or more broadly T1498 Network Denial of Service if used destructively, but primarily it facilitates lateral movement and privilege escalation akin to T1078 Valid Accounts. The attacker leverages valid credentials to access resources they should not be able to manipulate fully, thereby evading detection systems that rely on signature verification for integrity checks.
Mitigation strategies must focus immediately on upgrading the Amazon SageMaker Python SDK to version 3.11.0 or later, where this vulnerability has been addressed by implementing secure handling of signing keys and preventing their exposure through API responses. Organizations should also enforce strict least-privilege access controls within AWS IAM policies to ensure that only essential personnel have permission to describe pipelines containing sensitive components. Additionally, auditing logs for unusual patterns in DescribePipeline API calls can help detect potential exploitation attempts before significant damage occurs. Implementing network segmentation and monitoring tools such as Amazon GuardDuty or AWS CloudTrail Insights may provide early warning signs of unauthorized access attempts targeting these specific SDK vulnerabilities. Regular security assessments and code reviews focusing on how third-party libraries handle sensitive data are critical to preventing similar issues in future deployments.