CVE-2026-1778 is a vulnerability in the AWS SageMaker Python SDK affecting versions before 3.1.1 and 2.256.0. The issue arises from the SDK disabling TLS certificate verification for HTTPS connections when importing Triton Python models. This improper certificate validation (CWE-295) means that the SDK accepts connections with invalid, expired, or self-signed certificates without raising errors. As a result, an attacker capable of intercepting network traffic between the client and the model repository or endpoint can perform a man-in-the-middle attack, injecting malicious payloads or tampering with the model data during import. The vulnerability does not compromise confidentiality directly since data is still encrypted, but it severely impacts integrity by allowing unauthorized modification of models. The CVSS 3.1 score is 5.9 (medium), reflecting network attack vector, high attack complexity, no privileges required, no user interaction, unchanged scope, no confidentiality impact, high integrity impact, and no availability impact. No known exploits are reported in the wild yet. The vulnerability affects organizations using AWS SageMaker for machine learning workflows, particularly those importing Triton models via the Python SDK. The lack of certificate validation undermines the trust model of TLS, exposing AI/ML pipelines to supply chain attacks or data poisoning. AWS has released fixed versions 3.1.1 and 2.256.0 to address this issue, though no direct patch links are provided in the data. Organizations should upgrade promptly and audit their ML deployment pipelines for similar misconfigurations.

For European organizations, this vulnerability poses a significant risk to the integrity of machine learning workflows that rely on AWS SageMaker and Triton models. Attackers exploiting this flaw could inject malicious code or manipulate model parameters during import, potentially leading to incorrect model predictions, data poisoning, or backdoors in AI systems. This can affect sectors such as finance, healthcare, manufacturing, and critical infrastructure where AI-driven decisions are increasingly integrated. The impact on confidentiality is minimal, but the integrity compromise could lead to financial losses, regulatory non-compliance, reputational damage, and operational disruptions. Since the vulnerability does not require authentication or user interaction, attackers with network access (e.g., insider threats or compromised network segments) can exploit it. The medium severity rating reflects the high attack complexity, but the potential consequences in sensitive environments warrant urgent attention. European organizations using older SDK versions must prioritize remediation to maintain trust in their AI/ML pipelines.

1. Upgrade the AWS SageMaker Python SDK to version 3.1.1 or later (or 2.256.0 or later) immediately to restore proper TLS certificate validation. 2. Enforce strict TLS certificate validation policies in all machine learning workflows, including custom scripts and CI/CD pipelines. 3. Use network segmentation and secure VPNs to limit exposure of ML infrastructure to untrusted networks, reducing the risk of MITM attacks. 4. Monitor network traffic for unusual HTTPS connections or certificate anomalies during model imports. 5. Conduct regular security audits of AI/ML supply chains, verifying the authenticity and integrity of imported models. 6. Implement runtime integrity checks on models to detect unauthorized modifications post-import. 7. Educate development and DevOps teams about the risks of disabling TLS verification and the importance of secure coding practices in ML environments. 8. Consider using AWS PrivateLink or VPC endpoints to isolate SageMaker traffic within private networks. 9. Review and update incident response plans to include scenarios involving ML model tampering. 10. Collaborate with AWS support for guidance and to stay informed about any emerging threats related to this vulnerability.