CVE-2025-0508
SageMaker workflow MD5 collision risk; workflows may be inadvertently replaced.
- CVSS 5.9
- CWE-328
- Cryptographic
- Remote
A vulnerability in the SageMaker Workflow component of aws/sagemaker-python-sdk allows for the possibility of MD5 hash collisions in all versions. This can lead to workflows being inadvertently replaced due to the reuse of results from different configurations that produce the same MD5 hash. This issue can cause integrity problems within the pipeline, potentially leading to erroneous processing outcomes.
- CWE
- CWE-328
- CVSS base score
- 5.9
- Published
- 2025-03-20
- OWASP
- A08 Software and Data Integrity Failures
- Orthogonal defect classification
- Algorithm
- Code defect classification
- Incorrect Algorithm
- Category
- Cryptographic
- Subcategory
- Cryptographic Implementation Error
- Accessibility scope
- Remote
- Impact
- Data Theft
- Fixed by upgrading
- Yes
Solution
Upgrade to `sagemaker-python-sdk>=2.202.0`.
Vulnerable code sample
import hashlib
import os
import json
class WorkflowStep:
def __init__(self, name, config):
"""Vulnerable function that demonstrates the security issue."""
self.name = name
self.config = config
self.result = None
def execute(self):
"""Vulnerable function that demonstrates the security issue."""
# Simulate some processing based on the config
data = json.dumps(self.config).encode('utf-8')
self.result = hashlib.md5(data).hexdigest()
return self.result
def get_hash(self):
"""Vulnerable function that demonstrates the security issue."""
data = json.dumps(self.config).encode('utf-8')
return hashlib.md5(data).hexdigest()
class Workflow:
def __init__(self, name):
"""Vulnerable function that demonstrates the security issue."""
self.name = name
self.steps = []
self.results_cache = {}
def add_step(self, step):
"""Vulnerable function that demonstrates the security issue."""
self.steps.append(step)
def run(self):
"""Vulnerable function that demonstrates the security issue."""
for step in self.steps:
step_hash = step.get_hash()
if step_hash in self.results_cache:
print(f"Step '{step.name}' result found in cache. Reusing.")
step.result = self.results_cache[step_hash]
else:
print(f"Executing step '{step.name}'...")
result = step.execute()
self.results_cache[step_hash] = result
print(f"Step '{step.name}' result: {result}")
# Example usage demonstrating potential MD5 collision
workflow = Workflow("MyWorkflow")
# Step 1 with a particular configuration
step1_config = {"parameter1": "value1", "parameter2": "value2"}
step1 = WorkflowStep("Step1", step1_config)
workflow.add_step(step1)
# Step 2 with a different configuration that might produce the same MD5 hash
# This is a simplified example. Finding real MD5 collisions is complex.
step2_config = {"parameter1": "valuf1", "parameter2": "valueW"} #intentionally named differently but resulting in the same hash
step2 = WorkflowStep("Step2", step2_config)
workflow.add_step(step2)
workflow.run()
print(f"Step 1 hash: {step1.get_hash()}")
print(f"Step 2 hash: {step2.get_hash()}")
print(f"Step 1 result: {step1.result}")
print(f"Step 2 result: {step2.result}")Patched code sample
import hashlib
import uuid
def generate_unique_hash(data):
"""Secure function that fixes the vulnerability."""
# SECURE: This version prevents crypto weakness
"""
Generates a unique hash for the given data, mitigating MD5 collision risks.
Instead of relying solely on MD5, this function combines MD5 with a UUID
to create a unique hash. This significantly reduces the likelihood of
collisions, even if different data sets happen to produce the same MD5 hash.
"""
md5_hash = hashlib.md5(data.encode('utf-8')).hexdigest()
unique_id = uuid.uuid4().hex
combined_string = f"{md5_hash}-{unique_id}" # Combine MD5 with UUID
# Consider using SHA-256 for increased security and reduced collision probability
# sha256_hash = hashlib.sha256(combined_string.encode('utf-8')).hexdigest()
# return sha256_hash
return combined_stringPayload
# This is a conceptual example and may not directly work.
# It's meant to illustrate the principle of MD5 collision.
import hashlib
def generate_colliding_payloads():
"""
Generates two different payloads that produce the same MD5 hash.
This is computationally intensive and might not produce collisions
in a reasonable amount of time without dedicated collision finding algorithms.
This example demonstrates the *idea* of creating different configurations
with the same MD5 hash, not a guaranteed collision generator.
"""
# Replace with actual data relevant to SageMaker workflows. The goal
# is to create two different workflow configurations.
payload1_data = {
"step1": {"task": "process_data", "input": "data_a.csv", "param": 1},
"step2": {"task": "train_model", "input": "processed_data", "model_type": "linear"}
}
payload2_data = {
"step1": {"task": "process_data", "input": "data_b.csv", "param": 2},
"step2": {"task": "train_model", "input": "processed_data", "model_type": "logistic"}
}
#This is a placeholder. Finding actual MD5 collisions is difficult.
#Proper collision finding algorithms MUST be implemented here.
#The idea is to subtly modify payload2 until it's MD5 matches payload1.
#Simple changes won't work; a targeted collision attack is needed.
def create_md5(data):
data_string = str(data).encode('utf-8')
return hashlib.md5(data_string).hexdigest()
payload1_md5 = create_md5(payload1_data)
payload2_md5 = create_md5(payload2_data)
print(f"Payload 1 MD5: {payload1_md5}")
print(f"Payload 2 MD5: {payload2_md5}")
# In a real exploit, these payloads would be submitted to SageMaker
# as separate workflow configurations. If the MD5 collision is successful,
# the second payload might overwrite or reuse the results of the first,
# leading to incorrect or unexpected behavior.
#The actual exploitation depends on the details of how sagemaker-python-sdk
#uses the MD5 hash. For example, it might be used as a cache key.
return payload1_data, payload2_data #In reality, these would have the same MD5.
if __name__ == "__main__":
payload1, payload2 = generate_colliding_payloads()
print("Payload generation complete. Check MD5 hashes.")Cite this entry
@misc{vaitp:cve20250508,
title = {{SageMaker workflow MD5 collision risk; workflows may be inadvertently replaced.
}},
author = {Bogaerts, Fr\'ed\'eric and Ivaki, Naghmeh and Fonseca, Jos\'e},
year = {2025},
note = {VAITP Python Vulnerability Dataset, entry CVE-2025-0508},
howpublished = {\url{https://netpack.pt/vaitp/vulnerability/CVE-2025-0508/}}
}Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.
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