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CVE-2024-AI-002

Model inversion vulnerability enabling extraction of sensitive training data from ML models

HighCVSS 8.1Model InversionPrivacy Attack
Vulnerability Details

Timeline

  • • Discovered: April 8, 2024
  • • Reported: April 12, 2024
  • • Published: May 1, 2024
  • • Updated: May 20, 2024

Credit

Discovered by RFS during privacy assessment of healthcare ML systems.

CVSS v3.1 Score

8.1
AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H

Affected Systems

  • • TensorFlow Serving deployments
  • • PyTorch model servers
  • • MLflow model endpoints
  • • Custom ML API services
Technical Analysis

Vulnerability Description

CVE-2024-AI-002 is a model inversion vulnerability that allows attackers to extract sensitive training data from deployed machine learning models. The vulnerability exploits insufficient privacy protections in model inference APIs to reconstruct original training samples.

Root Cause

Lack of differential privacy mechanisms and excessive model confidence information exposure through prediction APIs, enabling gradient-based reconstruction attacks.

Exploitation Technique

Step 1: Model Probing

Attacker sends carefully crafted queries to the model API to gather confidence scores and gradient information about model predictions.

Step 2: Gradient Extraction

Using optimization techniques to extract gradient information from model responses, revealing patterns in the training data distribution.

Step 3: Data Reconstruction

Applying iterative reconstruction algorithms to generate synthetic samples that closely match original training data points.

Attack Scenario

# Model inversion attack example
import numpy as np
from sklearn.model_selection import train_test_split
# Query model with crafted inputs
for i in range(1000):
response = model_api.predict(crafted_input[i])
gradients.append(extract_gradient(response))
# Reconstruct training data
reconstructed_data = invert_model(gradients)
Impact Assessment
Confidentiality
HIGH
  • • Training data exposure
  • • Personal information leakage
  • • Proprietary data theft
Integrity
HIGH
  • • Model trust degradation
  • • Data authenticity questions
  • • Privacy guarantee violations
Availability
HIGH
  • • Service reputation damage
  • • Regulatory compliance issues
  • • Business continuity impact
Detection Methods

Query Pattern Analysis

  • •Unusual query sequences (87% accuracy)
  • •High-frequency API calls (91% accuracy)
  • •Gradient extraction patterns (74% accuracy)

Statistical Monitoring

  • •Confidence score anomalies (83% accuracy)
  • •Response time patterns (79% accuracy)
  • •Model behavior changes (68% accuracy)
Remediation Guidance

Immediate Actions

Differential Privacy

Implement differential privacy mechanisms to add noise to model outputs and prevent gradient extraction.

API Rate Limiting

Deploy aggressive rate limiting and query pattern detection to prevent systematic model probing.

Short-term Fixes

Output Sanitization

Limit confidence score precision and remove gradient information from API responses.

Query Monitoring

Implement comprehensive logging and monitoring of all model API interactions for anomaly detection.

Long-term Solutions

Privacy-Preserving ML

Adopt privacy-preserving machine learning techniques like federated learning and secure multi-party computation.

Model Architecture Review

Review and redesign model architectures to minimize information leakage while maintaining performance.

References & Resources
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