DiffusionGuard: Protecting Healthcare LLMs from Data Poisoning via Iterative Knowledge Graph Diffusion
Abstract
A novel defense mechanism against data poisoning in medical LLMs using iterative diffusion models to detect and filter malicious training data. The approach combines knowledge graph validation with discrete diffusion modeling to create a robust verification layer that can identify and neutralize poisoned data before model training.
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Research Gap Analysis
Current approaches focus on post-generation validation of LLM outputs, but don't address the fundamental vulnerability during training. No existing solution combines diffusion models with knowledge graphs for proactive defense against data poisoning.
DiffusionGuard: Protecting Healthcare LLMs from Data Poisoning via Iterative Knowledge Graph Diffusion
Motivation
Recent research has shown that medical LLMs are vulnerable to data poisoning attacks, where just 0.001% of corrupted training data can lead to harmful model outputs. While existing approaches use knowledge graphs for post-generation validation, there's no robust solution for detecting poisoned data during the training phase. Additionally, recent advances in discrete diffusion models for LLMs offer new possibilities for iterative refinement that haven't been explored in the context of security.
Proposed Approach
DiffusionGuard introduces a novel three-stage defense mechanism:
- Knowledge Graph Embedding Layer
- Convert biomedical knowledge graphs into dense vector representations
- Create a diffusion prior that encodes valid medical relationships
- Establish confidence thresholds for legitimate medical knowledge
- Iterative Diffusion Verification
- Apply discrete diffusion processes to training data chunks
- Gradually denoise data while comparing against KG embeddings
- Flag suspicious patterns that deviate from established medical knowledge
- Adaptive Defense Mechanism
- Implement dynamic thresholding based on content domain
- Use reinforcement learning to optimize detection parameters
- Maintain a feedback loop for continuous defense improvement
Expected Outcomes
- Reduction in successful poisoning attacks by >95%
- Minimal impact on legitimate training data (<1% false positives)
- Scalable solution that can process large training datasets efficiently
- Framework adaptable to different medical specialties and knowledge domains
Potential Applications
- Medical LLM training security
- Clinical decision support systems
- Drug discovery pipelines
- Healthcare chatbot safety
- Medical education platforms
The system would be particularly valuable for organizations developing specialized healthcare LLMs where data integrity is crucial for patient safety.
Proposed Methodology
Implement a three-stage defense system using knowledge graph embeddings, discrete diffusion processes, and adaptive verification mechanisms to detect and filter poisoned training data before it affects model training.
Potential Impact
Could significantly improve the safety and reliability of medical AI systems, reducing the risk of harmful misinformation while maintaining model performance. Has broader implications for secure AI development in other critical domains.