Adversarial Robustness in Vision-Language Models

Comprehensive analysis of attack vectors, defense mechanisms, and robustness evaluation for multimodal AI systems

Overview

This section explores the security landscape of Vision-Language Models, with particular focus on medical applications where adversarial robustness is critical for patient safety. We examine attack taxonomies, evaluation frameworks, and defense strategies that form the foundation of secure VLM deployment in healthcare.

📚 Chapter Structure

Foundations

1. Attack Fundamentals — Comprehensive introduction to VLM attacks and taxonomies
2. Theoretical Foundations — Mathematical basis for adversarial vulnerabilities

Attack Methods

3. Black-box Attacks — Query-based and transfer attack strategies
4. Robustness Evaluation — Comprehensive evaluation frameworks and metrics

Defense Strategies

5. Defense Mechanisms — Robust training and protection methods
6. Medical VLM Security — Healthcare-specific security considerations

Attack Categories

1. Visual Attacks

Pixel-level Perturbations

• FGSM (Fast Gradient Sign Method)
• PGD (Projected Gradient Descent)
• C&W (Carlini-Wagner)
• AutoAttack ensemble

Patch-based Attacks

• Adversarial patches
• Universal perturbations
• Physical-world attacks

2. Text-based Attacks

Prompt Injection

• Direct injection
• Indirect/chained injection
• Context hijacking

Jailbreaking

• Role-play attacks
• Instruction following exploits
• Safety bypass techniques

3. Multimodal Attacks

Cross-modal Exploitation

• Image-text misalignment
• Attention manipulation
• Feature space attacks

Semantic Attacks

• Context confusion
• Object relationship manipulation
• Scene understanding disruption

Defense Mechanisms

Preprocessing Defenses

1. Input Sanitization

   • Adversarial detection
   • Input reconstruction
   • Randomized smoothing

2. Augmentation-based

   • Test-time augmentation
   • Adversarial training
   • Certified defenses

Model-level Defenses

1. Architectural

   • Robust vision encoders
   • Attention regularization
   • Feature denoising

2. Training Strategies

   • Adversarial fine-tuning
   • Contrastive robust training
   • Multi-task robustness

Post-processing Defenses

1. Output Validation

   • Consistency checking
   • Uncertainty estimation
   • Ensemble voting

2. Safety Filters

   • Content moderation
   • Hallucination detection
   • Clinical validity checks

Medical Domain Challenges

Unique Attack Surfaces

• Diagnostic Manipulation: Subtle changes leading to misdiagnosis
• Report Generation: Incorrect clinical findings
• Treatment Recommendations: Safety-critical errors

Evaluation Metrics

• Clinical Accuracy: Maintaining diagnostic performance
• Robustness-Utility Trade-off: Balancing security and functionality
• Worst-case Analysis: Focus on high-risk failure modes

Research Directions

Current Focus

1. Black-box attack efficiency
2. Physical-world robustness
3. Certified defense mechanisms
4. Clinical deployment safety

Open Problems

• Multimodal attack detection
• Adaptive defense strategies
• Robustness benchmarks for medical VLMs
• Real-world threat modeling

Related Topics

• Safety Frameworks — Comprehensive protection systems
• Evaluation Protocols — Robustness assessment methods
• Medical VLM Applications — Clinical deployment considerations