Backdoor Attacks on AI Models
Backdoor attacks involve embedding hidden triggers in AI models that cause malicious behavior when activated, while maintaining normal performance otherwise.
Attack Mechanism
Attackers inject backdoors during training by poisoning the dataset with trigger patterns that cause specific misclassifications.
- • Training data poisoning
- • Model weight manipulation
- • Transfer learning exploitation
- • Supply chain attacks
Detection & Mitigation
Multiple techniques can help detect and mitigate backdoor attacks in AI models.
- • Activation clustering analysis
- • Neural cleanse techniques
- • Model pruning and fine-tuning
- • Input preprocessing defenses
Real-World Impact
Backdoor attacks pose severe risks in critical applications like autonomous vehicles, medical diagnosis, and security systems where triggered misclassifications could have catastrophic consequences.
Autonomous Systems
Triggered failures in self-driving cars or drones
Medical AI
Misdiagnosis when specific patterns are present
Security Systems
Bypassing authentication or detection systems