arXiv:2511.12046v2 Announce Type: replace-cross Abstract: Knowledge Distillation (KD) is essential for compressing large models, yet relying on pre-trained "teacher" models downloaded from third-party repositories introduces serious security risks--most notably backdoor attacks. Existing KD backdoor methods are typically complex and computationally intensive: they employ surrogate student models and simulated distillation to guarantee transferability, and construct triggers similar to universal adversarial perturbations (UAPs), which being not stealthy in magnitude, inherently exhibit strong a
The increasing reliance on pre-trained AI models from third-party repositories and the widespread use of Knowledge Distillation make this research into backdoor vulnerabilities timely and critical.
This research reveals a practical and stealthy method for backdooring AI models, which could compromise the integrity and security of countless AI systems built using Knowledge Distillation.
The ease with which backdoor attacks can be implemented via weak triggers and fine-tuning fundamentally alters the security posture of AI model development and deployment, requiring more robust verification.
- · AI security researchers
- · Developers of AI model verification tools
- · Organizations with strong internal AI model development
- · Cybersecurity consultancies
- · Companies relying on third-party pre-trained models
- · Organizations with weak AI supply chain security
- · Developers of AI models without robust auditing processes
- · Users of compromised AI systems
Increased scrutiny and demand for secure AI model supply chains will emerge, impacting how models are shared and integrated.
New industry standards and regulatory frameworks will likely be proposed to address AI model provenance, integrity, and backdoor detection.
A potential 'AI trust crisis' could develop if widespread backdoor incidents erode public and institutional confidence in AI systems, slowing adoption in critical sectors.
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Read at arXiv cs.LG