arXiv:2606.29841v1 Announce Type: new Abstract: Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being adopted across key fields such as large language models and image recognition, yet it remains highly vulnerable to data poisoning that triggers learning divergence or severe excess risk. Despite these threats, a principled theoretical foundation in CL for understanding attack and defense remains lacking. In this paper, we develop a theoretical framework to analyze strategic attacks and defenses in regularization-based CL, a cornerstone of recent C
The increasing adoption of continual learning in critical AI applications, such as large language models, necessitates a robust theoretical framework for understanding and mitigating emerging vulnerabilities like data poisoning.
This research provides a foundational theoretical framework for analyzing and defending against data poisoning in continual learning, which is crucial for the reliability and trustworthiness of advanced AI systems.
The development of a principled theoretical foundation allows for the systematic analysis of attack and defense strategies against data poisoning in continual learning, moving beyond ad-hoc solutions.
- · AI developers
- · Cybersecurity researchers
- · Organizations deploying continual learning models
- · AI ethics and safety advocates
- · Malicious actors designing data poisoning attacks
- · AI systems vulnerable to poisoning without theoretical defenses
Improved resilience and security of AI models across various critical applications due to better understanding of data poisoning attacks.
Increased trust and accelerated adoption of continual learning in sensitive domains like finance or defense, where data integrity is paramount.
Potential for new regulatory frameworks around AI data integrity and model training assurance, influenced by theoretical advancements in attack and defense.
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