BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
arXiv:2606.02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that this paradigm is highly vulnerable to backdoor attacks, and that existing defenses are ineffective in open-ended generation settings. In response, we propose BYORn, a backdoor-robust fine-tuning framework motivated by the observation that poisoned target responses are often semantically implausible given the corresponding image-text inputs and a pretrained model. BYORn identifies such misaligned res
As Large Vision-Language Models (LVLMs) become more ubiquitous and integrated into critical applications, their vulnerability to adversarial attacks, such as backdoors, becomes a pressing security and reliability concern that demands immediate solutions.
This development is crucial for strategic readers as it addresses a significant security vulnerability in a foundational AI technology, impacting the trustworthiness and deployment of advanced AI systems across various sectors.
The introduction of frameworks like BYORn changes the defensive posture of LVLMs, moving towards more robust and secure fine-tuning methods that can mitigate sophisticated backdoor attacks, enhancing their operational integrity.
- · AI security researchers
- · Organizations deploying LVLMs
- · AI ethics and safety advocates
- · Threat actors relying on backdoor attacks
- · Developers of insecure fine-tuning methods
Improved security and trustworthiness of advanced AI models.
Accelerated adoption of LVLMs in sensitive applications due to enhanced reliability.
A potential arms race between AI defense mechanisms and evolving attack strategies, leading to more sophisticated AI security paradigms.
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Read at arXiv cs.LG