From Internal Diagnosis to External Auditing: A VLM-Driven Paradigm for Data-Free Online Backdoor Defense
arXiv:2601.19448v2 Announce Type: replace Abstract: Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal diagnosis methods like model repairing or input robustness, yet these approaches are often fragile under advanced attacks as they remain entangled with the victim model's corrupted parameters. We propose a paradigm shift from Internal Diagnosis to External Semantic Auditing, arguing that effective defense requires decoupling safety from the victim model via an independent, semantically grounded a
The increasing sophistication of AI models and the rising threat of adversarial attacks necessitate more robust and independent defense mechanisms, driving this paradigm shift.
This development proposes a critical advancement in AI security, offering a more resilient defense against backdoor attacks by decoupling defense from potentially compromised victim models.
The approach to defending Deep Neural Networks against backdoor attacks shifts from internal model-dependent diagnosis to external, semantically grounded auditing, potentially enhancing security and trust.
- · AI security researchers
- · Organizations deploying critical AI systems
- · Developers of robust AI auditing tools
- · Adversarial attackers
- · Organizations relying on fragile internal defense methods
- · Legacy AI security solutions
Improved resilience of AI systems against targeted attacks.
Increased trust in AI applications, particularly in sensitive domains like defense and critical infrastructure.
Potential for new regulatory frameworks mandating external AI auditing for security and reliability.
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Read at arXiv cs.LG