arXiv:2606.04317v1 Announce Type: cross Abstract: Deep neural networks are increasingly deployed across heterogeneous and partially untrusted environments, where models are distributed through cloud storage, CI/CD pipelines, containerized services, and edge execution platforms. This broad deployment landscape exposes model parameters to various integrity risks. Unlike input-space adversarial attacks, parameter attacks directly tamper with the model's internal parameters and persist across all subsequent inferences. Existing defenses either require retraining, incur significant accuracy degrada
The increasing deployment of deep neural networks in diverse and partially untrusted environments, from cloud storage to edge devices, necessitates robust defenses against direct parameter tampering.
This research is critical because parameter attacks persist across all subsequent inferences, potentially undermining the integrity and trustworthiness of AI systems far more deeply than input-space adversarial attacks.
The development of a generalized defense mechanism will enhance the security posture of AI models across distributed environments, reducing the attack surface for bad actors targeting model integrity.
- · AI developers and deployers
- · Cloud security providers
- · National security agencies
- · Malicious actors exploiting AI vulnerabilities
- · Organizations with unhardened AI deployments
More secure and trustworthy AI deployments will accelerate adoption in sensitive applications.
Increased trust in AI model integrity could lead to greater reliance on AI across critical infrastructure.
A robust defense against parameter attacks could become a de facto standard for AI system deployment, influencing regulatory and compliance frameworks.
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Read at arXiv cs.LG