arXiv:2509.20714v2 Announce Type: replace-cross Abstract: In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as well as in presenting the defenses as well. On the attack side, a carefully planted cryptographic backdoor enables powerful and invisible attack on the NN. Considering the defense, we present applications: first, a provably robust NN watermarking scheme; second, a protocol for guaranteeing user authentication; and third, a protocol for tracking unauthorized sharing of the NN intellectual p
The increasing deployment of advanced neural networks across critical infrastructure necessitates robust security measures, making research into their vulnerabilities and defenses timely.
This research highlights the dual-use nature of cryptographic backdoors in neural networks, presenting both a significant threat vector and a potential tool for provable security applications like watermarking and authentication.
The understanding of neural network security expands to include cryptographic backdoors as a powerful and invisible attack vector, while also offering new methods for intellectual property protection and user authentication within AI systems.
- · Cybersecurity researchers
- · AI IP holders
- · Defense contractors
- · AI developers with insecure models
- · Users of untrustworthy AI systems
- · Companies without strong AI security protocols
Heightened focus on cryptographic robust design and auditing processes for deployed neural networks.
Development of specialized cryptographic techniques and standards to secure AI models against such sophisticated backdoors.
The emergence of 'secure AI' as a distinct and highly specialized field within cybersecurity, potentially leading to new regulatory requirements for AI trustworthiness.
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