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ZTE Launches ZXCSec MAF: A Comprehensive Security Solution for Large Model Applications


ZTE Launches ZXCSec MAF: A Comprehensive Security Solution for Large Model Applications

In an era where the widespread deployment of large models introduces critical security challenges, ZTE has launched its ZXCSec MAF security solution to address these concerns. This innovative application-layer security protection device is designed to safeguard large model services from a range of threats, including adversarial attacks and prompt injection. With its comprehensive multi-layered security framework, the solution provides a critical safeguard that allows enterprises to deploy large models in production environments with reduced risk.

  • ZTE launches ZXCSec MAF security solution to protect large model services.
  • The solution addresses pressing security challenges in various domains, including adversarial attacks and data leakage.
  • It offers a multi-layered security framework covering model security, data security, application security, and content security.
  • The solution provides protection against prompt injection, API abuse, and sophisticated techniques to bypass content filters.
  • It achieves an interception rate of 99.9% and features intelligent agent behavior monitoring with a Mean Time to Detection (MTTD) of under 5 seconds.
  • The solution supports multi-factor authentication, fine-grained authorization control, and dynamic routing for secure operation.



  • ZTE has recently announced the launch of its ZXCSec MAF security solution, a cutting-edge application-layer security protection device specifically designed to safeguard large model services. In an era where the widespread deployment of large models introduces critical security challenges across various domains, ZTE's innovative solution is poised to address these pressing concerns with a multi-layered security framework that comprehensively covers model security, data security, application security, and content security.

    The introduction of large models has brought about a plethora of security threats, including adversarial attacks and prompt injection that compromise model integrity, inference attacks leading to data leakage, API abuse resulting in service manipulation, and sophisticated techniques that bypass content filters to generate harmful material. The ZTE ZXCSec MAF is designed to mitigate these threats with a robust and comprehensive security solution.

    The solution's focus on the application layer of large model services enables it to construct a multi-layered protection system that offers a high level of security for both proprietary and third-party models, including popular ones such as Llama, Qwen, and DeepSeek. This includes leveraging prompt injection protection and dynamic domain fencing to block unauthorized access and model abuse attacks, achieving an interception rate of 99.9%. The solution also incorporates an Anti-DDoS module and an AIGC watermarking engine to ensure service continuity and prevent data leakage and forgery.

    Furthermore, the ZTE ZXCSec MAF features intelligent agent behavior monitoring to analyze API call traffic in real-time, identifying anomalies with a Mean Time to Detection (MTTD) of under 5 seconds. The solution also supports multi-factor authentication, fine-grained authorization control, and dynamic routing to enforce security domain division.

    The effectiveness of the ZTE ZXCSec MAF has been demonstrated across various industrial applications, including in an urban rail transit system where it protects AI-driven diagnostic models from prompt injection and unauthorized access, ensuring the integrity of operational decisions and improving operation and maintenance efficiency by 30%. In another instance, a smart manufacturing client witnessed a significant reduction in intelligent scheduling time by 88% due to real-time agent behavior monitoring.

    In addition, ZTE has successfully leveraged the solution in internal applications, utilizing fine-grained access control to enforce over 100 API call policies. The system has demonstrated exceptional performance and protection capabilities, supporting the stable operation of the Nebula Large Model for over 1.5 years and assisting it in obtaining key certifications, including the "Large Model Product Security Inspection Certificate".

    The launch of the ZTE ZXCSec MAF marks an important milestone in addressing the security challenges posed by large model applications. With its comprehensive multi-layered security framework, the solution provides a critical safeguard that allows enterprises to deploy large models in production environments with reduced risk. As such, ZTE's commitment to developing targeted technologies that address real-world challenges faced by global customers is well-founded and commendable.

    The introduction of the ZTE ZXCSec MAF underscores the significance of security in the age of large models. As these applications continue to proliferate across various domains, it becomes increasingly essential for organizations to deploy robust security solutions that can protect against a wide range of threats. In this context, ZTE's innovative solution is poised to play a pivotal role in safeguarding the integrity and security of large model applications.

    In conclusion, the launch of the ZTE ZXCSec MAF represents a significant development in the realm of large model security. The solution's comprehensive multi-layered security framework offers a high level of protection against various threats, making it an attractive option for organizations seeking to secure their large model applications. As the landscape of large models continues to evolve, it is likely that ZTE's innovative solution will remain at the forefront of this development.



    Related Information:
  • https://www.ethicalhackingnews.com/articles/ZTE-Launches-ZXCSec-MAF-A-Comprehensive-Security-Solution-for-Large-Model-Applications-ehn.shtml

  • https://go.theregister.com/feed/www.theregister.com/2025/11/21/zte-zxcsec-maf-security-solution/


  • Published: Fri Nov 21 03:27:39 2025 by llama3.2 3B Q4_K_M













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