2025年千帆大模型平台安全白皮书_17页_1mb

Baidu Qianfan Platform Security Summary

Overview

Qianfan Platform is an enterprise-level, one-stop service for large model development and application deployment, integrating model training, hosting, fine-tuning, and toolchains. Security is paramount for stable operation, as vulnerabilities can lead to data leaks and service disruptions, ensuring reliable AI adoption.

Main Security Challenges

• Platform Vulnerabilities: Risks from misconfigurations and lack of basic security, potentially enabling unauthorized access.
• Model Security Issues: Foundational models may lack standard safety, leading to risks like backdoor implants or poor content generation. Fine-tuned models face leakage risks during storage or transfer.
• Data Risks: Sensitive data uploads for model training raise concerns over exposure, transfer security, and lifecycle protection.
• Content Compliance: Generated content must align with regulations to avoid violations like generating harmful or illegal material, impacting social order and user trust.

Security Framework and Capabilities

The framework emphasizes multi-layered protection based on national laws, combining technical measures with continuous improvement. Key capabilities span:

• Infrastructure and Network: Virtualization, intrusion detection, encryption, and VPC isolation for robust defense.
• Application and Model: Identity management, secure development lifecycle, model encryption, and anti-leakage mechanisms.
• Data Security: Data masking, HTTPS encryption, storage encryption, and strict access control to prevent unauthorized data use.
• Content Safety: Content filtering, intervention systems, and URL detection to ensure generated outputs are compliant and safe.
• Operations and Compliance: Regular red-blue exercises, emergency responses, and certifications like ISO/IEC standards to maintain regulatory adherence.

Best Practices

• Secure Access: Utilizing hybrid cloud configurations via VPCs and private networks to minimize public exposure risks, enhancing data integrity and security for model services.

Conclusion and Future Directions

Qianfan Platform underscores the importance of security in AI advancement through its comprehensive framework, ensuring reliable and lawful operations. Looking ahead, emerging threats such as remote attacks on AI agents necessitate ongoing enhancements in security measures and standardized governance to foster a resilient and innovative ecosystem.