GLM-5 と agentic AI の新時代：プライバシーとセキュリティへの影響

Introduction

Chinese AI developer Zhipu AI recently unveiled GLM-5, a major new language model that underscores the accelerating arms race in large-scale AI. The company positions GLM-5 as a shift from what it calls "vibe coding" toward "agentic engineering" — enabling more autonomous, code-generating agents. With model size, training data, and efficiency innovations all scaling up, these advances bring both capability and new risks for privacy and security.

This article explains what GLM-5 represents technically, the privacy and security implications of more agentic models, and practical steps — including how using a VPN like Doppler VPN can reduce risk when interacting with advanced AI systems.

What GLM-5 Brings to the table

Key technical points reported about the new model:

• Dramatically larger footprint: GLM-5 reportedly grows to roughly 744 billion parameters, about double its predecessor.
• Vast training corpus: The model was trained on tens of trillions of tokens, reflecting a massive expansion in data intake.
• Efficiency-focused architecture: GLM-5 incorporates a sparse-attention architecture derived from recent research (sometimes referred to as DeepSeek Sparse Attention) to make computation more efficient and cost-effective.
• Focus on agentic performance: Zhipu highlights improved capabilities at multi-step, tool-using tasks — often called agentic behavior — and rates its own benchmarks favorably against certain open models.

The race to produce more capable agents and better coding assistants is global. GLM-5 sits alongside other major models that are optimizing for code generation, planning, and autonomous task execution.

Why "Agentic Engineering" Matters

Agentic engineering refers to building models that can carry out multi-step tasks, orchestrate tools or APIs, and make intermediate decisions with less human oversight. This heralds more powerful automation — but also a broader attack surface:

• Autonomous code generation can accelerate development, but it can also produce insecure or vulnerable code at scale.
• Agentic workflows typically involve invoking external tools and services, increasing the number of systems that may leak sensitive data.
• The capacity to reason over and manipulate web APIs raises the possibility of systems that inadvertently or maliciously perform actions on behalf of users.

These features make agentic models attractive for productivity — and valuable targets for attackers.

Privacy and Security Risks Introduced by Large, Agentic Models

As models scale and gain agency, several concrete privacy and security concerns intensify:

• Data leakage and memorization: Models trained on vast crawled datasets may memorize snippets of sensitive information (API keys, passwords, proprietary code) and reproduce them when prompted. Larger models and larger token corpora can increase the risk surface.
• Model inversion and extraction: Sophisticated attackers can probe models to reconstruct training data or extract model behavior and parameters.
• Malicious code generation: Agents that write programs or scripts may unintentionally produce insecure code or, when abused, generate malware or exploit scripts.
• Supply-chain and dependencies: New architectures and third-party components (like sparse attention libraries) add complexity and potential vulnerabilities in model toolchains.
• Unauthorized actions: Agentic systems that can interact with services or execute code may perform unintended or harmful operations if controls are weak.

These risks apply whether you are a developer using a public API, a business integrating agents into workflows, or an individual interacting with AI tools.

Practical Security Measures for Working with Agentic AI

Mitigations must span policies, engineering practices, and operational controls:

• Sanitize inputs and outputs: Treat model I/O as untrusted. Filter prompts and sanitize decrypted responses to prevent leakage of secrets.
• Limit model permissions: Use the principle of least privilege for any agent that can access services or execute code. Give the agent only the resources it strictly needs.
• Sandbox execution: Run generated code in isolated, ephemeral environments with strict network and file access controls.
• Monitor and audit: Keep detailed logs of agent actions and model queries; use anomaly detection to spot suspicious behavior.
• Validate generated code: Integrate automated static analysis and security scanning into any pipeline that executes model-generated artifacts.
• Maintain provenance and data governance: Know what data was used for training and establish policies to prevent training on sensitive internal material.

How a VPN Helps — and Where It Fits In

A VPN is not a cure-all for model-level risks, but it plays an important role in protecting network-level confidentiality and integrity when you interact with AI systems.

When to use a VPN:

• Protecting API keys and credentials: When sending requests to cloud model APIs from remote or untrusted networks, a VPN encrypts traffic and reduces the chance of interception.
• Secure remote development: Developers collaborating on agentic systems or testing generated code from public networks should tunnel traffic to avoid eavesdropping.
• Geo- and jurisdictional considerations: Some organizations route AI traffic through specific jurisdictions for compliance or to access region-locked resources. A VPN can help enforce those routing decisions.
• Preventing ISP or corporate monitoring: VPNs mask destination endpoints and traffic contents from local observers, which is helpful when you don't want browsing or API usage profiles to be visible to your network provider.

What a good VPN should provide for AI users and developers:

• Strong encryption and leak protection (DNS, IPv6, WebRTC)
• Kill switch to prevent accidental exposure if the VPN drops
• Split tunneling, so you can secure AI traffic while leaving other services on the local network
• Multi-hop or dedicated IPs for teams that want extra separation
• A global network to choose exit points aligned with compliance needs

Doppler VPN, for example, offers robust encryption, leak protection, and flexible routing options that can help safeguard communications with cloud AI providers and development environments. Using a VPN in combination with application-layer safeguards (API key rotation, scoped credentials) adds a valuable layer of defense.

Operational Checklist for Teams Deploying Agentic Models

• Classify data before it ever reaches a model: never feed secrets or personal data unless the model and legal terms explicitly allow it.
• Use scoped, short-lived API credentials and rotate them frequently.
• Route model interactions through secured networks (VPN) when working from public Wi‑Fi or untrusted endpoints.
• Apply runtime sandboxing and static analysis to any generated code before execution.
• Keep an incident response plan that includes model misuse scenarios and exfiltration vectors.

Conclusion

GLM-5 and similar next-generation models push the frontier of what AI agents can do, especially in coding and tool use. They promise productivity gains, but they also complicate the security and privacy landscape. Defending against the new risks requires a layered approach: governance and data hygiene, secure development practices, runtime controls, and network-level protections.

A VPN — such as Doppler VPN — is one practical component of that strategy. By encrypting and routing traffic securely, it reduces exposure when interacting with third-party model APIs or collaborating remotely. Pairing a VPN with strong credential management, sandboxing, and auditing will give organizations and individuals a more resilient posture as AI systems become more agentic and more powerful.

Staying ahead means combining technical safeguards with clear policies. As models like GLM-5 change what's possible, make privacy and security foundational to every AI integration, not an afterthought.