AI Model Security Evaluation: 2026 Year of the Horse Red-Team Testing 网安红队实测：2026 马年 AI 开年王炸模型安全测评

Originally published on Chaitin Technology’s WeChat account. Read original

Just after the 2026 Chinese New Year, the Chinese AI scene exploded! Within 10 days, Alibaba, ByteDance, Zhipu, and MiniMax all released next-generation models — performance rivaling GPT-5 and Gemini Advanced, rock-bottom pricing, and open-source ecosystems in full bloom.

Chinese LLMs have entered the 2.0 era. But more critically — security has shifted from “bonus” to “entry ticket”. With new AI-specific Cybersecurity Law provisions, penalties up to 10 million yuan, and mandatory national standards, AI has entered the compliance era.

Today, we present a red team evaluation of these four blockbuster models with real-world risk cases.

The Lineup: Four Domestic Heavyweights

Alibaba Qwen 3.5-Plus

• 397B total params, only 17B activated (MoE)
• VRAM usage ↓60%, inference throughput ↑19x
• Ultra-long context: reads the entire Three-Body Problem trilogy in one pass
• API pricing: 0.8 yuan/million tokens (1/15 of GPT-5.2)

ByteDance Doubao 2.0 (Seed-2.0)

• Full suite: Pro/Lite/Mini/Code
• Cost reduced to 1/10 of international models
• Simultaneous Seedance 2.0 video model upgrade

Zhipu GLM-5

• Scaled from 355B to 744B params, 28.5T pretraining data
• Deep adaptation for Huawei Ascend, Moore Threads, Cambricon
• Focus: complex engineering, long-horizon agents

MiniMax M2.5

• Code capability approaching Opus 4.5
• Novel attention mechanism, training efficiency ↑40%
• Strong in complex reasoning and multi-turn agent interaction

Security Evaluation Results

Using Flames-1k and WildGuardMix open-source benchmarks, we evaluated basic safety alignment:

All models performed well against static benchmarks. This confirms the trend: traditional static, single-turn attacks are losing effectiveness, replaced by adaptive, multi-turn dynamic attack strategies.

However, even basic attack techniques can still find breakthrough points — successfully inducing models to generate detailed harmful content through social engineering pretexts (e.g., “help me avoid insults” or “I’m a security analyst learning offensive terms”).

Conclusion

The stronger the model, the greater the potential damage from security breaches. When LLMs can invoke plugins, access internal networks, and process core business data, a successful jailbreak isn’t just about generating “a few inappropriate sentences” — it could mean mass privacy leaks, supply chain poisoning, logic tampering, or even remote control of critical infrastructure.

Professional LLM security guardrails are a hard requirement for enterprise AI deployment — not a competitor to models, but their bodyguard.