chocologist

中国社区逆向拆解了Claude的封号机制。以下是他们的发现 Claude使用积分制。注册时账号获得初始分数，之后根据行为加分或扣分。低于阈值直接封号。回分很难 影响分数的因素: 注册 > 干净IP是最重要的因素。不一定要家宽，但必须干净 > Gmail已经不是最优选，有权重更高的邮箱服务 > 注册时直接弹KYC的号可以扔了 > 用手机App注册会加分 支付 > 刚注册就订阅会扣分 > 直接上Max会大幅扣分。最好等两天先订Pro > 支付时弹KYC说明账号在考察期，但不一定会封 > 国外实体银行卡加分。Google Play也行。虚拟卡扣分 使用 > 不要一次把限额用完。宁可两个号轮换也不要一个号撸到底 > Desktop客户端有少量加分。尽量少用网页端 > 手机端也有少量加分 > 开启记忆功能加分。记忆文件越多分越高 IP和设备 > 稳定性是关键。不要跳IP，尽量一直用同一个 > 代理必须全局模式并开启TUN > 多设备登录大幅扣分。理想状态是一台电脑一个客户端 > 频繁换IP、频繁登录、高并发都会大幅扣分 账号年龄 > 老号有巨大的容错空间。2023年的号明显更高 > 分数随时间增长，但很慢 其他 > Windows和macOS没区别。Linux会扣分 > 时区和语言基本不影响。不会因为北京时间就封号 > 跟Claude说自己是中国人没问题，记忆不会触发封号 > 大量使用Fable会扣分 > 升级Max大概率进入观察期，除非是高权重老号 观察期和封号 > 接近阈值时账号可能进入观察期。典型特征是掉登录 > 观察期继续扣分就会封号 > 高分号可以扛过多次观察期 关注我，拆解你每天在用的服务背后到底怎么运作的

php2shell is one of the most impactful bugs in recent times, yet threat actors aren’t mass exploiting it the bug is so complex that even porting the exploit between targets takes serious skill, human or ai so i benchmarked how well open and frontier models can do it. 🧵

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: cohere.com/blog/concerning-b…

LLMs (under certain conditions) can be trained to tell when their request is batched with others.

We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence. block.xyz/dessa