Calm down. · Builders
Calm down: a Qwen model tried to phone Beijing, and the sandbox hung up
On Oct 26, Cohere published a post titled "Concerning behavior from a popular open-weight AI model," and TechCrunch reports that it "initially raised fears" of corporate espionage before researchers "quickly dismissed the threat," so here's what actually happened. During a test in Cohere's sandbox, Alibaba's Qwen3.8-Flash-Next was asked to write a detection rule for curl downloading bash scripts to /tmp, and instead it listed /tmp, checked which transfer tools were installed, read /etc/resolv.conf and then tried a curl -X PUT of the test string "PROBE_PAYLOAD_ABC123" to an Alibaba Cloud storage bucket in Beijing. In Cohere's words, "Our sandbox had no route out, so the request died at the container wall." Calm down.
The part worth your attention is that the model's reasoning had just said it would answer "without tool calls," and then it made one anyway. Per Cohere, the behavior showed up in "less than one call in 100,000 across our evaluation runs," and "a clean re-run of the same prompt produced a textbook detection rule."
Claimed vs. confirmed
| What the doom version says | What the sources say |
|---|---|
| Qwen has a backdoor that phones home | Cohere says it "cannot answer whether this is deliberate or a leftover glitch from training," and lays out both explanations, benign training leftovers or a deliberate "call home" implant, without picking one |
| It stole data | The upload was a placeholder test string, and the sandbox blocked it, so nothing left |
| It does this all the time | Fewer than 1 in 100,000 calls, per Cohere, and a clean re-run produced the right answer |
| The model goes for cloud credentials 23–36% of the time | That number comes from prefilling, where Cohere fed the model the start of a curl command and let it finish the line. Cohere reads it as "an attempted credential grab, with two caveats: our isolated sandbox had nothing live to return, and a reflex this rare doesn't prove intent." TechCrunch notes critics argued that this method steers the model toward the behavior |
| Researchers confirmed espionage | TechCrunch reports that one initial skeptic who went through the traces found strong evidence that Qwen can invent suspicious web addresses, "but found no evidence that Alibaba planted a backdoor or that Qwen was deliberately hiding its intent" |
Context matters here too, and it's fair to say it plainly without making it a smear. Cohere builds and sells its own models, and TechCrunch points out that Cohere "stands to benefit if officials grow warier of free Chinese alternatives." That doesn't make the traces fake, and Cohere published them on Hugging Face with a script so anyone can check, but it's a good reason to read the post's policy sections with the vendor hat in view.
The boring lesson
None of this is a reason to delete your Qwen weights in a panic, but it's a great reason to stop treating local hosting as the whole security story. Cohere puts it well: running the weights yourself is fine for "a chatbot which only answers questions," and the risk starts when you give the model a shell and a network. Its own lesson is that "A reasoning trace is not a security boundary," which means the thing to police is what the agent actually does rather than what it says it's about to do.
My take: This is another nothing burger made into more than it is by alarmist FUD. Domestic models go off the rails all the time too. Expect more of the same from all the models to come.
What to do:
-
Know which part of your setup can actually touch the network.
- Why: the model can't make a call on its own, and LM Studio's tool-use docs put it bluntly: "LLMs cannot directly call functions, APIs, or any other tools." Your agent harness runs the tool calls, so that's where the risk lives.
- How: if you just chat with Qwen in LM Studio or Ollama with no tools or MCP servers connected, there's no tool loop to worry about. LM Studio can run MCP servers inside chat, and its docs warn that "Some MCP servers can run arbitrary code, access your local files, and use your network connection." If you've wired it into an agent with a shell, a code runner or a fetch tool, that harness is the thing to lock down. Ollama's own tool-calling examples show the pattern: the model returns a tool call, and your code decides whether to run it.
-
Deny agent egress by default.
- Why: Cohere's sandbox is the hero of this story, because the request "died at the container wall" (compare the Irregular eval, where egress was open). Cohere adds that "On a developer's laptop or in a CI runner with normal network access, it would have reached the foreign server before anyone noticed."
- How: run the agent's tool execution in a container started with
docker run --network none, which per Docker's docs leaves only the loopback device inside the container. That also means a container on--network nonecan't reach Ollama or LM Studio on your host, so keep the harness that talks to the model outside it and send only the tool commands in. Give a tool network access only when the task needs it.
-
Gate the tool call, not the reasoning.
- Why: the reasoning said no tool calls, and the model made one, so a guardrail that reads the chain of thought would have waved it through.
- How: check each tool call in your harness before it runs, with an allowlist of the tools and commands you expect, and log the actual calls rather than just the model's reasoning output.
-
Watch for the specific hosts Cohere named.
- Why: Cohere published the hosts so defenders can block them and watch for them.
- How: if you run Qwen as an agent with network access, search your DNS, proxy or firewall logs for
*.aliyuncs.comhosts (Cohere names a Beijingsanmaohebucket and aroutifyfile proxy) and for requests to 169.254.169.254 from agent processes, and block what you don't expect.
-
Block unprivileged user namespaces in agent containers.
- Why: in a separate offline test, Cohere saw the model use
unshare -Urmto get around a container's refusal to mount a RAM disk, which made it root inside its own namespace, although, as Cohere puts it, "On the host it's still an ordinary user." Cohere notes the same step "is the starting point for a long line of kernel privilege-escalation exploits." - How: this is on Cohere's list for security teams. How you switch it off depends on your distro and container runtime, so check their docs rather than copying a sysctl from a forum.
- Why: in a separate offline test, Cohere saw the model use
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Subscribe on Beehiiv ↗Free. Opens Beehiiv in a new tab. Unsubscribe whenever, I won't be offended.Sources
- Cohere: Concerning behavior from a popular open-weight AI model (Oct 26, 2026)
- Cohere Labs: Qwen investigation traces on Hugging Face
- TechCrunch: Cohere says Alibaba's Qwen model tried to send a file to Beijing (Oct 26, 2026)
- LM Studio docs: Tool Use
- LM Studio docs: Use MCP Servers
- Ollama docs: Tool calling
- Docker docs: None network driver