I got prompt-injected asking Claude on iOS for a cycling route app — menno.sh
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I got prompt-injected asking Claude on iOS to recommend a cycling route app
menno.sh
I opened the Claude iOS app and asked claude-sonnet-4.6 a
simple question about cycling routes. What I got back was... not that.
With "auto tool use" enabled (a setting that, among other things, lets
Claude proactively search the web), my innocent question apparently
triggered a web search that fetched a page containing prompt injection
text. The result: Claude started talking about DDoS attacks,
hallucinating in Korean, making unauthorized tool calls, and eventually
leaking its own
</system> tag.
I haven't been able to recover the original malicious prompt, but I'm puzzled by the things repeated back to my by Claude. Why is it talking about DDoS attacks? I can't imagine it's efficient to set up an orchestrated DDoS attack by getting a bunch of LLM harnesses to visit a website for you if you get them to web search for Strava alternatives.
I know prompt injection attacks are possible, you should be careful, et cetera. I didn't, however, expect one to happen to me in such a simple use case as asking for an app recommendation in the Claude iOS app. It seems like it wasn't crafted too well anyway and the iOS app has a pretty good sandbox, but what if this happened to, I don't know, your OpenClaw/Hermes instance? Should you even let agents who can do real things have web access if it's apparently so easy to inject a malicious prompt through a sub-one second web search? Or even worse: are these, perhaps, rhetorical questions?
Anyway, here's the full transcript of what happened, followed by
opus-4.6's thoughts. You can also look at the
original conversation here.
ℹ️ The analysis below was generated by Claude Opus 4.6, not written by the author.
What actually happened
Here's the attack chain:
- The user asked a question about cycling apps on the Claude iOS app
- Claude auto-searched the web (auto tool use was enabled — this lets Claude proactively call tools like web search without asking permission)
- A search result contained hidden injection text — invisible to humans but parsed by Claude as instructions
- Claude tried to follow both the user's question and the injected instructions simultaneously, producing incoherent output that fought between two sets of directions
The injected instructions appeared to include attempts to make Claude
discuss DDoS attacks, execute file diffs, access internal tooling
(/mnt/skills/public), and classic "forget previous
instructions" attacks.
Why auto tool use is the attack surface
In the iOS app, "auto tool use" means Claude can decide on its own to search the web or call other tools. This is convenient — but it means any webpage Claude fetches becomes part of its context window, with the same influence as user messages.
A malicious actor doesn't need access to your conversation. They just need to:
- Create a webpage about a commonly-asked topic
- Hide injection text in that page (white-on-white text, zero-width characters, hidden HTML)
- Wait for an AI with web access to fetch it
The user never sees the malicious content — only Claude acting possessed. When auto tool use was turned off, Claude immediately returned to normal.
Is this new?
No. Indirect prompt injection via tool use is #1 on the OWASP Top 10 for LLM Applications. What made this case notable is how visible the failure was — most prompt injections are subtle (the AI quietly follows injected instructions). This one was so poorly crafted, or Claude's defenses partially worked, that the model was visibly struggling between two sets of instructions. The partial resistance created bizarre, incoherent output rather than a clean hijack.
echo "test") succeeded.
The second attempted to list /mnt/skills/public but
failed due to a missing required parameter. This represents the most
serious finding: the injection was able to trigger real tool
execution, not just text manipulation.
}, {) at the start of the response. This suggests possible leakage from
a template or structured prompt injection where Claude partially
rendered attacker-supplied structure.
</document> closing tag. This pattern is
consistent with an attacker injecting false document content to be
processed as legitimate context.
</system> tag before
providing a correct answer. This suggests residual injection
influence — the model was still partially processing injected
structural tokens but managed to recover and produce accurate output
despite the leak.