Cloudflare: GLM-5.3 KV Cache Mismatch Leaked Customer Data
10/31/2026 — 10/31, 15:17·1 sources (all)·1 reports (all)
Key takeaways
- In an October 26 incident review, Cloudflare disclosed that between August 27 and September 9, 155 GLM-5.3 responses on Workers AI contained…
- oversized requests were flagged for rejection but never actually aborted.
Background & analysis
Cloudflare published an incident report on October 26, 2026, revealing that between August 27 and September 9, the GLM-5.3 model on Workers AI produced 155 responses that contained fragments of other customers' requests. According to the report, none of the requests involved any hacking — the incident stemmed from two failures happening at the same time.
The first is a capacity mismatch. GLM-5.3 advertises a context window of roughly 1M tokens, but the GPU KV cache pool on the serving infrastructure holds only 681,280 tokens. Requests exceeding that ceiling are therefore marked for rejection, because there is not enough room for their KV cache. The second failure is that this rejection was never enforced: over-long requests were flagged as rejected but never actually terminated, so they continued to execute on the GPUs. Once those requests ran, fragments of other customers' requests — prompts longer than 681,280 tokens belonging to different tenants — ended up mixed into GLM-5.3's outputs.
The incident was surfaced in a third-party write-up published on October 31, 2026, which characterized the underlying bug as more instructive than the headline numbers. As of that report, Cloudflare had disclosed the mechanism through its postmortem, but no further details were available on how the affected customers were notified or what follow-up fixes were shipped. The affected window ran for roughly two weeks before ending on September 9.
AI-generated from 1 reports · updated 6 days ago
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Reports on this story headlines open the original
Cloudflare's October 26 incident report says that between August 27 and September 9, 155 responses from GLM-5.3 on Workers AI contained fragments of other customers' requests, all from prompts longer than 681,280 tokens. The model advertised a ~1M-token context, but the GPU KV cache pool held only 681,280 tokens, and over-length requests were flagged as rejected without being aborted.
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