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Cloudflare discloses GLM-5.3 cross-tenant leak and fixes it

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AI overview

Z.ai's GLM-5.3 launched on Cloudflare Workers AI on August 27, 2026. On September 8, Knostic researcher Gadi Evron reported via HackerOne: the endpoint would return other users' session content when a request exceeded about 700,000 tokens, involving names, invoice numbers, source code information, etc.; black-box testing ruled out hallucination and reproduction of training data. Cloudflare completed the fix on September 9 and published a report on October 26: from August 27 to September 9, a total of 155 responses mixed in other users' data, with no evidence of deliberate exploitation, and other models were unaffected. There were two root causes: Cloudflare's configured context length of 1,048,576 tokens exceeded the prefill cache pool's 681,280 tokens; SGLang did not reject over-capacity requests as designed. The SGLang maintainers coordinated disclosure with Cloudflare and released a security update improving request isolation.

Generated by AI from reports · Updated 2 days ago

Progress timeline

Oct 29
Oct 28
  1. iThome (Taiwan)Selected
    Cloudflare AI service cache error: 155 GLM-5.3 responses mixed in others' data

    Cloudflare published the findings of its investigation into the data leak at its Workers AI: between August 27 and September 9, the GLM-5.3 model had a total of 155 responses that mixed in other customers' data; the fix was completed on September 9; other models were unaffected, and no evidence of deliberate exploitation was found. The incident stemmed from Cloudflare's deployment configuration and a program vulnerability in the open-source inference software SGLang: the GLM-5.3 input limit was set to about 1.05 million text units, but the GPU cache handling the input could hold only about 680,000; when input exceeded the cache capacity, SGLang did not reject the request as designed, causing responses to mix in content left in the KV cache by other customers. Gadi Evron, a researcher at the security firm Knostic, reported it on September 8; Cloudflare reproduced the problem the next day with two accounts, suspended the model service, and restored it the same day after the fix. Cloudflare acknowledged that before launch it knew the cache capacity did not match the input limit, but at the time it only checked response status and performance; SGLang users had reported the related vulnerability as early as July.

Oct 27
  1. KnosticSelected
    Hallucination or cross-tenant: A large language model on Cloudflare Workers AI answers with another user's context

    Knostic researcher Gadi Evron found that the @cf/zai-org/glm-5.3 endpoint of Cloudflare Workers AI returns the contents of other users' inference sessions rather than a response to the submitted prompt when a request exceeds about 700,000 tokens. The leaked content included personal names, company names, invoice numbers, euro amounts, payment dates, source code information, and Agent task records. The author used black-box falsification to rule out hallucination and training data reproduction: the unique marker in the request disappeared, latency dropped sharply at about 700,000 tokens, the output had zero overlap with the input, and a shell call returned in one response matched a public repository that was created after the model's training cutoff and had a signed verification commit less than 4 minutes after the affected inference. The issue was reported via HackerOne on September 8, 2026; Cloudflare confirmed and deployed a fix on September 24, publicly disclosed it on October 26, and its blog analyzed in depth the upstream vulnerability in SGLang.

  2. Unite.AISelected
    Cloudflare released a report stating that a GLM-5.3 flaw caused cross-tenant session leakage of 155 responses

    Cloudflare released an incident report on October 26, 2026: the GLM-5.3 model on its Workers AI platform suffered a cross-request KV cache leak, and 155 responses were mixed with data from other customers' sessions. The issue was reported by Knostic researcher Gadi Evron via HackerOne on September 8, 2026, and affected the service provided as @cf/zai-org/glm-5.3 since the model went live on August 27; Cloudflare fixed it and restored the model on September 9, stating that no evidence of deliberate abuse was found and that other Workers AI models were unaffected. Cloudflare has expanded the prefill KV pool to approximately 2.04 million tokens and deployed an engine patch; the relevant fixes have been merged into SGLang v0.5.20 through v0.5.22, and the company recommends that all SGLang operators upgrade to v0.5.22 or higher.

  3. CloudflareSelected
    Cloudflare discloses cross-request KV cache leak in the GLM-5.3 model on Workers AI and coordinated fix with SGLang

    Cloudflare 披露,其 Workers AI 平台上 Z.ai 的 GLM-5.3 模型曾发生跨请求 KV cache 泄露,2026 年 8 月 27 日至 9 月 9 日间共 155 条回复包含其他用户请求的数据。受影响回复全部来自超过 681,280 token 的长提示,无证据显示漏洞被刻意利用,其他 Workers AI 模型未受影响。根因是两个问题叠加:prefill 节点的 KV cache 池仅 681,280 token,小于模型 1,048,576 token 的上下文窗口,而推理引擎 SGLang 在分离式 prefill/decode 模式下会把被拒的超长请求替换为单 token 占位符后照常传输,decode 节点因此读取了先前请求遗留的缓存页。

Oct 26
  1. LMSYS OrgSelected
    SGLang security update: improved request isolation

    SGLang 维护方与 Cloudflare 协调披露 SGLang PD 分离服务中的跨请求 KV 缓存隔离缺陷。Cloudflare 的调查发现,其 Workers AI 上的 GLM-5.3 部署在 2026 年 8 月 27 日至 9 月 9 日间,超出 prefill 缓存容量的请求可能返回其他用户会话的内容,起因是配置的上下文长度 1,048,576 token 超过 prefill 缓存池的 681,280 token,被拒请求仍进入分离交接,decode 读到未为本请求填充的缓存页。相关的一类竞态是请求中止后已发出的 RDMA 写入落入被重新分配的槽位,造成无报错的单向跨请求泄漏,长上下文会扩大窗口;SGLang 自 v0.4.x 至 v0.5.21(MORI 后端至 v0.5.22)的 PD 默认配置受影响。维护方建议 PD 部署升级并运行能配对中止的路由器。

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