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{"bugzilla": {"description": "vllm: vLLM: Cross-User Data Leakage via Integer Overflow", "id": "2515383", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2515383"}, "csaw": false, "cvss3": {"cvss3_base_score": "5.3", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:N/A:N", "status": "draft"}, "cwe": "CWE-190", "details": ["vLLM is an inference and serving engine for large language models. Prior to 0.27.0, an integer overflow in blockIdx.x * 2 * d in activation_kernels.cu can cause act_and_mul_kernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or complete copy of another user's inference result. This issue is fixed in version 0.27.0.", "A flaw was found in vLLM, an inference and serving engine for large language models. An integer overflow vulnerability in the `act_and_mul_kernel` kernel allows a remote attacker to cause cross-user data leakage. By crafting specific requests processed in the same inference batch, an attacker can obtain a partial or complete copy of another user's inference result. This could lead to the unauthorized disclosure of sensitive information."], "name": "CVE-2026-73558", "public_date": "2026-08-13T15:06:06Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-73558\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-73558\nhttps://github.com/vllm-project/vllm/commit/451227cb3ff07989698fed982c2d3e4300257924\nhttps://github.com/vllm-project/vllm/issues/42860\nhttps://github.com/vllm-project/vllm/pull/49660\nhttps://github.com/vllm-project/vllm/releases/tag/v0.27.0\nhttps://github.com/vllm-project/vllm/security/advisories/GHSA-7m6h-x95x-82q5"], "threat_severity": "Moderate"}