CVE-2026-73557

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{"bugzilla": {"description": "vllm: vLLM: Incomplete remediation allows bypass via concurrent prompt processing", "id": "2515401", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2515401"}, "csaw": false, "cvss3": {"cvss3_base_score": "5.3", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L", "status": "draft"}, "cwe": "CWE-367", "details": ["vLLM is an inference and serving engine for large language models. From 0.20.2rc0 until 0.26.0, safe_load_prompt_embeds in vllm/renderers/embed_utils.py uses torch.sparse.check_sparse_tensor_invariants, whose process-global save, enable, and restore state can be raced by concurrent prompt_embeds parts submitted to POST /v1/chat/completions through AsyncMultiModalItemTracker.resolve_items, asyncio.gather, and the default executor, allowing an invalid sparse tensor to reach tensor.to_dense despite the CVE-2025-62164 guard when enable_prompt_embeds is enabled. This issue is fixed in version 0.26.0.", "A flaw was found in vLLM, an inference and serving engine for large language models. An incomplete remediation for a previous vulnerability (CVE-2025-62164) can be bypassed. This occurs when concurrent prompt parts are processed, leading to a race condition in the handling of sparse tensors. This vulnerability could allow an attacker to cause a denial of service."], "name": "CVE-2026-73557", "public_date": "2026-08-13T15:00:15Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-73557\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-73557\nhttps://github.com/vllm-project/vllm/commit/793cf79c89d4049124e756915468ac30318f2e50\nhttps://github.com/vllm-project/vllm/pull/48583\nhttps://github.com/vllm-project/vllm/releases/tag/v0.26.0\nhttps://github.com/vllm-project/vllm/security/advisories/GHSA-pr7f-p5mw-fc87"], "threat_severity": "Moderate"}

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