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OpenAdvisories
High WID-SEC-2026-2842 5 CVEs

vllm: Mehrere Schwachstellen

Published Aug 13, 2026 · Updated Nov 9, 2026 · Revision 8 · de

Summary

Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Informationen offenzulegen, Daten zu manipulieren, Sicherheitsmaßnahmen zu umgehen oder einen Denial-of-Service-Zustand herbeizuführen.

Product

Open Source vLLM ist eine Open-Source-Bibliothek für schnelle und effiziente Inferenz von Large Language Models (LLMs).

Vulnerabilities 5

  • Medium 6.5

    vLLM: Completion prompt lists fan out into unbounded engine requests

    vLLM is an inference and serving engine for large language models. From 0.19.0 until 0.26.0, the /v1/completions CompletionRequest.prompt field in vllm/entrypoints/openai/completion/protocol.py accepts an unbounded list[str] or list[list[int]], prompt_to_seq(…

  • Medium 6.3

    vLLM: Incomplete CVE-2025-62164 remediation can be bypassed by concurrent prompt parts

    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…

  • Medium 5.3

    vLLM: Unauthenticated Internal Path and Username Disclosure via Validation Error Messages

    vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the validation_exception_handler in vllm/entrypoints/openai/server_utils.py converts FastAPI RequestValidationError objects with str(exc), and sanitize_message in vllm/entrypo…

  • Medium 5.3

    vLLM: ReDoS via structured_outputs.regex in the lm-format-enforcer backend (no compile timeout) — missed sibling of CVE-2026-55574

    vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the structured_outputs.regex parameter in vllm/v1/structured_output/backend_lm_format_enforcer.py is passed to lmformatenforcer.RegexParser without compile_regex_with_timeout …

  • Medium 5.3

    vLLM: Cross-User Data Leak Vulnerability

    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 sa…

Revision history

  1. #8

    Neue Updates von Red Hat aufgenommen

  2. #7

    Neue Updates von Red Hat aufgenommen

  3. #6

    Neue Updates von Red Hat aufgenommen

  4. #5

    Neue Updates von Red Hat aufgenommen

  5. #4

    Neue Updates von Red Hat aufgenommen

  6. #3

    Neue Updates von Red Hat aufgenommen

  7. #2

    Neue Updates von Red Hat aufgenommen

  8. #1

    Initiale Fassung

References