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55 vulnerabilities found for vllm by vllm-project
CVE-2026-78684 (GCVE-0-2026-78684)
Vulnerability from cvelistv5
Published
2026-08-25 11:33
Modified
2026-08-27 15:08
Severity ?
6.9 (Medium) - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
5.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
5.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
VLAI Severity ?
EPSS score ?
CWE
- CWE-400 - Uncontrolled Resource Consumption
Summary
vLLM before 0.27.0 fails to properly classify DeepStream as a GPU backend and omits pixel-limit enforcement in its decode path. Unauthenticated attackers can activate DeepStream at request time to initialize the process-wide GPU decode pool and submit video that bypasses resource controls, causing partial denial of service for concurrent requests.
References
| URL | Tags | |
|---|---|---|
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: 0 ≤ |
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CVE-2026-73560 (GCVE-0-2026-73560)
Vulnerability from cvelistv5
Published
2026-08-17 20:17
Modified
2026-08-18 16:00
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Summary
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the MiMoV2OmniMultiModalProcessor in vllm/transformers_utils/processors/mimo_v2_omni.py passes attacker-controlled image and audio strings through _fetch_image, requests.get, and Image.open instead of MediaConnector, bypassing allowed_media_domains and allowed_local_media_path protections and allowing server-side requests and reads of arbitrary files accessible to the vLLM process. This issue is fixed in version 0.26.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.26.0 |
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CVE-2026-71486 (GCVE-0-2026-71486)
Vulnerability from cvelistv5
Published
2026-08-17 20:13
Modified
2026-08-18 12:34
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the /v1/completions/derender and /v1/chat/completions/derender endpoints accept caller-supplied GenerateResponse objects whose generate_responses, choices, token_ids, prompt_logprobs, logprobs.content, top_logprobs, and routed_experts structures are processed by OnlineDerenderer and tokenizer.decode before max_model_len, max_tokens, max_num_seqs, or response-size limits are enforced, allowing an authenticated API client to consume excessive CPU and memory and produce oversized responses. This issue is fixed in version 0.26.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.26.0 |
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CVE-2026-73559 (GCVE-0-2026-73559)
Vulnerability from cvelistv5
Published
2026-08-13 15:09
Modified
2026-08-14 16:51
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-400 - Uncontrolled Resource Consumption
Summary
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() in vllm/renderers/inputs/preprocess.py and OnlineRenderer.preprocess_completion() in vllm/renderers/online_renderer.py expand every element, and vllm/entrypoints/openai/completion/serving.py creates one engine generator and response slot per prompt, allowing an authenticated API client to exhaust CPU, memory, async scheduling capacity, engine request slots, and response buffering with one request. This issue is fixed in version 0.26.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.19.0, < 0.26.0 |
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CVE-2026-73558 (GCVE-0-2026-73558)
Vulnerability from cvelistv5
Published
2026-08-13 15:06
Modified
2026-08-13 15:38
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-190 - Integer Overflow or Wraparound
Summary
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.
References
| URL | Tags | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.27.0 |
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CVE-2026-73557 (GCVE-0-2026-73557)
Vulnerability from cvelistv5
Published
2026-08-13 15:00
Modified
2026-08-13 17:52
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-362 - Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')
Summary
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.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.20.2rc0, < 0.26.0 |
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CVE-2026-73556 (GCVE-0-2026-73556)
Vulnerability from cvelistv5
Published
2026-08-13 14:56
Modified
2026-08-14 16:52
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
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 or validation in validate_structured_output_request_lm_format_enforcer, allowing an unauthenticated /v1/completions request against the lm-format-enforcer backend to consume a CPU core and stall the structured-output engine path with a catastrophic regular expression. This issue is fixed in version 0.26.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.26.0 |
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CVE-2026-73555 (GCVE-0-2026-73555)
Vulnerability from cvelistv5
Published
2026-08-13 14:50
Modified
2026-08-14 15:57
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-209 - Generation of Error Message Containing Sensitive Information
Summary
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/entrypoints/utils.py does not remove traceback-style file paths, allowing unauthenticated malformed JSON requests to /v1/chat/completions, /v1/completions, /tokenize, and /detokenize to disclose the OS username, home and virtual-environment paths, Python version, internal package structure, line numbers, and endpoint handler names. This issue is fixed in version 0.26.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.26.0 |
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CVE-2026-55514 (GCVE-0-2026-55514)
Vulnerability from cvelistv5
Published
2026-07-06 20:07
Modified
2026-07-06 20:46
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-617 - Reachable Assertion
Summary
vLLM is a library for LLM inference and serving. From 0.12.0 to before 0.24.0, sending a pure prompt embeds payload in a /v1/completions request with a model using M-RoPE causes EngineCore to fail an assertion and fatally crash, shutting down the entire server application. Any remote user who is authorized to make a /v1/completions request can make such a request and induce a crash. This issue is fixed in version 0.24.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.12.0, < 0.24.0 |
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CVE-2026-55574 (GCVE-0-2026-55574)
Vulnerability from cvelistv5
Published
2026-07-06 20:05
Modified
2026-07-06 20:52
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1333 - Inefficient Regular Expression Complexity
Summary
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, the structured_outputs.regex API parameter passes a user-supplied regular expression string directly to the grammar compiler backends with no compilation timeout; in the xgrammar backend the string reaches the regex compiler with no guard, and in the outlines backend the validation step blocks structural issues such as lookarounds and backreferences but performs no complexity analysis, so a pattern with nested quantifiers passes all checks and causes exponential state-space expansion, allowing a single request containing an adversarial regex to hang an inference worker indefinitely and deny service. This issue is fixed in version 0.24.0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.24.0 |
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CVE-2026-54234 (GCVE-0-2026-54234)
Vulnerability from cvelistv5
Published
2026-07-06 19:49
Modified
2026-07-07 14:13
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, a frontend-legal multi-request speculative decoding workload can cause the rejection sampler to produce a recovered token equal to the model vocabulary size boundary value, which is then converted to negative one when the engine selects the next live token for a request and is written back into the drafter's input ids; that out-of-vocabulary value is later consumed by the model's embedding and attention path and crashes the engine worker with a GPU device-side assertion. The same triggering request sequence is reachable through the public gRPC Generate and Abort endpoints, so a remote client that can send generation requests can crash the shared engine worker, aborting concurrent requests and causing a service-wide denial of service for other clients of the deployment until the worker is restarted. This issue is fixed in version 0.24.0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.24.0 |
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CVE-2026-55646 (GCVE-0-2026-55646)
Vulnerability from cvelistv5
Published
2026-07-06 19:41
Modified
2026-07-06 19:49
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
vLLM is an inference and serving engine for large language models. From 0.22.0 to 0.23.0, the /v1/audio/transcriptions and /v1/audio/translations routes call request.file.read() to fully materialize an uploaded audio file into memory before vLLM checks the documented VLLM_MAX_AUDIO_CLIP_FILESIZE_MB compressed upload size limit (default 25 MB) later in the speech-to-text preprocessing step, so an API caller who can reach those routes can submit an oversized multipart upload and cause vLLM to allocate memory proportional to the uploaded file size before the request is rejected as too large, creating memory pressure or terminating the process depending on deployment resource limits. This issue is fixed in version 0.24.0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.22.0, < 0.24.0 |
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CVE-2026-47155 (GCVE-0-2026-47155)
Vulnerability from cvelistv5
Published
2026-06-22 22:20
Modified
2026-06-23 12:35
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-345 - Insufficient Verification of Data Authenticity
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
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CVE-2026-41523 (GCVE-0-2026-41523)
Vulnerability from cvelistv5
Published
2026-06-22 22:18
Modified
2026-08-25 12:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (python -O or PYTHONOPTIMIZE=1). This vulnerability is fixed in 0.22.0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.22.0 |
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CVE-2026-54232 (GCVE-0-2026-54232)
Vulnerability from cvelistv5
Published
2026-06-22 22:16
Modified
2026-06-23 14:30
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-427 - Uncontrolled Search Path Element
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.
References
| URL | Tags | ||||
|---|---|---|---|---|---|
|
|||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.22.1 |
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CVE-2026-54233 (GCVE-0-2026-54233)
Vulnerability from cvelistv5
Published
2026-06-22 22:10
Modified
2026-06-23 12:15
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-409 - Improper Handling of Highly Compressed Data (Data Amplification)
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0.
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.23.1rc0 |
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CVE-2026-54236 (GCVE-0-2026-54236)
Vulnerability from cvelistv5
Published
2026-06-22 22:09
Modified
2026-06-23 12:33
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-532 - Insertion of Sensitive Information into Log File
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.23.1rc0 |
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CVE-2026-54235 (GCVE-0-2026-54235)
Vulnerability from cvelistv5
Published
2026-06-22 21:59
Modified
2026-06-23 12:26
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1287 - Improper Validation of Specified Type of Input
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: < 0.23.1rc0 |
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CVE-2026-48746 (GCVE-0-2026-48746)
Vulnerability from cvelistv5
Published
2026-06-22 21:57
Modified
2026-08-19 12:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-444 - Inconsistent Interpretation of HTTP Requests ('HTTP Request/Response Smuggling')
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.3.0 until 0.22.0, a vulnerability in ASGI web servers and starlette's trust on those web servers enables an authentication bypass of the OpenAI API AuthenticationMiddleware. It allows to use the API without providing the configured VLLM_API_KEY or --api-key. This vulnerability is fixed in 0.22.0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.3.0, < 0.22.0 |
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CVE-2026-53923 (GCVE-0-2026-53923)
Vulnerability from cvelistv5
Published
2026-06-22 21:55
Modified
2026-06-23 15:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.5.5, < 0.23.1rc0 |
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CVE-2026-12491 (GCVE-0-2026-12491)
Vulnerability from cvelistv5
Published
2026-06-17 10:07
Modified
2026-07-07 07:44
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-115 - Misinterpretation of Input
Summary
A flaw was found in vLLM, an open-source library for large language model inference. This vulnerability arises from improper handling of image metadata, specifically EXIF orientation and PNG transparency (tRNS) data, during image processing. When images are converted to RGB, transparency information may be implicitly discarded or remapped, leading to unexpected rendering of transparent pixels and distortion of input content. This can result in the model misinterpreting image content, potentially affecting the integrity of processed data.
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
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CVE-2026-9540 (GCVE-0-2026-9540)
Vulnerability from cvelistv5
Published
2026-05-26 10:30
Modified
2026-05-26 13:47
Severity ?
5.5 (Medium) - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P
5.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L/E:P/RC:R
5.3 (Medium) - CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L/E:P/RC:R
5.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L/E:P/RC:R
5.3 (Medium) - CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L/E:P/RC:R
VLAI Severity ?
EPSS score ?
CWE
- CWE-404 - Denial of Service
Summary
A vulnerability was identified in vllm-project vllm 0.19.0. This issue affects some unknown processing of the component OpenAI-compatible Serving Path. Such manipulation leads to denial of service. It is possible to launch the attack remotely. The exploit is publicly available and might be used. The pull request to fix this issue awaits acceptance.
References
| URL | Tags | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: 0.19.0 cpe:2.3:a:vllm-project:vllm:*:*:*:*:*:*:*:* |
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CVE-2026-44223 (GCVE-0-2026-44223)
Vulnerability from cvelistv5
Published
2026-05-12 19:58
Modified
2026-06-22 21:49
Severity ?
VLAI Severity ?
EPSS score ?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0.
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.18.0, < 0.20.0 |
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CVE-2026-44222 (GCVE-0-2026-44222)
Vulnerability from cvelistv5
Published
2026-05-12 19:57
Modified
2026-05-13 12:24
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-129 - Improper Validation of Array Index
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.6.1, < 0.20.0 |
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CVE-2026-34756 (GCVE-0-2026-34756)
Vulnerability from cvelistv5
Published
2026-04-06 15:40
Modified
2026-08-25 12:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-770 - Allocation of Resources Without Limits or Throttling
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionRequest Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large n value. This completely blocks the Python asyncio event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. This vulnerability is fixed in 0.19.0.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.1.0, < 0.19.0 |
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CVE-2026-34755 (GCVE-0-2026-34755)
Vulnerability from cvelistv5
Published
2026-04-06 15:38
Modified
2026-08-25 12:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-770 - Allocation of Resources Without Limits or Throttling
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.7.0 to before 0.19.0, the VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The num_frames parameter (default: 32), which is enforced by the load_bytes() code path, is completely bypassed in the video/jpeg base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM. This vulnerability is fixed in 0.19.0.
References
| URL | Tags | ||||
|---|---|---|---|---|---|
|
|||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.7.0, < 0.19.0 |
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CVE-2026-34753 (GCVE-0-2026-34753)
Vulnerability from cvelistv5
Published
2026-04-06 15:36
Modified
2026-04-07 14:15
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions.
This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0.
References
| URL | Tags | ||||
|---|---|---|---|---|---|
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Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.16.0, < 0.19.0 |
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CVE-2026-34760 (GCVE-0-2026-34760)
Vulnerability from cvelistv5
Published
2026-04-02 18:59
Modified
2026-04-03 14:42
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-20 - Improper Input Validation
Summary
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing (to_mono), while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results in inconsistency between audio heard by humans (e.g., through headphones/regular speakers) and audio processed by AI models (Which infra via Librosa, such as vllm, transformer). This issue has been patched in version 0.18.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.5.5, < 0.18.0 |
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CVE-2026-27893 (GCVE-0-2026-27893)
Vulnerability from cvelistv5
Published
2026-03-26 23:56
Modified
2026-07-27 12:05
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-693 - Protection Mechanism Failure
Summary
vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.18.0, two model implementation files hardcode `trust_remote_code=True` when loading sub-components, bypassing the user's explicit `--trust-remote-code=False` security opt-out. This enables remote code execution via malicious model repositories even when the user has explicitly disabled remote code trust. Version 0.18.0 patches the issue.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.10.1, < 0.18.0 |
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CVE-2026-25960 (GCVE-0-2026-25960)
Vulnerability from cvelistv5
Published
2026-03-09 21:01
Modified
2026-07-21 12:04
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Summary
vLLM is an inference and serving engine for large language models (LLMs). The SSRF protection fix for CVE-2026-24779 add in 0.15.1 can be bypassed in the load_from_url_async method due to inconsistent URL parsing behavior between the validation layer and the actual HTTP client. The SSRF fix uses urllib3.util.parse_url() to validate and extract the hostname from user-provided URLs. However, load_from_url_async uses aiohttp for making the actual HTTP requests, and aiohttp internally uses the yarl library for URL parsing. This vulnerability in 0.17.0.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Version: >= 0.15.1, < 0.17.0 |
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