CVE-2026-105754 vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path accepts caller-supplied tensors in the features.kwargsdata field, cache identifiers in the features.mmhashes field, ranges in the...

CVE-2026-92971

InternLM LMDeploy through 0.17.0 contains a reachable assertion vulnerability in the DistServe decode migration loop that allows unauthenticated attackers to terminate the inference engine. Attackers can submit a migrationrequest with an empty remoteblockids list to trigger an AssertionError that...

CVE-2026-92971 InternLM LMDeploy through 0.17.0 Assertion Denial of Service

InternLM LMDeploy through 0.17.0 contains a reachable assertion vulnerability in the DistServe decode migration loop that allows unauthenticated attackers to terminate the inference engine. Attackers can submit a migrationrequest with an empty remoteblockids list to trigger an AssertionError that...

Inference-Engine Fingerprinting Attacks Are Practical: Exploring Model-Driven Environmental Discovery, Exploitation, and Escape

Frontier AI models are rapidly gaining the ability to exploit vulnerabilities in complex pieces of software. The risk is not theoretical, as evidenced by recent sandbox escapes performed by frontier models at OpenAI and Anthropic. Discussions of how to sandbox inference stack components often foc...

CVE-2026-73560 vLLM: SSRF + arbitrary local file read in MiMoV2OmniMultiModalProcessor `_fetch_image` and audio loader bypass MediaConnector protections

vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the MiMoV2OmniMultiModalProcessor in vllm/transformersutils/processors/mimov2omni.py passes attacker-controlled image and audio strings through fetchimage, requests.get, and Image.open instead of MediaConnector,...

CVE-2026-73558: Integer Overflow or Wraparound

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 activationkernels.cu can cause actandmulkernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or...

CVE-2026-44223

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. The extracthiddenstates speculative decoding proposer returns a tensor with an incorrect shape after the first decode step. This can be triggered by a remote attacker sending a request that uses sampling...

onnx onnxruntime old.cc convPoolShapeInference_opset19 out-of-bounds

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PT-2026-29877

Name of the Vulnerable Software and Affected Versions vLLM versions 0.5.5 through 0.17.999 Description vLLM, an inference and serving engine for large language models LLMs, exhibits an inconsistency in audio processing. Versions 0.5.5 through 0.17.999 utilize numpy.mean for mono downmixing via...

FoolishScan

Foolish Scan v2.3 Gold Master Context-Aware CTF & Lab Re...

FoolishScan-

Foolish Scan v2.3 Gold Master Context-Aware CTF & Lab Re...

EUVD-2025-16189

Malicious code in bioql PyPI...

CVE-2025-52566

llama.cpp is an inference of several LLM models in C/C++. Prior to version b5721, there is a signed vs. unsigned integer overflow in llama.cpp's tokenizer implementation llamavocab::tokenize src/llama-vocab.cpp:3036 resulting in unintended behavior in tokens copying size comparison. Allowing...

CVE-2025-48942

vLLM is an inference and serving engine for large language models LLMs. In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid jsonschema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex...

CVE-2025-48944 vLLM Tool Schema allows DoS via Malformed pattern and type Fields

vLLM is an inference and serving engine for large language models LLMs. In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality ...

PYSEC-2025-53

vLLM is an inference and serving engine for large language models LLMs. Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT Time to First Token. These timing differences...

编号撤回

vLLM is vLLM open source a high throughput and memory efficient inference and service engine for LLM. This CVE number has been withdrawn...

vLLM 安全漏洞

vLLM is a high throughput and memory efficient inference and service engine for LLM from the vLLM open source. A security vulnerability exists in vLLM that stems from a maliciously constructed statement that could lead to a hash collision, which could lead to cache reuse, which could interfere wi...

Use of Weak Hash

Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Use of Weak Hash due to the use of a predictable constant value in the Python 3.12 built-in hash function. An attacker can interfere with subsequent...

Spring AI with Groq - a blazingly fast AI inference engine

Faster information processing not only informs - it transforms how we perceive and innovate. Spring AI, a powerful framework for integrating AI capabilities into Spring applications, now offers support for Groq - a blazingly fast AI inference engine with support for Tool/Function calling...