Vulnerability database
Every flaw the U.S. government’s cybersecurity agency (CISA) has confirmed attackers are using, plus the new WordPress plugin and theme bugs published each day, sorted by what they mean for a small business. We refresh it twice a day. If you see something your website or office runs, tell us.
220 results · page 4 of 5
Sep 17, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause a denial of service by loading a misconfigured modelCVE-2025-23336 · Other software · NVIDIA · reported by psirt@nvidia.comMedium 4.4
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause a denial of service by loading a misconfigured model. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Sep 17, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause memory corruption by identifying and accessing the sCVE-2025-23329 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause memory corruption by identifying and accessing the shared memory region used by the Python backend. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Sep 17, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an out-of-bounds write through a specially crafted iCVE-2025-23328 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an out-of-bounds write through a specially crafted input. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Sep 17, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause a remote code execution by maCVE-2025-23316 · Other software · NVIDIA · reported by psirt@nvidia.comCritical 9.8
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause a remote code execution by manipulating the model name parameter in the model control APIs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Sep 17, 2025NVIDIA Triton Inference Server contains a vulnerability in the DALI backend where an attacker may cause an improper input validation issue.CVE-2025-23268 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 8
NVIDIA Triton Inference Server contains a vulnerability in the DALI backend where an attacker may cause an improper input validation issue. A successful exploit of this vulnerability may lead to code execution.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Aug 21, 2025vLLM is an inference and serving engine for large language models (LLMs).CVE-2025-48956 · Other software · vllm-project · reported by GitHubHigh 7.5
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux and the Tensor RT backend contain a vulnerability where an attacker could cause an underflow by a specific CVE-2025-23335 · Other software · NVIDIA · reported by psirt@nvidia.comMedium 4.4
NVIDIA Triton Inference Server for Windows and Linux and the Tensor RT backend contain a vulnerability where an attacker could cause an underflow by a specific model configuration and a specific input. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by sendCVE-2025-23334 · Other software · NVIDIA · reported by psirt@nvidia.comMedium 5.9
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by sending a request. A successful exploit of this vulnerability might lead to information disclosure.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by maniCVE-2025-23333 · Other software · NVIDIA · reported by psirt@nvidia.comMedium 5.9
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by manipulating shared memory data. A successful exploit of this vulnerability might lead to information disclosure.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a memory allocation with excessive size value, leading toCVE-2025-23331 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a memory allocation with excessive size value, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an integer overflow through specially crafted inputsCVE-2025-23327 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an integer overflow through specially crafted inputs. A successful exploit of this vulnerability might lead to denial of service and data tampering.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an integer overflow through a specially crafted inpuCVE-2025-23326 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an integer overflow through a specially crafted input. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause uncontrolled recursion through a specially crafted iCVE-2025-23325 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause uncontrolled recursion through a specially crafted input. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentatCVE-2025-23324 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentatCVE-2025-23323 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where multiple requests could cause a double free when a stream is cancelled beforCVE-2025-23322 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where multiple requests could cause a double free when a stream is cancelled before it is processed. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a divide by zero issue by issuing an invalid request.CVE-2025-23321 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a divide by zero issue by issuing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause the shared memory limit to beCVE-2025-23320 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 7.5
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause the shared memory limit to be exceeded by sending a very large request. A successful exploit of this vulnerability might lead to information disclosure.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write by senCVE-2025-23319 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 8.1
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write by sending a request. A successful exploit of this vulnerability might lead to remote code execution, denial of service, data tampering, or information disclosure.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write.CVE-2025-23318 · Other software · NVIDIA · reported by psirt@nvidia.comHigh 8.1
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server contains a vulnerability in the HTTP server, where an attacker could start a reverse shell by sending a specially crafted HTTP reCVE-2025-23317 · Other software · NVIDIA · reported by psirt@nvidia.comCritical 9.1
NVIDIA Triton Inference Server contains a vulnerability in the HTTP server, where an attacker could start a reverse shell by sending a specially crafted HTTP request. A successful exploit of this vulnerability might lead to remote code execution, denial of service, data tampering, or information disclosure.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a stack overflow through specially crafted HTTP requests.CVE-2025-23311 · Other software · NVIDIA · reported by psirt@nvidia.comCritical 9.8
NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a stack overflow through specially crafted HTTP requests. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, or data tampering.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Aug 6, 2025NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause stack buffer overflow by specially crafted inputs.CVE-2025-23310 · Other software · NVIDIA · reported by psirt@nvidia.comCritical 9.8
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause stack buffer overflow by specially crafted inputs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordnvd.nist.govnvidia.custhelp.com
Jul 29, 2025BentoML is a Python library for building online serving systems optimized for AI apps and model inference.CVE-2025-54381 · Other software · bentoml · reported by GitHubCritical 9.9
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.0 until 1.4.19, the file upload processing system contains an SSRF vulnerability that allows unauthenticated remote attackers to force the server to make arbitrary HTTP requests. The vulnerability stems from the multipart form data and JSON request handlers, which automatically download files from user-provided URLs without validating whether those URLs point to internal network addresses, cloud metadata endpoints, or other restricted resources. The documentation explicitly promotes this URL-based file upload feature, making it an intended design that exposes all deployed services to SSRF attacks by default. Version 1.4.19 contains a patch for the issue.
- Product
- BentoML
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Jul 10, 2025llama.cpp is an inference of several LLM models in C/C++.CVE-2025-53630 · Other software · ggml-org · reported by GitHubHigh 8.9
llama.cpp is an inference of several LLM models in C/C++. Integer Overflow in the gguf_init_from_file_impl function in ggml/src/gguf.cpp can lead to Heap Out-of-Bounds Read/Write. This vulnerability is fixed in commit 26a48ad699d50b6268900062661bd22f3e792579.
- Product
- llama.cpp
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Jul 2, 2025Incorrect access control in Xinference before v1.4.0 allows attackers to access the Web GUI without authentication.CVE-2025-45424 · Other software · Xinference · reported by MITREMedium 5.3
Incorrect access control in Xinference before v1.4.0 allows attackers to access the Web GUI without authentication.
- Product
- Xinference
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Jul 1, 2025A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server.CVE-2025-6920 · Other software · Red Hat · reported by Red HatMedium 5.3
A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/* endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows unauthorized users to access the same inference features available on protected endpoints, potentially exposing sensitive functionality or allowing unintended access to backend resources.
- Product
- Red Hat AI Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordaccess.redhat.combugzilla.redhat.com
Jun 24, 2025llama.cpp is an inference of several LLM models in C/C++.CVE-2025-52566 · Other software · ggml-org · reported by GitHubHigh 8.6
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 (llama_vocab::tokenize) (src/llama-vocab.cpp:3036) resulting in unintended behavior in tokens copying size comparison. Allowing heap-overflowing llama.cpp inferencing engine with carefully manipulated text input during tokenization process. This issue has been patched in version b5721.
- Product
- llama.cpp
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Jun 17, 2025llama.cpp is an inference of several LLM models in C/C++.CVE-2025-49847 · Other software · ggml-org · reported by GitHubHigh 8.8
llama.cpp is an inference of several LLM models in C/C++. Prior to version b5662, an attacker‐supplied GGUF model vocabulary can trigger a buffer overflow in llama.cpp’s vocabulary‐loading code. Specifically, the helper _try_copy in llama.cpp/src/vocab.cpp: llama_vocab::impl::token_to_piece() casts a very large size_t token length into an int32_t, causing the length check (if (length < (int32_t)size)) to be bypassed. As a result, memcpy is still called with that oversized size, letting a malicious model overwrite memory beyond the intended buffer. This can lead to arbitrary memory corruption and potential code execution. This issue has been patched in version b5662.
- Product
- llama.cpp
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 30, 2025vLLM is an inference and serving engine for large language models (LLMs).CVE-2025-48944 · Other software · vllm-project · reported by GitHubMedium 6.5
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 is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 30, 2025vLLM is an inference and serving engine for large language models (LLMs).CVE-2025-48943 · Other software · vllm-project · reported by GitHubMedium 6.5
vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 30, 2025vLLM is an inference and serving engine for large language models (LLMs).CVE-2025-48942 · Other software · vllm-project · reported by GitHubMedium 6.5
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 json_schema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 30, 2025vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entryCVE-2025-48887 · Other software · vllm-project · reported by GitHubMedium 6.5
vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 29, 2025vLLM is an inference and serving engine for large language models (LLMs).CVE-2025-46722 · Other software · vllm-project · reported by GitHubMedium 4.2
vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 29, 2025vLLM is an inference and serving engine for large language models (LLMs).CVE-2025-46570 · Other software · vllm-project · reported by GitHubLow 2.6
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 caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
May 20, 2025vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PCVE-2025-47277 · Other software · vllm-project · reported by GitHubCritical 9.8
vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side. The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network. The default and intentional behavior from PyTorch is that the `TCPStore` interface listens on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. As of version 0.8.5, vLLM limit
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recorddocs.vllm.aigithub.com
May 6, 2025vLLM is an inference and serving engine for large language models.CVE-2025-30165 · Other software · vllm-project · reported by GitHubHigh 8
vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host. When data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Apr 29, 2025vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.CVE-2025-46560 · Other software · vllm-project · reported by GitHubMedium 6.5
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., <|audio_|>, <|image_|>) with repeated tokens based on precomputed lengths. Due to inefficient list concatenation operations, the algorithm exhibits quadratic time complexity (O(n²)), allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Apr 29, 2025vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.CVE-2025-32444 · Other software · vllm-project · reported by GitHubCritical 10
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Apr 29, 2025vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.CVE-2025-30202 · Other software · vllm-project · reported by GitHubHigh 7.5
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts. Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker. By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher. This issue has been patched in version 0.8.5.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Apr 15, 2025A vulnerability, which was classified as critical, has been found in Xorbits Inference up to 1.4.1.CVE-2025-3622 · Other software · Xorbits · reported by VulDBMedium 5.1
A vulnerability, which was classified as critical, has been found in Xorbits Inference up to 1.4.1. This issue affects the function load of the file xinference/thirdparty/cosyvoice/cli/model.py. The manipulation leads to deserialization.
- Product
- Inference
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Apr 9, 2025BentoML is a Python library for building online serving systems optimized for AI apps and model inference.CVE-2025-32375 · Other software · bentoml · reported by GitHubCritical 9.8
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.8, there was an insecure deserialization in BentoML's runner server. By setting specific headers and parameters in the POST request, it is possible to execute any unauthorized arbitrary code on the server, which will grant the attackers to have the initial access and information disclosure on the server. This vulnerability is fixed in 1.4.8.
- Product
- BentoML
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Apr 4, 2025BentoML is a Python library for building online serving systems optimized for AI apps and model inference.CVE-2025-27520 · Other software · bentoml · reported by GitHubCritical 9.8
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. A Remote Code Execution (RCE) vulnerability caused by insecure deserialization has been identified in the latest version (v1.4.2) of BentoML. It allows any unauthenticated user to execute arbitrary code on the server. It exists an unsafe code segment in serde.py. This vulnerability is fixed in 1.4.3.
- Product
- BentoML
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Mar 19, 2025vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.CVE-2025-29783 · Other software · vllm-project · reported by GitHubCritical 9
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. When vLLM is configured to use Mooncake, unsafe deserialization exposed directly over ZMQ/TCP on all network interfaces will allow attackers to execute remote code on distributed hosts. This is a remote code execution vulnerability impacting any deployments using Mooncake to distribute KV across distributed hosts. This vulnerability is fixed in 0.8.0.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Mar 19, 2025vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.CVE-2025-29770 · Other software · vllm-project · reported by GitHubMedium 6.5
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. The outlines library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server. The affected code in vLLM is vllm/model_executor/guided_decoding/outlines_logits_processors.py, which unconditionally uses the cache from outlines. A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space. Note that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the guided_decoding_backend key of the extra_body field of the request. This issue applies only to the V0 engine and is fixed in 0.8.0.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Feb 11, 2025NVIDIA Triton Inference Server contains a vulnerability in the model loading API, where a user could cause an integer overflow or wraparound error by loading a CVE-2024-53880 · Other software · NVIDIA · reported by psirt@nvidia.comMedium 4.9
NVIDIA Triton Inference Server contains a vulnerability in the model loading API, where a user could cause an integer overflow or wraparound error by loading a model with an extra-large file size that overflows an internal variable. A successful exploit of this vulnerability might lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Feb 7, 2025vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.CVE-2025-25183 · Other software · vllm-project · reported by GitHubLow 2.6
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Jan 27, 2025vLLM is a library for LLM inference and serving.CVE-2025-24357 · Other software · vllm-project · reported by GitHubHigh 7.5
vLLM is a library for LLM inference and serving. vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It uses the torch.load function and the weights_only parameter defaults to False. When torch.load loads malicious pickle data, it will execute arbitrary code during unpickling. This vulnerability is fixed in v0.7.0.
- Product
- vllm
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Nov 10, 2024Certain Cypress (and Broadcom) Wireless Combo chips, when a January 2021 firmware update is not present, allow inferences about memory content via a "Spectra" aCVE-2020-10369 · Other software · reported by MITREMedium 5.5
Certain Cypress (and Broadcom) Wireless Combo chips, when a January 2021 firmware update is not present, allow inferences about memory content via a "Spectra" attack.
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Full record and what to do →NVD recordbugzilla.redhat.comgithub.com
Oct 1, 2024NVIDIA Triton Inference Server contains a vulnerability where a user may cause an out-of-bounds read issue by releasing a shared memory region while it is in usCVE-2024-0116 · Other software · NVIDIA · reported by psirt@nvidia.comMedium 4.9
NVIDIA Triton Inference Server contains a vulnerability where a user may cause an out-of-bounds read issue by releasing a shared memory region while it is in use. A successful exploit of this vulnerability may lead to denial of service.
- Product
- Triton Inference Server
- What to do
- Apply the vendor’s update. The NVD record links to the advisory.
Where this comes from: the exploited list is CISA’s Known Exploited Vulnerabilities catalog. Everything else is the whole National Vulnerability Database, where each record is published by the team that found the bug (Wordfence, Patchstack, WPScan, the vendors themselves and others); we show who. Exploit odds are FIRST’s EPSS, updated daily. Titles and categories are ours, so check the linked record before acting on a detail. For a write-up of the big ones, see our advisories. Every vendor, and every WordPress plugin and theme with a recorded flaw, also has its own page. All of it is free to reuse as JSON, CSV or RSS.
Weekly security brief
The flaws that hit local websites, every Friday.
One short email: what got exploited this week, which WordPress plugins broke, and what to check. Unsubscribe in one click.
Tell us what you're building.
A website, your marketing, a security check, an app, or the thing you've been running out of a spreadsheet for three years. A few lines is plenty.
Not ready to talk yet? Run the free website check and see what Google sees.