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WongKinYiu yolov9

A security vulnerability has been detected in WongKinYiu yolov9 0.1. This vulnerability affects the function DetectMultiBackend.__init__ of the file models/common.py of the component ONNX Metadata Handler. The manipulation of the argument names/stride leads to code injection. Remote exploitation of the attack is possible. The exploit has been disclosed publicly and may be used. The project was informed of the problem early through an issue report but has not responded yet.

k2-fsa sherpa-onnx

A vulnerability was found in k2-fsa sherpa-onnx up to 1.13.8. This issue affects the function OfflineTransducerGreedySearchDecoder::Decode of the file sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.cc of the component Offline Transducer Greedy Search Decoder. The manipulation of the argument vocab_size results in buffer overflow. The attack can be launched remotely. The exploit has been made public and could be used. If only official model packs are used and they always keep metadata consistent with the joiner, the bug may not be reachable in normal supported usage. However, from a security and robustness standpoint, the code still contains the vulnerability because it trusts unvalidated model metadata and can be triggered by a malformed or tampered model pack.

sonos tract

Tract is a tiny, no-nonsense, self-contained TensorFlow and ONNX inference toolkit. Prior to 0.21.16, 0.22.2, and 0.23.1, tract-nnef uses unchecked usize multiplication in nnef/src/tensors.rs read_tensor for attacker-controlled tensor dimensions, the allocation size, and the reported tensor length. Loading a crafted NNEF archive through model_for_path or model_for_read reaches the default DatLoader and can make the wrapped size check accept a small allocation while data/src/tensor.rs as_slice_unchecked creates a much larger logical slice. Model construction through as_uniform can then read beyond the heap allocation and disclose adjacent data, and later access can terminate the process with a segmentation fault. The affected dense numeric tensor path does not include the independently guarded bool, String, or block-quant paths, and no out-of-bounds write or code execution was demonstrated. This issue is fixed in versions 0.21.16, 0.22.2, and 0.23.1.

sonos tract

Tract is a tiny, no-nonsense, self-contained TensorFlow and ONNX inference toolkit. Prior to 0.21.17, 0.22.3, and 0.23.2, the tract-onnx crate passes the attacker-controlled external_data location from an ONNX model through onnx/src/tensor.rs get_external_resources and joins the value to the model directory without rejecting absolute paths or parent directory components. Loading an untrusted model through model_for_path can therefore make onnx/src/data_resolver.rs MmapDataResolver open an arbitrary local file and place the file contents into model tensors or inference output. Attacker-controlled offset and length fields can also select an out-of-range mapping slice and cause a denial of service, but the flaw does not write files or execute code. This issue is fixed in versions 0.21.17, 0.22.3, and 0.23.2.

CVHub520 X-AnyLabeling

X-AnyLabeling's model downloader disabled TLS certificate verification. download_with_retry in anylabeling/services/auto_labeling/model.py built a context with ssl._create_unverified_context() and passed it to urllib.request.urlopen, so neither the certificate chain nor the hostname was checked on any model download, and models are fetched over HTTPS from the project's release host. Any party positioned to intercept that connection could therefore answer it with content of their own choosing. The response is written to a .part file and moved into place with os.replace, and the only post-download check, safe_check_model, validates the file's format rather than its provenance: no hash or signature is compared against an expected value. For an ONNX target the substituted file passes onnx.checker.check_model and is then used for inference, so the attacker chooses the model that produces the application's annotations. For a .pth or .pt target, which the shipped SAM2 video, YOLOE, UPN and open_vision configurations use, the check worker calls torch.load without weights_only, so a substituted file is unpickled and executes code of the attacker's choosing on PyTorch releases predating the weights_only default.

sonos tract

A security vulnerability has been detected in sonos tract up to 0.23.4. This impacts the function Tensor::from_raw_dt_align of the file data/src/tensor.rs of the component ONNX Initializer Loader. Such manipulation leads to incorrect calculation of buffer size. The attack may be launched remotely. The exploit has been disclosed publicly and may be used. The name of the patch is 66b10bda8895f4bfaf8c205361f0125cdf51f99b. It is best practice to apply a patch to resolve this issue.

onnx · Linuxfoundation onnx

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.9.0 before 1.22.0, onnx.version_converter.convert_version() can dereference a null pointer in Upsample_6_7::adapt_upsample_6_7() in onnx/version_converter/adapters/upsample_6_7.h when processing an untrusted model with an Upsample node that has zero inputs, causing an unrecoverable denial of service. This issue is fixed in version 1.22.0.

onnx

A weakness has been identified in onnx up to 1.21.x. This vulnerability affects the function convPoolShapeInference_opset19 of the file onnx/defs/nn/old.cc of the component onnxruntime. This manipulation causes out-of-bounds read. It is possible to initiate the attack remotely. The exploit has been made available to the public and could be used for attacks. Patch name: a7bf3a0f1d18bb62575236ef6e4944980c40e045. It is recommended to apply a patch to fix this issue.

Spring AI · Vmware spring ai

In Spring AI, having access to a shared environment can expose the ONNX model used by the application. Affected versions: Spring AI: 1.0.0 - 1.0.5 (fixed in 1.0.6), 1.1.0 - 1.1.4 (fixed in 1.1.5)