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| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2026-40086 | 5.3 medium | 1.0% | danielgatis, rembg | Rembg is a tool to remove images background. Prior to 2.0.75, a path traversal vulnerability in the rembg HTTP server allows unauthenticated remote attackers to read arbitrary files from the server's filesystem. By sending a crafted request with a malicious model_path parameter, an attacker can force the server to attempt loading any file as an ONNX model, revealing file existence, permissions, and potentially file contents through error messages. This vulnerability is fixed in 2.0.75. | Mon 26 Oct |
| CVE-2026-75093 | 2.1 low | 1.0% | no CPE yet | 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. | 22 Oct |
| CVE-2026-115924 | 8.6 high | 0.00% | no CPE yet | Rembg is a tool to remove images background. Prior to 2.0.82, the public /api/remove endpoint passes attacker-controlled extras.sam_model through rembg/commands/s_command.py to rembg/sessions/sam.py, where it is interpolated into SAM filenames and GitHub download URLs passed to pooch.retrieve() without a pinned hash. An unauthenticated attacker can supply a sam_model value combining path traversal with URL fragment syntax to exploit the HTTP request discarding the fragment while the local POSIX path retains it. The first request primes the attacker-selected path tree, and an identical repeated request downloads malicious ONNX encoder and decoder files beside a readable target file. The graphs can load the target through ONNX external data and encode its bytes into the returned PNG mask, disclosing complete files accessible to the rembg process and allowing arbitrary ONNX computation. This issue is fixed in version 2.0.82. | Tue 27 Oct |
| CVE-2026-49114 | 6.8 medium | 0.00% | linuxfoundation, onnx | In ONNX before 1.21.0, the 'save_external_data' function builds the external-data file path from the model's external_data location field and opens it for writing without 'O_NOFOLLOW/O_EXCL', after a non-atomic 'os.path.isfile()' check. A local attacker with write access to the directory where a victim serializes external data can deterministically pre-plant a symlink that is being followed, causing the victim's write to append to any file the victim can write, e.g. ~/.ssh/authorized_keys, cron files, or application configs. Fixed in 1.21.0. | 15 Oct |
| CVE-2026-115920 | 6.2 medium | 0.00% | no CPE yet | Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.2.1 until 1.22.0, ConvTranspose shape inference processes a weight tensor with fewer than three dimensions without a kernel_shape attribute by deriving an empty effective_kernel_shape and indexing it for the input's spatial dimensions. The flaw affects convTransposeShapeInference_opset11() and the current inference path, and an untrusted model that can pass onnx.checker.check_model() can crash onnx.shape_inference.infer_shapes(), causing denial of service without demonstrated confidentiality or integrity impact. This issue is fixed in version 1.22.0. | Mon 26 Oct |
| CVE-2026-115919 | 6.2 medium | 0.00% | no CPE yet | Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, RNNShapeInference_opset1_to_6() processes malformed RNN and LSTM nodes from opsets 1 through 6 and GRU nodes from opsets 3 through 6 without verifying that the first input has rank three. The function accesses dim(0) and dim(1) on an undersized protobuf shape, which can read outside the intended repeated field and terminate an application when onnx.shape_inference.infer_shapes() processes an untrusted model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0. | Mon 26 Oct |
| CVE-2026-115922 | 6.2 medium | 0.00% | no CPE yet | Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0. | Mon 26 Oct |
| CVE-2026-115921 | 6.2 medium | 0.00% | no CPE yet | Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.4.0 until 1.22.0, ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9 do not safely validate the required num_scan_inputs attribute. A missing attribute is dereferenced as a null pointer, while a negative value is converted to size_t and can reach an exceptional oversized allocation path. An untrusted model can therefore terminate or disrupt shape inference, including a negative-value model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0. | Mon 26 Oct |
| CVE-2026-40979 | 6.1 medium | 0.00% | 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) | Mon 26 Oct |
| CVE-2026-111991 | 2.1 low | 0.00% | no CPE yet | 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. | 22 Oct |
| CVE-2026-108954 | 2.1 low | 0.00% | no CPE yet | 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. | 14 Oct |
| CVE-2026-11329 | 2.0 low | 0.00% | no CPE yet | A vulnerability has been found in onnx onnx-mlir up to 0.5.0.0. Affected by this issue is the function generate_hash_key of the file src/Runtime/python/torch_onnxmlir/src/torch_onnxmlir/backend.py of the component Placeholder Node Cache Handler. Such manipulation leads to use of weak hash. An attack has to be approached locally. A high complexity level is associated with this attack. The exploitation is known to be difficult. The name of the patch is 72c5187ff6d13c2c2b3d3789b8f5faf99f08a5b4. Applying a patch is advised to resolve this issue. | Mon 26 Oct |
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EPSS scores: 2026-10-29. NVD cursor: 2026-10-29 00:22. The News page shows only notable CVEs (KEV, your stack, new criticals, EPSS over 50%, or active exploitation per SSVC); this table has all of them.