{"items":[{"id":"NVI-00008911","kind":"vulnerability","title":"ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank","revision":2,"unknowns":["Customer inventory, deployment, reachability and exploitation have not been inspected."],"lifecycle":"active","identifiers":["GHSA-6pwm-x34p-fvj2","CVE-2026-115920"],"plain_summary":"GitHub lists this reviewed advisory for onnx. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.","review_status":"source_listed","control_topics":[],"public_actions":[{"summary":"Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.","source_ids":["sr-2390ba8cd625fb3f8195eea4d320f2b6"],"action_type":"investigate","limitations":"Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system.","current_supported_target":null}],"schema_version":"1.1.0","source_metrics":[{"kind":"CVSS","as_of":"2026-10-28T21:16:43.000Z","value":"6.2 medium","meaning":"GitHub source severity; exploitability and local consequence require separate evidence.","source_id":"sr-293c3346858bd6a2c54f7d8a443b6a83"},{"kind":"EPSS","as_of":"2026-11-01T12:00:16.000Z","value":"0.178%","meaning":"FIRST EPSS model v2026.06.15; exploitation probability over 30 days, percentile 0.07414. Missing scores are unknown.","source_id":"sr-2390ba8cd625fb3f8195eea4d320f2b6"}],"why_it_matters":"Published affected context helps an owner decide what evidence to gather. This report has not examined your application.","related_sources":[{"id":"sr-293c3346858bd6a2c54f7d8a443b6a83","url":"https://github.com/advisories/GHSA-6pwm-x34p-fvj2","basis":"vendor_advisory","notes":"GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.","title":"GHSA-6pwm-x34p-fvj2","publisher":"GitHub Advisory Database","observed_at":"2026-10-31T21:56:08.420Z"},{"id":"sr-2390ba8cd625fb3f8195eea4d320f2b6","url":"https://www.first.org/epss/data.html","basis":"official_catalog","notes":"FIRST EPSS; probability of exploitation in the next 30 days, not local compromise. Dataset date/model retained; FIRST services terms apply.","title":"CVE-2026-115920","publisher":"FIRST EPSS","observed_at":"2026-11-01T14:28:32.267Z"}],"affected_context":[{"certainty":"source_listed","source_ids":["sr-293c3346858bd6a2c54f7d8a443b6a83"],"technology":"onnx","known_exclusions":[],"conditions_summary":"## Summary\n\nONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a `kernel_shape` attribute.\n\nThe inference function derived an empty `effective_kernel_shape` vector from the undersized weight tensor, then indexed that vector once for each spatial dimension of the input tensor. A crafted model passed `onnx.checker.check_model()` and caused SIGSEGV when passed to `onnx.shape_inference.infer_shapes()`.\n\n## Technical details\n\nFor an input shape such as `[1, 1, 5]`, shape inference computes one spatial input dimension. A weight shape such as `[1, 3]` has no spatial dimensions, so deriving `kernel_shape` from dimensions 2 onward produces an empty vector. The output loop nevertheless accesses `effective_kernel_shape[0]`.\n\nThe defect affected both the older `convTransposeShapeInference_opset11()` path and the then-current ConvTranspose inference implementation.\n\n## Impact\n\nAn application that performs shape inference on an untrusted model can be terminated, causing denial of service. The observed result was a deterministic process crash. No confidentiality or integrity impact was demonstrate","public_affected_ranges":[">= 1.2.1, <= 1.21.0"]}],"null_published_at":"2026-11-04T18:04:03.182Z","source_updated_at":"2026-10-28T21:16:43.000Z","source_disclosed_at":"2026-10-28T21:16:42.000Z","is_synthetic_fixture":false,"null_last_reviewed_at":null,"public_change_summary":"Dated EPSS estimate updated; this is not a new disclosure or proof of exploitation.","null_first_observed_at":"2026-10-31T21:56:08.420Z"},{"id":"NVI-00008911","kind":"vulnerability","title":"ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank","revision":1,"unknowns":["Customer inventory, deployment, reachability and exploitation have not been inspected."],"lifecycle":"active","identifiers":["GHSA-6pwm-x34p-fvj2","CVE-2026-115920"],"plain_summary":"GitHub lists this reviewed advisory for onnx. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.","review_status":"source_listed","control_topics":[],"public_actions":[{"summary":"Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.","source_ids":["sr-293c3346858bd6a2c54f7d8a443b6a83"],"action_type":"investigate","limitations":"Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system.","current_supported_target":null}],"schema_version":"1.1.0","source_metrics":[{"kind":"CVSS","as_of":"2026-10-28T21:16:43.000Z","value":"6.2 medium","meaning":"GitHub source severity; exploitability and local consequence require separate evidence.","source_id":"sr-293c3346858bd6a2c54f7d8a443b6a83"}],"why_it_matters":"Published affected context helps an owner decide what evidence to gather. This report has not examined your application.","related_sources":[{"id":"sr-293c3346858bd6a2c54f7d8a443b6a83","url":"https://github.com/advisories/GHSA-6pwm-x34p-fvj2","basis":"vendor_advisory","notes":"GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.","title":"GHSA-6pwm-x34p-fvj2","publisher":"GitHub Advisory Database","observed_at":"2026-10-31T21:56:08.420Z"}],"affected_context":[{"certainty":"source_listed","source_ids":["sr-293c3346858bd6a2c54f7d8a443b6a83"],"technology":"onnx","known_exclusions":[],"conditions_summary":"## Summary\n\nONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a `kernel_shape` attribute.\n\nThe inference function derived an empty `effective_kernel_shape` vector from the undersized weight tensor, then indexed that vector once for each spatial dimension of the input tensor. A crafted model passed `onnx.checker.check_model()` and caused SIGSEGV when passed to `onnx.shape_inference.infer_shapes()`.\n\n## Technical details\n\nFor an input shape such as `[1, 1, 5]`, shape inference computes one spatial input dimension. A weight shape such as `[1, 3]` has no spatial dimensions, so deriving `kernel_shape` from dimensions 2 onward produces an empty vector. The output loop nevertheless accesses `effective_kernel_shape[0]`.\n\nThe defect affected both the older `convTransposeShapeInference_opset11()` path and the then-current ConvTranspose inference implementation.\n\n## Impact\n\nAn application that performs shape inference on an untrusted model can be terminated, causing denial of service. The observed result was a deterministic process crash. No confidentiality or integrity impact was demonstrate","public_affected_ranges":[">= 1.2.1, <= 1.21.0"]}],"null_published_at":"2026-10-31T22:13:27.122Z","source_updated_at":"2026-10-28T21:16:43.000Z","source_disclosed_at":"2026-10-28T21:16:42.000Z","is_synthetic_fixture":false,"null_last_reviewed_at":null,"public_change_summary":"First indexed by Null; source disclosure date is shown separately.","null_first_observed_at":"2026-10-31T21:56:08.420Z"}],"next_cursor":null,"snapshot_revision":"42594"}