{"items":[{"id":"NVI-00009114","kind":"vulnerability","title":"ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs","revision":4,"unknowns":["Customer inventory, deployment, reachability and exploitation have not been inspected.","NVD lastModified does not establish freshness of every associated CPE record."],"lifecycle":"active","identifiers":["GHSA-q69g-f4f5-v8m7","CVE-2026-115922"],"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-17ed5446c6fe2b25797cddc51bdf4e61"],"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":"2026-10-28T21:16:39.000Z","value":"6.2 medium","meaning":"GitHub source severity; exploitability and local consequence require separate evidence.","source_id":"sr-b59c8e752fdc187a2c78dbd88ff787c8"},{"kind":"CVSS","as_of":"2026-10-30T18:17:23.540Z","value":"6.2 MEDIUM (3.1)","meaning":"security-advisories@github.com severity; no combined Null risk score.","source_id":"sr-719c2693f32fa199d2a9f9abbef50888"},{"kind":"EPSS","as_of":"2026-11-04T12:03:12.000Z","value":"0.131%","meaning":"FIRST EPSS model v2026.06.15; exploitation probability over 30 days, percentile 0.02715. Missing scores are unknown.","source_id":"sr-17ed5446c6fe2b25797cddc51bdf4e61"}],"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-b59c8e752fdc187a2c78dbd88ff787c8","url":"https://github.com/advisories/GHSA-q69g-f4f5-v8m7","basis":"vendor_advisory","notes":"GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.","title":"GHSA-q69g-f4f5-v8m7","publisher":"GitHub Advisory Database","observed_at":"2026-10-31T21:56:08.420Z"},{"id":"sr-719c2693f32fa199d2a9f9abbef50888","url":"https://nvd.nist.gov/vuln/detail/CVE-2026-115922","basis":"official_catalog","notes":"This product uses data from the NVD API but is not endorsed or certified by the NVD.","title":"CVE-2026-115922","publisher":"NIST National Vulnerability Database","observed_at":"2026-11-03T09:59:00.529Z"},{"id":"sr-17ed5446c6fe2b25797cddc51bdf4e61","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-2026-115922","publisher":"FIRST EPSS","observed_at":"2026-11-04T17:58:50.313Z"}],"affected_context":[{"certainty":"source_listed","source_ids":["sr-b59c8e752fdc187a2c78dbd88ff787c8"],"technology":"onnx","known_exclusions":[],"conditions_summary":"## Summary\n\nONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.\n\nA very small model could therefore cause a disproportionately large allocation when an application called `onnx.numpy_helper.to_array()` or constructed `onnx.reference.ReferenceEvaluator` with the model. The issue affected `UINT2`, `INT2`, `UINT4`, `INT4`, and `FLOAT4E2M1` tensors stored in either `raw_data` or packed `int32_data`.\n\n## Technical details\n\nThe vulnerable `_unpack_2bit()` and `_unpack_4bit()` paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in `TensorProto.dims`. They did not first confirm that the payload encoded enough logical elements for that shape.\n\nFor example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed `onnx.checker.check_model()` and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.\n\nLoading, checking, or","public_affected_ranges":["<= 1.21.0"]}],"null_published_at":"2026-11-08T19:53:38.941Z","source_updated_at":"2026-10-28T21:16:39.000Z","source_disclosed_at":"2026-10-28T21:16:37.000Z","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-00009114","kind":"vulnerability","title":"ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs","revision":3,"unknowns":["Customer inventory, deployment, reachability and exploitation have not been inspected.","NVD lastModified does not establish freshness of every associated CPE record."],"lifecycle":"active","identifiers":["GHSA-q69g-f4f5-v8m7","CVE-2026-115922"],"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-da0641001511eb03b9f021e2be260748"],"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":"2026-10-28T21:16:39.000Z","value":"6.2 medium","meaning":"GitHub source severity; exploitability and local consequence require separate evidence.","source_id":"sr-b59c8e752fdc187a2c78dbd88ff787c8"},{"kind":"CVSS","as_of":"2026-10-30T18:17:23.540Z","value":"6.2 MEDIUM (3.1)","meaning":"security-advisories@github.com severity; no combined Null risk score.","source_id":"sr-719c2693f32fa199d2a9f9abbef50888"},{"kind":"EPSS","as_of":"2026-10-31T12:00:27.000Z","value":"0.131%","meaning":"FIRST EPSS model v2026.06.15; exploitation probability over 30 days, percentile 0.02675. Missing scores are unknown.","source_id":"sr-da0641001511eb03b9f021e2be260748"}],"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-b59c8e752fdc187a2c78dbd88ff787c8","url":"https://github.com/advisories/GHSA-q69g-f4f5-v8m7","basis":"vendor_advisory","notes":"GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.","title":"GHSA-q69g-f4f5-v8m7","publisher":"GitHub Advisory Database","observed_at":"2026-10-31T21:56:08.420Z"},{"id":"sr-719c2693f32fa199d2a9f9abbef50888","url":"https://nvd.nist.gov/vuln/detail/CVE-2026-115922","basis":"official_catalog","notes":"This product uses data from the NVD API but is not endorsed or certified by the NVD.","title":"CVE-2026-115922","publisher":"NIST National Vulnerability Database","observed_at":"2026-11-03T09:59:00.529Z"},{"id":"sr-da0641001511eb03b9f021e2be260748","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-2026-115922","publisher":"FIRST EPSS","observed_at":"2026-10-31T22:06:15.374Z"}],"affected_context":[{"certainty":"source_listed","source_ids":["sr-b59c8e752fdc187a2c78dbd88ff787c8"],"technology":"onnx","known_exclusions":[],"conditions_summary":"## Summary\n\nONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.\n\nA very small model could therefore cause a disproportionately large allocation when an application called `onnx.numpy_helper.to_array()` or constructed `onnx.reference.ReferenceEvaluator` with the model. The issue affected `UINT2`, `INT2`, `UINT4`, `INT4`, and `FLOAT4E2M1` tensors stored in either `raw_data` or packed `int32_data`.\n\n## Technical details\n\nThe vulnerable `_unpack_2bit()` and `_unpack_4bit()` paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in `TensorProto.dims`. They did not first confirm that the payload encoded enough logical elements for that shape.\n\nFor example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed `onnx.checker.check_model()` and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.\n\nLoading, checking, or","public_affected_ranges":["<= 1.21.0"]}],"null_published_at":"2026-11-08T08:10:32.021Z","source_updated_at":"2026-10-28T21:16:39.000Z","source_disclosed_at":"2026-10-28T21:16:37.000Z","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-00009114","kind":"vulnerability","title":"ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs","revision":2,"unknowns":["Customer inventory, deployment, reachability and exploitation have not been inspected.","NVD lastModified does not establish freshness of every associated CPE record."],"lifecycle":"active","identifiers":["GHSA-q69g-f4f5-v8m7","CVE-2026-115922"],"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-719c2693f32fa199d2a9f9abbef50888"],"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":"2026-10-28T21:16:39.000Z","value":"6.2 medium","meaning":"GitHub source severity; exploitability and local consequence require separate evidence.","source_id":"sr-b59c8e752fdc187a2c78dbd88ff787c8"},{"kind":"CVSS","as_of":"2026-10-30T18:17:23.540Z","value":"6.2 MEDIUM (3.1)","meaning":"security-advisories@github.com severity; no combined Null risk score.","source_id":"sr-719c2693f32fa199d2a9f9abbef50888"}],"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-b59c8e752fdc187a2c78dbd88ff787c8","url":"https://github.com/advisories/GHSA-q69g-f4f5-v8m7","basis":"vendor_advisory","notes":"GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.","title":"GHSA-q69g-f4f5-v8m7","publisher":"GitHub Advisory Database","observed_at":"2026-10-31T21:56:08.420Z"},{"id":"sr-719c2693f32fa199d2a9f9abbef50888","url":"https://nvd.nist.gov/vuln/detail/CVE-2026-115922","basis":"official_catalog","notes":"This product uses data from the NVD API but is not endorsed or certified by the NVD.","title":"CVE-2026-115922","publisher":"NIST National Vulnerability Database","observed_at":"2026-11-03T09:59:00.529Z"}],"affected_context":[{"certainty":"source_listed","source_ids":["sr-b59c8e752fdc187a2c78dbd88ff787c8"],"technology":"onnx","known_exclusions":[],"conditions_summary":"## Summary\n\nONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.\n\nA very small model could therefore cause a disproportionately large allocation when an application called `onnx.numpy_helper.to_array()` or constructed `onnx.reference.ReferenceEvaluator` with the model. The issue affected `UINT2`, `INT2`, `UINT4`, `INT4`, and `FLOAT4E2M1` tensors stored in either `raw_data` or packed `int32_data`.\n\n## Technical details\n\nThe vulnerable `_unpack_2bit()` and `_unpack_4bit()` paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in `TensorProto.dims`. They did not first confirm that the payload encoded enough logical elements for that shape.\n\nFor example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed `onnx.checker.check_model()` and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.\n\nLoading, checking, or","public_affected_ranges":["<= 1.21.0"]}],"null_published_at":"2026-11-07T07:07:42.505Z","source_updated_at":"2026-10-28T21:16:39.000Z","source_disclosed_at":"2026-10-28T21:16:37.000Z","is_synthetic_fixture":false,"null_last_reviewed_at":null,"public_change_summary":"Source facts changed; compare the preserved prior revision.","null_first_observed_at":"2026-10-31T21:56:08.420Z"},{"id":"NVI-00009114","kind":"vulnerability","title":"ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs","revision":1,"unknowns":["Customer inventory, deployment, reachability and exploitation have not been inspected."],"lifecycle":"active","identifiers":["GHSA-q69g-f4f5-v8m7","CVE-2026-115922"],"plain_summary":"GitHub lists this reviewed advisory for onnx. 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This report has not examined your application.","related_sources":[{"id":"sr-b59c8e752fdc187a2c78dbd88ff787c8","url":"https://github.com/advisories/GHSA-q69g-f4f5-v8m7","basis":"vendor_advisory","notes":"GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.","title":"GHSA-q69g-f4f5-v8m7","publisher":"GitHub Advisory Database","observed_at":"2026-10-31T21:56:08.420Z"}],"affected_context":[{"certainty":"source_listed","source_ids":["sr-b59c8e752fdc187a2c78dbd88ff787c8"],"technology":"onnx","known_exclusions":[],"conditions_summary":"## Summary\n\nONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.\n\nA very small model could therefore cause a disproportionately large allocation when an application called `onnx.numpy_helper.to_array()` or constructed `onnx.reference.ReferenceEvaluator` with the model. The issue affected `UINT2`, `INT2`, `UINT4`, `INT4`, and `FLOAT4E2M1` tensors stored in either `raw_data` or packed `int32_data`.\n\n## Technical details\n\nThe vulnerable `_unpack_2bit()` and `_unpack_4bit()` paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in `TensorProto.dims`. They did not first confirm that the payload encoded enough logical elements for that shape.\n\nFor example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed `onnx.checker.check_model()` and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.\n\nLoading, checking, or","public_affected_ranges":["<= 1.21.0"]}],"null_published_at":"2026-10-31T22:13:31.284Z","source_updated_at":"2026-10-28T21:16:39.000Z","source_disclosed_at":"2026-10-28T21:16:37.000Z","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"}