GSD-2022-41887
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-41887",
"id": "GSD-2022-41887"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"aliases": [
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],
"details": "TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.",
"id": "GSD-2022-41887",
"modified": "2023-12-13T01:19:32.548084Z",
"schema_version": "1.4.0"
}
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"STATE": "PUBLIC",
"TITLE": "Overflow in `tf.keras.losses.poisson` in Tensorflow"
},
"affects": {
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"value": "TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 4.8,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H",
"version": "3.1"
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"value": "CWE-131: Incorrect Calculation of Buffer Size"
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"source": {
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"discovery": "UNKNOWN"
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"affected_range": "\u003c2.9.3||\u003e=2.10.0,\u003c2.10.1",
"affected_versions": "All versions before 2.9.3, all versions starting from 2.10.0 before 2.10.1",
"cwe_ids": [
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"date": "2022-11-21",
"description": "TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.",
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"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2022-11-21",
"solution": "Upgrade to versions 2.9.3, 2.10.1 or above.",
"title": "Incorrect Calculation of Buffer Size",
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"uuid": "313b09eb-77cf-4933-b8d8-d0d1ebb57c16"
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"date": "2022-11-21",
"description": "TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.",
"fixed_versions": [
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"identifier": "CVE-2022-41887",
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"not_impacted": "All versions starting from 2.9.3 before 2.10.0, all versions starting from 2.10.1",
"package_slug": "pypi/tensorflow-gpu",
"pubdate": "2022-11-21",
"solution": "Upgrade to versions 2.9.3, 2.10.1 or above.",
"title": "Incorrect Calculation of Buffer Size",
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"package_slug": "pypi/tensorflow",
"pubdate": "2022-11-18",
"solution": "Upgrade to versions 2.9.3, 2.10.1 or above.",
"title": "Incorrect Calculation of Buffer Size",
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"impact": {
"baseMetricV3": {
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"attackVector": "NETWORK",
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"baseScore": 7.5,
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"exploitabilityScore": 3.9,
"impactScore": 3.6
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},
"lastModifiedDate": "2022-11-22T21:55Z",
"publishedDate": "2022-11-18T22:15Z"
}
}
}
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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