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tensorflow-gpu

Open actionable advisories and recorded vendor fixes. This public package view has no inventory attached and cannot establish whether your deployment is affected. A vendor fix never resolves your finding; verify your installed version and the source guidance.

Open advisories

427 actionable advisories, newest first. Withdrawn advisories and news are excluded. Coverage is partial.

  1. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in MaxPool

    CVE-2022-35989 · GHSA-j43h-pgmg-5hjq

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact When MaxPool receives a window size input array ksize with dimensions greater than its input tensor input, the GPU kernel gives a CHECK fail that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 32d7bd3defd134f21a4e344c8dfd40099aaf6b18. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Jingyi Shi.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  2. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in SparseBincount

    CVE-2022-35982 · GHSA-397c-5g2j-qxpv

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If SparseBincount is given inputs for indices, values, and denseshape that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 40adbe4dd15b582b0210dfbf40c243a62f5119fa. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  3. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in FractionalMaxPoolGrad

    CVE-2022-35981 · GHSA-vxv8-r8q2-63xw

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack: Patches We have patched the issue in GitHub commit 8741e57d163a079db05a7107a7609af70931def4. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  4. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizedRelu and QuantizedRelu6

    CVE-2022-35979 · GHSA-v7vw-577f-vp8x

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for minfeatures or maxfeatures, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  5. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizeDownAndShrinkRange

    CVE-2022-35974 · GHSA-vgvh-2pf4-jr2x

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizeDownAndShrinkRange is given nonscalar inputs for inputmin or inputmax, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  6. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizedMatMul

    CVE-2022-35973 · GHSA-689c-r7h2-fv9v

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxb It gives a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  7. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizedBiasAdd

    CVE-2022-35972 · GHSA-4pc4-m9mj-v2r9

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizedBiasAdd is given mininput, maxinput, minbias, maxbias tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  8. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in FakeQuantWithMinMaxVars

    CVE-2022-35971 · GHSA-9fpg-838v-wpv7

    Affected
    • PyPI tensorflow-gpu >= 2.9.0, < 2.9.1; < 2.7.2; >= 2.8.0, < 2.8.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If FakeQuantWithMinMaxVars is given min or max tensors of a nonzero rank, it results in a CHECK fail that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by: - Neophytos Christou, Secure Systems Labs, Brown University. - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  9. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizedInstanceNorm

    CVE-2022-35970 · GHSA-g35r-369w-3fqp

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizedInstanceNorm is given xmin or xmax tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  10. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in Conv2DBackpropInput

    CVE-2022-35969 · GHSA-q2c3-jpmc-gfjx

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact The implementation of Conv2DBackpropInput requires inputsizes to be 4-dimensional. Otherwise, it gives a CHECK failure which can be used to trigger a denial of service attack: Patches We have patched the issue in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  11. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in AvgPoolGrad

    CVE-2022-35968 · GHSA-2475-53vw-vp25

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact The implementation of AvgPoolGrad does not fully validate the input originputshape. This results in a CHECK failure which can be used to trigger a denial of service attack: Patches We have patched the issue in GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  12. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizedAdd

    CVE-2022-35967 · GHSA-v6h3-348g-6h5x

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizedAdd is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  13. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in QuantizedAvgPool

    CVE-2022-35966 · GHSA-4w68-4x85-mjj9

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If QuantizedAvgPool is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  14. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in FakeQuantWithMinMaxVarsPerChannelGradient

    CVE-2022-35990 · GHSA-h7ff-cfc9-wmmh

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact When tf.quantization.fakequantwithminmaxvarsperchannelgradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack. Patches We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology - Neophytos Christou, Secure Systems Labs, Brown University

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  15. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in TensorListScatter and TensorListScatterV2

    CVE-2022-35991 · GHSA-vm7x-4qhj-rrcq

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact When TensorListScatter and TensorListScatterV2 receive an elementshape of a rank greater than one, they give a CHECK fail that can trigger a denial of service attack. Patches We have patched the issue in GitHub commit bb03fdf4aae944ab2e4b35c7daa051068a8b7f61. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  16. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in LowerBound and UpperBound

    CVE-2022-35965 · GHSA-qxpx-j395-pw36

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If LowerBound or UpperBound is given an emptysortedinputs input, it results in a nullptr dereference, leading to a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit bce3717eaef4f769019fd18e990464ca4a2efeea. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  17. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in TensorListFromTensor

    CVE-2022-35992 · GHSA-9v8w-xmr4-wgxp

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact When TensorListFromTensor receives an elementshape of a rank greater than one, it gives a CHECK fail that can trigger a denial of service attack. Patches We have patched the issue in GitHub commit 3db59a042a38f4338aa207922fa2f476e000a6ee. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  18. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in SetSize

    CVE-2022-35993 · GHSA-wq6q-6m32-9rv9

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact When SetSize receives an input setshape that is not a 1D tensor, it gives a CHECK fails that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit cf70b79d2662c0d3c6af74583641e345fc939467. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  19. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to segfault in BlockLSTMGradV2

    CVE-2022-35964 · GHSA-f7r5-q7cx-h668

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact The implementation of BlockLSTMGradV2 does not fully validate its inputs. - wci, wcf, wco, b must be rank 1 - w, csprev, hprev must be rank 2 - x must be rank 3 This results in a a segfault that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 2a458fc4866505be27c62f81474ecb2b870498fa. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  20. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK failures in FractionalAvgPoolGrad

    CVE-2022-35963 · GHSA-84jm-4cf3-9jfm

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact The implementation of FractionalAvgPoolGrad does not fully validate the input originputtensorshape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  21. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK failures in UnbatchGradOp

    CVE-2022-35952 · GHSA-h5vq-gw2c-pq47

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact The UnbatchGradOp function takes an argument id that is assumed to be a scalar. A nonscalar id can trigger a CHECK failure and crash the program. It also requires its argument batchindex to contain three times the number of elements as indicated in its batchindex.dimsize(0). An incorrect batchindex can trigger a CHECK failure and crash the program. Patches We have patched the issue in GitHub commit 5f945fc6409a3c1e90d6970c9292f805f6e6ddf2. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Kang Hong Jin from Singapore Management University and 刘力源 from the Information System & Security and Countermeasures Experiments Center,…

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  22. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to Int overflow in RaggedRangeOp

    CVE-2022-35940 · GHSA-x989-q2pq-4q5x

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact The RaggedRangOp function takes an argument limits that is eventually used to construct a TensorShape as an int64. If limits is a very large float, it can overflow when converted to an int64. This triggers an InvalidArgument but also throws an abort signal that crashes the program. Patches We have patched the issue in GitHub commit 37cefa91bee4eace55715eeef43720b958a01192. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Jingyi Shi.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  23. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK fail in CollectiveGather

    CVE-2022-35994 · GHSA-fhfc-2q7x-929f

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact When CollectiveGather receives an scalar input input, it gives a CHECK fails that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit c1f491817dec39a26be3c574e86a88c30f3c4770. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  24. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to CHECK failure in TensorListReserve via missing validation

    CVE-2022-35960 · GHSA-v5xg-3q2c-c2r4

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact In core/kernels/listkernels.cc's TensorListReserve, numelements is assumed to be a tensor of size 1. When a numelements of more than 1 element is provided, then tf.rawops.TensorListReserve fails the CHECKEQ in CheckIsAlignedAndSingleElement. Patches We have patched the issue in GitHub commit b5f6fbfba76576202b72119897561e3bd4f179c7. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Kang Hong Jin from Singapore Management University.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →
  25. Medium, CVSS 5.9PyPIAmendedPublished

    TensorFlow vulnerable to floating point exception in Conv2D

    CVE-2022-35996 · GHSA-q5jv-m6qw-5g37

    Affected
    • PyPI tensorflow-gpu < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1
    Fixed in
    2.8.12.7.22.9.1· Vendor fix available
    Issue
    Impact If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. Patches We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Jingyi Shi.

    Recommended action: Upgrade to 2.9.1, 2.8.1 or 2.7.2 by release line, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.

    Sources:osv.dev (1)github.com (2)Source checked Evidence →