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    <link>https://cve.radiocsirt.org</link>
    <description>Contains only the most 10 recent entries.</description>
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    <lastBuildDate>Sun, 04 Oct 2026 00:56:41 +0000</lastBuildDate>
    <item>
      <title>bdu:2023-06445</title>
      <link>https://cve.radiocsirt.org/vuln/bdu:2023-06445</link>
      <description>bdu:2023-06445</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/bdu:2023-06445</guid>
    </item>
    <item>
      <title>EUVD-2026-216861</title>
      <link>https://cve.radiocsirt.org/vuln/euvd-2026-216861</link>
      <description>EUVD-2026-216861</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/euvd-2026-216861</guid>
    </item>
    <item>
      <title>fkie_cve-2023-43654</title>
      <link>https://cve.radiocsirt.org/vuln/fkie_cve-2023-43654</link>
      <description>&lt;p&gt;TorchServe is a tool for serving and scaling PyTorch models in production. TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions 0.1.0 to 0.8.1. A user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the allowed_urls and specifying the model URL to be used. A pull request to warn the user when the default value for allowed_urls is used has been merged in PR #2534. TorchServe release 0.8.2 includes this change. Users are advised to upgrade. There are no known workarounds for this issue.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;TorchServe is a tool for serving and scaling PyTorch models in production. TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions 0.1.0 to 0.8.1. A user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the allowed_urls and specifying the model URL to be used. A pull request to warn the user when the default value for allowed_urls is used has been merged in PR #2534. TorchServe release 0.8.2 includes this change. Users are advised to upgrade. There are no known workarounds for this issue.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/fkie_cve-2023-43654</guid>
    </item>
    <item>
      <title>GHSA-8fxr-qfr9-p34w — TorchServe Server-Side Request Forgery vulnerability</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-8fxr-qfr9-p34w</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: torchserve&lt;/p&gt;
&lt;p&gt;## Impact
**Remote Server-Side Request Forgery (SSRF)**
    **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`.
    **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change.&lt;/p&gt;
&lt;p&gt;## Patches&lt;/p&gt;
&lt;p&gt;## TorchServe release 0.8.2 includes fixes to address the previously listed issue:&lt;/p&gt;
&lt;p&gt;https://github.com/pytorch/serve/releases/tag/v0.8.2&lt;/p&gt;
&lt;p&gt;**Tags for upgraded DLC release**
User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2:
x86 GPU&lt;/p&gt;
&lt;p&gt;* v1.9-pt-ec2-2.0.1-inf-gpu-py310
* v1.8-pt-sagemaker-2.0.1-inf-gpu-py310&lt;/p&gt;
&lt;p&gt;x86 CPU&lt;/p&gt;
&lt;p&gt;* v1.8-pt-ec2-2.0.1-inf-cpu-py310
* v1.7-pt-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Graviton&lt;/p&gt;
&lt;p&gt;* v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310
* v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Neuron&lt;/p&gt;
&lt;p&gt;* 1.13.1-…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: torchserve&lt;/p&gt;
&lt;p&gt;## Impact
**Remote Server-Side Request Forgery (SSRF)**
    **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`.
    **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change.&lt;/p&gt;
&lt;p&gt;## Patches&lt;/p&gt;
&lt;p&gt;## TorchServe release 0.8.2 includes fixes to address the previously listed issue:&lt;/p&gt;
&lt;p&gt;https://github.com/pytorch/serve/releases/tag/v0.8.2&lt;/p&gt;
&lt;p&gt;**Tags for upgraded DLC release**
User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2:
x86 GPU&lt;/p&gt;
&lt;p&gt;* v1.9-pt-ec2-2.0.1-inf-gpu-py310
* v1.8-pt-sagemaker-2.0.1-inf-gpu-py310&lt;/p&gt;
&lt;p&gt;x86 CPU&lt;/p&gt;
&lt;p&gt;* v1.8-pt-ec2-2.0.1-inf-cpu-py310
* v1.7-pt-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Graviton&lt;/p&gt;
&lt;p&gt;* v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310
* v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Neuron&lt;/p&gt;
&lt;p&gt;* 1.13.1-…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/ghsa-8fxr-qfr9-p34w</guid>
    </item>
    <item>
      <title>gsd-2023-43654</title>
      <link>https://cve.radiocsirt.org/vuln/gsd-2023-43654</link>
      <description>gsd-2023-43654</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/gsd-2023-43654</guid>
    </item>
    <item>
      <title>PYSEC-2026-553 — TorchServe Server-Side Request Forgery vulnerability</title>
      <link>https://cve.radiocsirt.org/vuln/pysec-2026-553</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: torchserve&lt;/p&gt;
&lt;p&gt;## Impact
**Remote Server-Side Request Forgery (SSRF)**
    **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`.
    **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change.&lt;/p&gt;
&lt;p&gt;## Patches&lt;/p&gt;
&lt;p&gt;## TorchServe release 0.8.2 includes fixes to address the previously listed issue:&lt;/p&gt;
&lt;p&gt;https://github.com/pytorch/serve/releases/tag/v0.8.2
 
**Tags for upgraded DLC release**
User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2:
x86 GPU&lt;/p&gt;
&lt;p&gt;* v1.9-pt-ec2-2.0.1-inf-gpu-py310
 * v1.8-pt-sagemaker-2.0.1-inf-gpu-py310&lt;/p&gt;
&lt;p&gt;x86 CPU&lt;/p&gt;
&lt;p&gt;* v1.8-pt-ec2-2.0.1-inf-cpu-py310
 * v1.7-pt-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Graviton&lt;/p&gt;
&lt;p&gt;* v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310
 * v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Neuron&lt;/p&gt;
&lt;p&gt;* 1.1…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: torchserve&lt;/p&gt;
&lt;p&gt;## Impact
**Remote Server-Side Request Forgery (SSRF)**
    **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`.
    **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change.&lt;/p&gt;
&lt;p&gt;## Patches&lt;/p&gt;
&lt;p&gt;## TorchServe release 0.8.2 includes fixes to address the previously listed issue:&lt;/p&gt;
&lt;p&gt;https://github.com/pytorch/serve/releases/tag/v0.8.2
 
**Tags for upgraded DLC release**
User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2:
x86 GPU&lt;/p&gt;
&lt;p&gt;* v1.9-pt-ec2-2.0.1-inf-gpu-py310
 * v1.8-pt-sagemaker-2.0.1-inf-gpu-py310&lt;/p&gt;
&lt;p&gt;x86 CPU&lt;/p&gt;
&lt;p&gt;* v1.8-pt-ec2-2.0.1-inf-cpu-py310
 * v1.7-pt-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Graviton&lt;/p&gt;
&lt;p&gt;* v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310
 * v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310&lt;/p&gt;
&lt;p&gt;Neuron&lt;/p&gt;
&lt;p&gt;* 1.1…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/pysec-2026-553</guid>
    </item>
    <item>
      <title>WID-SEC-W-2023-2545 — PyTorch: Schwachstelle ermöglicht Manipulation von Dateien</title>
      <link>https://cve.radiocsirt.org/vuln/wid-sec-w-2023-2545</link>
      <description>&lt;p&gt;Ein entfernter, anonymer Angreifer kann eine Schwachstelle in PyTorch ausnutzen, um Dateien zu manipulieren.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;Ein entfernter, anonymer Angreifer kann eine Schwachstelle in PyTorch ausnutzen, um Dateien zu manipulieren.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/wid-sec-w-2023-2545</guid>
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