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    <link>https://vulnerability.circl.lu</link>
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    <lastBuildDate>Tue, 29 Sep 2026 15:30:14 +0000</lastBuildDate>
    <item>
      <title>BREW-dvc-CVE-2026-68508 — Hydra: hydra.utils.instantiate with untrusted config can lead to code execution</title>
      <link>https://vulnerability.circl.lu/vuln/brew-dvc-cve-2026-68508</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; Homebrew: dvc&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;`hydra.utils.instantiate()` resolves and calls Python objects from config. If an
application passes untrusted config to `instantiate()`, an attacker who controls
`_target_` and its arguments can cause arbitrary code execution in the consuming
process.&lt;/p&gt;
&lt;p&gt;Hydra is not a network service. Exploitation requires a consuming application,
library, or user workflow to load attacker-controlled config, CLI overrides, or
model metadata and pass it to `hydra.utils.instantiate()`.&lt;/p&gt;
&lt;p&gt;## Details&lt;/p&gt;
&lt;p&gt;Hydra&amp;#39;s instantiate API is designed to construct objects and call functions from
configuration. For example:&lt;/p&gt;
&lt;p&gt;```yaml
component:
  _target_: package.module.Class
  arg: value
```&lt;/p&gt;
&lt;p&gt;When this config is passed to `hydra.utils.instantiate()`, Hydra resolves
`_target_` and calls it with the provided arguments.&lt;/p&gt;
&lt;p&gt;This is intended for trusted application configuration. However, if untrusted
input controls `_target_`, the config becomes a callable-selection mechanism. A
malicious config can select a callable capable of executing code or commands and
provide attacker-controlled arguments.&lt;/p&gt;
&lt;p&gt;This issue is the same general class of problem discussed by Unit 42 for
downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata
was passed into Hydra instantiate:&lt;/p&gt;
&lt;p&gt;https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/&lt;/p&gt;
&lt;p&gt;Hydra 1.3.4 includes a blacklist for some dangerous `_target_` values. That
blacklist is defense-in-depth and is not a complete security boundary.…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; Homebrew: dvc&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;`hydra.utils.instantiate()` resolves and calls Python objects from config. If an
application passes untrusted config to `instantiate()`, an attacker who controls
`_target_` and its arguments can cause arbitrary code execution in the consuming
process.&lt;/p&gt;
&lt;p&gt;Hydra is not a network service. Exploitation requires a consuming application,
library, or user workflow to load attacker-controlled config, CLI overrides, or
model metadata and pass it to `hydra.utils.instantiate()`.&lt;/p&gt;
&lt;p&gt;## Details&lt;/p&gt;
&lt;p&gt;Hydra&amp;#39;s instantiate API is designed to construct objects and call functions from
configuration. For example:&lt;/p&gt;
&lt;p&gt;```yaml
component:
  _target_: package.module.Class
  arg: value
```&lt;/p&gt;
&lt;p&gt;When this config is passed to `hydra.utils.instantiate()`, Hydra resolves
`_target_` and calls it with the provided arguments.&lt;/p&gt;
&lt;p&gt;This is intended for trusted application configuration. However, if untrusted
input controls `_target_`, the config becomes a callable-selection mechanism. A
malicious config can select a callable capable of executing code or commands and
provide attacker-controlled arguments.&lt;/p&gt;
&lt;p&gt;This issue is the same general class of problem discussed by Unit 42 for
downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata
was passed into Hydra instantiate:&lt;/p&gt;
&lt;p&gt;https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/&lt;/p&gt;
&lt;p&gt;Hydra 1.3.4 includes a blacklist for some dangerous `_target_` values. That
blacklist is defense-in-depth and is not a complete security boundary.…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://vulnerability.circl.lu/vuln/brew-dvc-cve-2026-68508</guid>
    </item>
    <item>
      <title>CVE-2026-68508 — Hydra: hydra.utils.instantiate with untrusted config can lead to code execution</title>
      <link>https://vulnerability.circl.lu/vuln/cve-2026-68508</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; facebookresearch hydra&lt;/p&gt;
&lt;p&gt;Hydra is a framework for elegantly configuring complex applications. Prior to 1.3.4, hydra.utils.instantiate() resolves and calls Python objects selected by configuration through _resolve_target() in hydra/_internal/instantiate/_instantiate2.py, allowing attacker-controlled target values and arguments to choose dangerous callables. A consuming application, library, CLI workflow, or model loader that passes untrusted configuration, CLI overrides, or model metadata into hydra.utils.instantiate() can therefore execute arbitrary code in its own process, including reading or modifying files and credentials or terminating the process. Version 1.3.4 adds target blocking with an explicit HYDRA_INSTANTIATE_ALLOWLIST_OVERRIDE escape hatch. This issue is fixed in version 1.3.4.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; facebookresearch hydra&lt;/p&gt;
&lt;p&gt;Hydra is a framework for elegantly configuring complex applications. Prior to 1.3.4, hydra.utils.instantiate() resolves and calls Python objects selected by configuration through _resolve_target() in hydra/_internal/instantiate/_instantiate2.py, allowing attacker-controlled target values and arguments to choose dangerous callables. A consuming application, library, CLI workflow, or model loader that passes untrusted configuration, CLI overrides, or model metadata into hydra.utils.instantiate() can therefore execute arbitrary code in its own process, including reading or modifying files and credentials or terminating the process. Version 1.3.4 adds target blocking with an explicit HYDRA_INSTANTIATE_ALLOWLIST_OVERRIDE escape hatch. This issue is fixed in version 1.3.4.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://vulnerability.circl.lu/vuln/cve-2026-68508</guid>
    </item>
    <item>
      <title>GHSA-2cp2-2r3c-7p7r — Hydra: hydra.utils.instantiate with untrusted config can lead to code execution</title>
      <link>https://vulnerability.circl.lu/vuln/ghsa-2cp2-2r3c-7p7r</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: hydra-core&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;`hydra.utils.instantiate()` resolves and calls Python objects from config. If an
application passes untrusted config to `instantiate()`, an attacker who controls
`_target_` and its arguments can cause arbitrary code execution in the consuming
process.&lt;/p&gt;
&lt;p&gt;Hydra is not a network service. Exploitation requires a consuming application,
library, or user workflow to load attacker-controlled config, CLI overrides, or
model metadata and pass it to `hydra.utils.instantiate()`.&lt;/p&gt;
&lt;p&gt;## Details&lt;/p&gt;
&lt;p&gt;Hydra&amp;#39;s instantiate API is designed to construct objects and call functions from
configuration. For example:&lt;/p&gt;
&lt;p&gt;```yaml
component:
  _target_: package.module.Class
  arg: value
```&lt;/p&gt;
&lt;p&gt;When this config is passed to `hydra.utils.instantiate()`, Hydra resolves
`_target_` and calls it with the provided arguments.&lt;/p&gt;
&lt;p&gt;This is intended for trusted application configuration. However, if untrusted
input controls `_target_`, the config becomes a callable-selection mechanism. A
malicious config can select a callable capable of executing code or commands and
provide attacker-controlled arguments.&lt;/p&gt;
&lt;p&gt;This issue is the same general class of problem discussed by Unit 42 for
downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata
was passed into Hydra instantiate:&lt;/p&gt;
&lt;p&gt;https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/&lt;/p&gt;
&lt;p&gt;Hydra 1.3.4 includes a blacklist for some dangerous `_target_` values. That
blacklist is defense-in-depth and is not a complete security boundary.…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: hydra-core&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;`hydra.utils.instantiate()` resolves and calls Python objects from config. If an
application passes untrusted config to `instantiate()`, an attacker who controls
`_target_` and its arguments can cause arbitrary code execution in the consuming
process.&lt;/p&gt;
&lt;p&gt;Hydra is not a network service. Exploitation requires a consuming application,
library, or user workflow to load attacker-controlled config, CLI overrides, or
model metadata and pass it to `hydra.utils.instantiate()`.&lt;/p&gt;
&lt;p&gt;## Details&lt;/p&gt;
&lt;p&gt;Hydra&amp;#39;s instantiate API is designed to construct objects and call functions from
configuration. For example:&lt;/p&gt;
&lt;p&gt;```yaml
component:
  _target_: package.module.Class
  arg: value
```&lt;/p&gt;
&lt;p&gt;When this config is passed to `hydra.utils.instantiate()`, Hydra resolves
`_target_` and calls it with the provided arguments.&lt;/p&gt;
&lt;p&gt;This is intended for trusted application configuration. However, if untrusted
input controls `_target_`, the config becomes a callable-selection mechanism. A
malicious config can select a callable capable of executing code or commands and
provide attacker-controlled arguments.&lt;/p&gt;
&lt;p&gt;This issue is the same general class of problem discussed by Unit 42 for
downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata
was passed into Hydra instantiate:&lt;/p&gt;
&lt;p&gt;https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/&lt;/p&gt;
&lt;p&gt;Hydra 1.3.4 includes a blacklist for some dangerous `_target_` values. That
blacklist is defense-in-depth and is not a complete security boundary.…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://vulnerability.circl.lu/vuln/ghsa-2cp2-2r3c-7p7r</guid>
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