Summary
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.
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().
Details
Hydra's instantiate API is designed to construct objects and call functions from
configuration. For example:
component:
_target_: package.module.Class
arg: value
When this config is passed to hydra.utils.instantiate(), Hydra resolves
_target_ and calls it with the provided arguments.
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.
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:
https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/
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. The
blacklist is not present in the released hydra-core 1.3.3 package, so this
issue should not be described as a bypass of a released 1.3.3 blacklist.
Impact
A successful attack can execute code in the process that calls
hydra.utils.instantiate(). The impact is limited to the privileges and
environment of that process.
Potential impact includes:
- Reading files, credentials, environment variables, or data accessible to the
process
- Modifying files, outputs, checkpoints, or application state writable by the
process
- Terminating or disrupting the process
Affected Usage
Applications and libraries are affected when they pass untrusted or semi-trusted
config, model metadata, CLI overrides, or other externally controlled data to
hydra.utils.instantiate() without constraining which targets may be
instantiated.
Trusted application-owned configuration is not affected in the same way.
Remediation
Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious
dangerous targets. It is a substantial security improvement, and users remaining
on the 1.3 release line should upgrade to 1.3.4 or a newer version.
The unreleased Hydra 1.4 development line uses an allowlist-based instantiation
model that fully addresses this vulnerability class. The allowlist must come
from trusted application code or another trusted channel, not from the untrusted
config being instantiated.
Applications that consume untrusted or semi-trusted config should not pass it
directly to hydra.utils.instantiate(). They should validate _target_ values
against a trusted allowlist before instantiation.
References
guwu1017 Reporter
Summary
hydra.utils.instantiate()resolves and calls Python objects from config. If anapplication passes untrusted config to
instantiate(), an attacker who controls_target_and its arguments can cause arbitrary code execution in the consumingprocess.
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().Details
Hydra's instantiate API is designed to construct objects and call functions from
configuration. For example:
When this config is passed to
hydra.utils.instantiate(), Hydra resolves_target_and calls it with the provided arguments.This is intended for trusted application configuration. However, if untrusted
input controls
_target_, the config becomes a callable-selection mechanism. Amalicious config can select a callable capable of executing code or commands and
provide attacker-controlled arguments.
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:
https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/
Hydra 1.3.4 includes a blacklist for some dangerous
_target_values. Thatblacklist is defense-in-depth and is not a complete security boundary. The
blacklist is not present in the released
hydra-core1.3.3 package, so thisissue should not be described as a bypass of a released 1.3.3 blacklist.
Impact
A successful attack can execute code in the process that calls
hydra.utils.instantiate(). The impact is limited to the privileges andenvironment of that process.
Potential impact includes:
process
process
Affected Usage
Applications and libraries are affected when they pass untrusted or semi-trusted
config, model metadata, CLI overrides, or other externally controlled data to
hydra.utils.instantiate()without constraining which targets may beinstantiated.
Trusted application-owned configuration is not affected in the same way.
Remediation
Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious
dangerous targets. It is a substantial security improvement, and users remaining
on the 1.3 release line should upgrade to 1.3.4 or a newer version.
The unreleased Hydra 1.4 development line uses an allowlist-based instantiation
model that fully addresses this vulnerability class. The allowlist must come
from trusted application code or another trusted channel, not from the untrusted
config being instantiated.
Applications that consume untrusted or semi-trusted config should not pass it
directly to
hydra.utils.instantiate(). They should validate_target_valuesagainst a trusted allowlist before instantiation.
References