vulnerability / NVI-00009187 / Revision 1
Hydra legacy target blacklist permits unsafe PyYAML loading
GitHub lists this reviewed advisory for hydra-core, hydra-core. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.
This report has not inspected your inventory, configuration, deployed build, reachability or evidence. A catalog listing and a severity score do not establish exposure or compromise.
01 / Meaning
Why it matters
Published affected context helps an owner decide what evidence to gather. This report has not examined your application.
7.8 high
GitHub source severity; exploitability and local consequence require separate evidence.
As of · GitHub Advisory Database
02 / Applicability
The affected context
Affected versions, configuration prerequisites and known exclusions are public. Confirm each one against the deployed service.
hydra-core
source listed>= 1.3.6, < 1.3.8
## Summary Hydra's legacy target blacklist omits PyYAML's unsafe loading surfaces. If an application passes attacker-controlled targets and arguments to instantiate(), a target such as yaml.unsafe_load can execute Python code through a config-controlled YAML stream containing Python object-construction tags. ## Impact and prerequisites The attacker must control the relevant target and arguments that the application instantiates. The configured callable executes with the application's process privileges. This is a gap in the best-effort legacy blacklist. It is not a bypass of Hydra 1.4's execution whitelist supplied by trusted Python code. One realistic exposure is a model downloaded from a third-party repository, such as Hugging Face, that bundles attacker-controlled configuration. When a victim loads the model using application code that passes this configuration to legacy Hydra instantiation without effective target restrictions, the model's metadata can execute code in the victim process. This model-loading attack pattern is documented in the original Hydra advisory GHSA-2cp2-2r3c-7p7r and the associated Unit 42 research. It does not imply that all Hugging Face models or cur
hydra-core
source listed>= 1.4.0.dev9, < 1.4.0rc3
## Summary Hydra's legacy target blacklist omits PyYAML's unsafe loading surfaces. If an application passes attacker-controlled targets and arguments to instantiate(), a target such as yaml.unsafe_load can execute Python code through a config-controlled YAML stream containing Python object-construction tags. ## Impact and prerequisites The attacker must control the relevant target and arguments that the application instantiates. The configured callable executes with the application's process privileges. This is a gap in the best-effort legacy blacklist. It is not a bypass of Hydra 1.4's execution whitelist supplied by trusted Python code. One realistic exposure is a model downloaded from a third-party repository, such as Hugging Face, that bundles attacker-controlled configuration. When a victim loads the model using application code that passes this configuration to legacy Hydra instantiation without effective target restrictions, the model's metadata can execute code in the victim process. This model-loading attack pattern is documented in the original Hydra advisory GHSA-2cp2-2r3c-7p7r and the associated Unit 42 research. It does not imply that all Hugging Face models or cur
03 / Response
What to do next
- investigate
Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.
Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system. GitHub Advisory Database
A fixed release is not deployment evidence.
Check current vendor support and compatibility. After making a change, verify the deployed build and relevant runtime conditions. Keep the evidence, owner and time. An updated lockfile alone does not prove what is running.
04 / Limits
What this report cannot establish
- Customer inventory, deployment, reachability and exploitation have not been inspected.
05 / Provenance
Sources you can inspect
Each assertion retains its source. Mirrored records may repeat one underlying publisher; additional links are not independent corroboration.
- GHSA-wf6j-7hvr-gr93 ↗
GitHub Advisory Database · vendor advisory
Observed . GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.
Public revision history
Only published revisions appear here. Routine fetches do not create a material-change event.
- Revision 1 ·
First indexed by Null; source disclosure date is shown separately.