Data-science-pipelines-operator: dspo: cryptographically weak secret generation (math/rand) for db and s3 credentials
- Published
- Last modified
- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean? →
- published
- Whether CISA lists it in its Known Exploited Vulnerabilities catalog: proof that attackers are using it.What do these mean? →
- Not listed in CISA KEV
- Whether working attack code is public (Exploit-DB, Metasploit, Nuclei or GitHub). Public code makes attacks easier.What do these mean? →
- None on Exploit-DB, Metasploit or GitHub
- Exploitation: none
- Automatable: yes
- Technical impact: partial
Summary
A flaw was found in the Data Science Pipelines Operator. This vulnerability allows an unauthenticated attacker to derive sensitive credentials, such as MariaDB root/user passwords and MinIO access/secret keys, if they can access the MinIO Route or MariaDB Service. The flaw occurs because the operator uses a cryptographically weak pseudo-random number generator (PRNG) to generate these credentials, making them predictable. Successful exploitation could lead to unauthorized access to all pipeline artifacts and metadata, resulting in significant information disclosure.
Timeline
- CVE published
- CISA SSVC assessment
Exploitation: none · Automatable: yes · Technical impact: partial
- Record last updated
Affected versions
- rhoai/odh-data-science-pipelines-operator-controller-rhel9
Version range not reported
- rhoai/odh-data-science-pipelines-operator-controller-rhel9
Version range not reported
- rhoai/odh-data-science-pipelines-operator-controller-rhel9
Version range not reported
- rhoai/odh-data-science-pipelines-operator-controller-rhel9
Version range not reported
- rhoai/odh-data-science-pipelines-operator-controller-rhel9
Version range not reported
- rhoai/odh-latency-predictor-prediction-rhel9
Version range not reported
- rhoai/odh-latency-predictor-training-rhel9
Version range not reported