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CVE-2024-34997VulnerabilityPublished Updated

CVE-2024-34997: joblib · n/a

Affects joblibSource CVE Database V5

0

Not known to be exploited · CVSS 7.5 High.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score
7.5High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.6% chance of exploitation in 30 days – higher than 51% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 17 May 2024

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.

What to do

Affected software

ProductAffectedFixed in
PyPIjoblibpkg:pypi/joblibOther threatsAffected
  • =1.4.2
Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

1. Restrict joblib cache usage strictly to trusted data sources; avoid caching or loading data from untrusted or unauthenticated inputs. 2. Implement strict access controls on cache directories to prevent unauthorized modification or injection of malicious cache files. 3. Monitor and audit cache file integrity regularly to detect tampering. 4. Consider disabling caching features in joblib where feasible, especially in environments exposed to untrusted users or data. 5. Use containerization or sandboxing to isolate processes using joblib to limit potential impact of exploitation. 6. Stay alert for official patches or updates from the joblib maintainers and apply them promptly once available. 7. Educate developers and data scientists about the risks of deserialization vulnerabilities and safe handling of cached data. 8. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to detect anomalous behavior indicative of exploitation attempts.

Description

joblib v1.4.2 was discovered to contain a deserialization vulnerability via the component joblib.numpy_pickle::NumpyArrayWrapper().read_array(). NOTE: this is disputed by the supplier because NumpyArrayWrapper is only used during caching of trusted content.

References

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