CVSS 4.0
8.5 highCVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:NCVE-2026-120833
High (8.5). EPSS 0.1% estimated probability of exploitation activity in the next 30 days.
labbench before 0.46.1 contains a deserialization of untrusted data vulnerability in read() and read_relational() that passes any file with a .p or .pickle extension to pandas.read_pickle based on the extension alone. Attackers who supply a crafted data file or relational data archive can execute arbitrary code when a user loads it with these functions.
Severity
CVSS 3.1
7.8 highCVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H- EPSS
- 0.1% estimated probability of exploitation activity in the next 30 days; 4th percentile. Scored 2026-11-04. A forecast for this CVE across the internet, not for your own systems.
- CWE
- CWE-502 Deserialization of Untrusted Data
- OWASP Top 10
- A08:2025 Software or Data Integrity Failures
- Published
- Modified
- Record from
- the NVD, synced
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Explain CVE-2026-120833 for an engineer deciding whether to act. Use https://cve.bz/api/public/cves/CVE-2026-120833?via=ask-ai-copy and https://cve.bz/cve/CVE-2026-120833?via=ask-ai-copy as the source of truth: what is affected and which versions fix it, whether it is in CISA KEV, public exploits, and its EPSS (an internet-wide 30-day estimate of exploitation activity, not a forecast for my systems). If they give no fixed versions, use the CVE record https://cveawg.mitre.org/api/cve/CVE-2026-120833 or the vendor advisory, not third-party mirrors. Tell me how to check whether I am affected and how to verify a fix. Cite the sources and say where they are uncertain.Opens a third-party assistant; its answer is its own, not cve.bz's. The prompt holds only this CVE's id and public links.
Versions
From the CNA's record in the CVE List.
| Product | Affected |
|---|---|
| NIST labbench | before 0.46.1 |
Attack patterns (CAPEC)
- CAPEC-586 Object Injection Likelihood: Medium · Severity: High
The attack patterns MITRE's CAPEC relates to this record's weaknesses, as MITRE names them. Credits.
References
- https://github.com/usnistgov/labbench
- https://github.com/usnistgov/labbench/blob/0.46.0/src/labbench/_data.py#L1664-L1666
- https://github.com/usnistgov/labbench/blob/0.46.1/ChangeLog.md
- https://github.com/usnistgov/labbench/commit/ac9b9b18cba67cb8e85eb563b6bb7dc1092b4c54
- https://github.com/usnistgov/labbench/pull/32
- https://pypi.org/project/labbench/0.46.1/
- https://www.vulncheck.com/advisories/labbench-before-0.46.1-deserialization-of-untrusted-data-via-read-pickle-auto-dispatch