CVE-2026-33481 (GCVE-0-2026-33481)
Vulnerability from cvelistv5 – Published: 2026-03-26 17:10 – Updated: 2026-03-27 14:42
VLAI
EPSS
VEX
Title
Syft improper temporary file cleanup
Summary
Syft is a a CLI tool and Go library for generating a Software Bill of Materials (SBOM) from container images and filesystems. Syft versions before v1.42.3 would not properly cleanup temporary storage if the temporary storage was exhausted during a scan. When scanning archives Syft will unpack those archives into temporary storage then inspect the unpacked contents. Under normal operation Syft will remove the temporary data it writes after completing a scan. This vulnerability would affect users of Syft that were scanning content that could cause Syft to fill the temporary storage that would then cause Syft to raise an error and exit. When the error is triggered Syft would exit without properly removing the temporary files in use. In our testing this was most easily reproduced by scanning very large artifacts or highly compressed artifacts such as a zipbomb. Because Syft would not clean up its temporary files, the result would be filling temporary file storage preventing future runs of Syft or other system utilities that rely on temporary storage being available. The patch has been released in v1.42.3. Syft now cleans up temporary files when an error condition is encountered. There are no workarounds for this vulnerability in Syft. Users that find their temporary storage depleted can manually remove the temporary files.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-27 14:41 UTC
CWE
- CWE-460 - Improper Cleanup on Thrown Exception
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/anchore/syft/security/advisori… | x_refsource_CONFIRM |
| https://github.com/anchore/stereoscope/pull/537 | x_refsource_MISC |
| https://github.com/anchore/syft/pull/4629 | x_refsource_MISC |
| https://github.com/anchore/syft/pull/4668 | x_refsource_MISC |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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