CVE-2026-86749 (GCVE-0-2026-86749)
Vulnerability from cvelistv5 – Published: 2026-09-09 13:32 – Updated: 2026-09-19 14:21
VLAI
EPSS
VEX
Title
snipe-it before 8.7.0 Data Loss via Failed Image Write
Summary
Snipe-IT versions <= 8.6.3 (fixed in 8.7.0) do not check the return value of storage write operations in ImageUploadRequest::handleImages(). Because Laravel's default disk mode does not throw on failure, a silently failed Storage::disk('public')->put(...) call still caused the application to delete the previous image via deleteExistingImage() and to reassign and persist the model's image reference to the new filename, destroying the existing image and leaving the database row pointing at a file that was never written. A mirror problem existed in deleteExistingImage(), where a failed Storage::delete() still nulled the model's image field, orphaning the file on disk. The condition is not directly attacker-controlled: it is triggered when any legitimate authenticated user submits an image upload while the storage backend transiently fails (for example an S3 network error, a local filesystem permission problem, or quota exhaustion). The result is unrecoverable loss of the prior image and a durable inconsistency between the database and disk that requires manual reconciliation. All models whose controllers route through ImageUploadRequest::handleImages (assets, asset models, users, companies, manufacturers, locations, categories, suppliers, departments, and other image-carrying models) are affected.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-18 17:43 UTC
CWE
- CWE-252 - Unchecked Return Value
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/grokability/snipe-it/security/… | vendor-advisory |
| https://www.vulncheck.com/advisories/snipe-it-bef… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| grokability | snipe-it |
Affected:
0 , < 8.7.0
(semver)
Unaffected: 8.7.0 (semver) cpe:2.3:a:snipeitapp:snipe-it:*:*:*:*:*:*:*:* |
Date Public
2026-08-24 00:00
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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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