CVE-2026-48089 (GCVE-0-2026-48089)
Vulnerability from cvelistv5 – Published: 2026-06-19 19:38 – Updated: 2026-06-22 17:17
VLAI
EPSS
VEX
Title
DevGuard has improper authorization on public assets
Summary
DevGuard provides vulnerability management for the full software supply chain. Prior to 1.4.2, on a DevGuard API instance with one or more public assets, any authenticated user — including users from a different organization with no membership or role in the affected org/project — can create, update, reapply, and delete VEX rules on those public assets. The same flaw affects the other vulnerability-triage write endpoints exposed under a public asset, including VEX rule create / update / reapply / delete; dependency-vuln event creation (accept / reject / mitigate decisions), batch event creation, vuln sync, and mitigation; license risk creation; external reference writes; and/or artifact creation and license refresh. The attacker needs a valid account on the instance, but no membership in the victim organization, project, or asset is required. Version `v1.4.2`contains a patch. As a workaround, make affected assets non-public. In the asset settings, switch visibility from public to private. This removes the public-read exemption in the access-control middleware and restores correct authorization on all write endpoints for that asset. Downstream consumers that previously relied on the public `vex.json` / `sbom.json` endpoints will need to be granted explicit access or must receive an exported file version until the patched release is deployed.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-22 17:17 UTC
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/l3montree-dev/devguard/securit… | x_refsource_CONFIRM |
| https://github.com/l3montree-dev/devguard/commit/… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| l3montree-dev | devguard |
Affected:
< 1.4.2
|
guessed |
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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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