CVE-2026-79785 (GCVE-0-2026-79785)
Vulnerability from cvelistv5 – Published: 2026-08-25 16:03 – Updated: 2026-08-25 19:01
VLAI
EPSS
VEX
Title
X-AnyLabeling before 4.0.0-beta.9 Improper Certificate Validation in Model Downloads
Summary
X-AnyLabeling's model downloader disabled TLS certificate verification. download_with_retry in anylabeling/services/auto_labeling/model.py built a context with ssl._create_unverified_context() and passed it to urllib.request.urlopen, so neither the certificate chain nor the hostname was checked on any model download, and models are fetched over HTTPS from the project's release host. Any party positioned to intercept that connection could therefore answer it with content of their own choosing. The response is written to a .part file and moved into place with os.replace, and the only post-download check, safe_check_model, validates the file's format rather than its provenance: no hash or signature is compared against an expected value. For an ONNX target the substituted file passes onnx.checker.check_model and is then used for inference, so the attacker chooses the model that produces the application's annotations. For a .pth or .pt target, which the shipped SAM2 video, YOLOE, UPN and open_vision configurations use, the check worker calls torch.load without weights_only, so a substituted file is unpickled and executes code of the attacker's choosing on PyTorch releases predating the weights_only default.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-25 19:01 UTC
CWE
- CWE-295 - Improper Certificate Validation
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/CVHub520/X-AnyLabeling | product |
| https://github.com/CVHub520/X-AnyLabeling/commit/… | patch |
| https://github.com/CVHub520/X-AnyLabeling/release… | release-notes |
| https://github.com/CVHub520/X-AnyLabeling/blob/v4… | technical-description |
| https://pypi.org/project/x-anylabeling-cvhub/ | product |
| https://www.vulncheck.com/advisories/x-anylabelin… | third-party-advisory |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| CVHub520 | X-AnyLabeling |
Affected:
0 , < 4.0.0-beta.9
(semver)
|
guessed | |
| CVHub520 | X-AnyLabeling |
Affected:
0 , < 4.0.0b9
(python)
|
guessed |
Date Public
2026-06-16 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.
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.
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