CVE-2026-79785
X-AnyLabeling before 4.0.0-beta.9 Improper Certificate Validation in Model Downloads
Description
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.
INFO
Published Date :
Aug. 25, 2026, 4:17 p.m.
Last Modified :
Aug. 25, 2026, 4:17 p.m.
Remotely Exploit :
Yes !
Source :
[email protected]
Affected Products
The following products are affected by CVE-2026-79785
vulnerability.
Even if cvefeed.io is aware of the exact versions of the
products
that
are
affected, the information is not represented in the table below.
No affected product recoded yet
CVSS Scores
| Score | Version | Severity | Vector | Exploitability Score | Impact Score | Source |
|---|---|---|---|---|---|---|
| CVSS 3.1 | MEDIUM | 83251b91-4cc7-4094-a5c7-464a1b83ea10 | ||||
| CVSS 3.1 | MEDIUM | [email protected] | ||||
| CVSS 4.0 | HIGH | 83251b91-4cc7-4094-a5c7-464a1b83ea10 | ||||
| CVSS 4.0 | HIGH | [email protected] |
Solution
- Enable TLS certificate verification for all HTTPS connections.
- Implement checks for model integrity using hashes or signatures.
- Update PyTorch to versions with secure defaults.
- Validate model format and provenance before use.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-79785.
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-79785 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-79785
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2026-79785 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-79785 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
New CVE Received by [email protected]
Aug. 25, 2026
Action Type Old Value New Value Added Affected [{'repo': 'https://github.com/CVHub520/X-AnyLabeling', 'vendor': 'CVHub520', 'product': 'X-AnyLabeling', 'versions': [{'status': 'affected', 'version': '0', 'lessThan': '4.0.0-beta.9', 'versionType': 'semver'}], 'collectionURL': 'https://github.com/CVHub520/X-AnyLabeling', 'defaultStatus': 'unaffected'}, {'vendor': 'CVHub520', 'product': 'X-AnyLabeling', 'versions': [{'status': 'affected', 'version': '0', 'lessThan': '4.0.0b9', 'versionType': 'python'}], 'packageURL': 'pkg:pypi/x-anylabeling-cvhub', 'defaultStatus': 'unaffected'}] Added Description 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. Added CVSS V4.0 AV:N/AC:H/AT:P/PR:N/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X Added CVSS V3.1 AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:N Added CWE CWE-295 Added Reference https://github.com/CVHub520/X-AnyLabeling Added Reference https://github.com/CVHub520/X-AnyLabeling/blob/v4.0.0-beta.8/anylabeling/services/auto_labeling/model.py Added Reference https://github.com/CVHub520/X-AnyLabeling/commit/52f7c30333f3f99711f09334d740212aa30b9958 Added Reference https://github.com/CVHub520/X-AnyLabeling/releases/tag/v4.0.0-beta.9 Added Reference https://pypi.org/project/x-anylabeling-cvhub/ Added Reference https://www.vulncheck.com/advisories/x-anylabeling-before-4.0.0-beta.9-improper-certificate-validation-in-model-downloads