8.2
HIGH CVSS 4.0
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 !
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
The Common Vulnerability Scoring System is a standardized framework for assessing the severity of vulnerabilities in software and systems. We collect and displays CVSS scores from various sources for each CVE.
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 verification for model downloads and validate model integrity using hashes or signatures.
  • 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.
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
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days. Following chart shows the EPSS score history of the vulnerability.