Vulnerability-Lookup

PYSEC-2026-2240

Vulnerability from pysec - Published: 2026-04-01 18:16 - Updated: 2026-07-13 05:50

Details

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python’s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX model file. It didn’t check if the "keys" in the file were valid. Due to this, an attacker could craft a malicious model that overwrites internal object properties. This issue has been patched in version 1.21.0.

Severity

8.6 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H

Impacted products

Name	purl
onnx	pkg:pypi/onnx

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "ecosystem_specific": {},
      "package": {
        "ecosystem": "PyPI",
        "name": "onnx",
        "purl": "pkg:pypi/onnx"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.21.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.1",
        "0.2",
        "0.2.1",
        "1.0.0",
        "1.0.1",
        "1.1.0",
        "1.1.1",
        "1.1.2",
        "1.10.0",
        "1.10.1",
        "1.10.2",
        "1.11.0",
        "1.12.0",
        "1.13.0",
        "1.13.1",
        "1.14.0",
        "1.14.1",
        "1.15.0",
        "1.16.0",
        "1.16.1",
        "1.16.2",
        "1.17.0",
        "1.18.0",
        "1.19.0",
        "1.19.1",
        "1.19.1rc1",
        "1.2.1",
        "1.2.2",
        "1.2.3",
        "1.20.0",
        "1.20.0rc1",
        "1.20.0rc2",
        "1.20.1",
        "1.20.1rc1",
        "1.21.0rc1",
        "1.21.0rc2",
        "1.21.0rc3",
        "1.21.0rc4",
        "1.3.0",
        "1.4.0",
        "1.4.1",
        "1.5.0",
        "1.6.0",
        "1.7.0",
        "1.8.0",
        "1.8.1",
        "1.9.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-34445",
    "GHSA-538c-55jv-c5g9"
  ],
  "details": "Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python\u2019s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX model file. It didn\u2019t check if the \"keys\" in the file were valid. Due to this, an attacker could craft a malicious model that overwrites internal object properties. This issue has been patched in version 1.21.0.",
  "id": "PYSEC-2026-2240",
  "modified": "2026-07-13T05:50:25.772517Z",
  "published": "2026-04-01T18:16:30.500Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/onnx/onnx/commit/e30c6935d67cc3eca2fa284e37248e7c0036c46b"
    },
    {
      "type": "FIX",
      "url": "https://github.com/onnx/onnx/pull/7751"
    },
    {
      "type": "FIX",
      "url": "https://github.com/onnx/onnx/security/advisories/GHSA-538c-55jv-c5g9"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
      "type": "CVSS_V3"
    }
  ]
}

CVE-2026-34445 (GCVE-0-2026-34445)

Vulnerability from cvelistv5 – Published: 2026-04-01 17:30 – Updated: 2026-04-01 18:00

Title

ONNX: Malicious ONNX models can crash servers by exploiting unprotected object settings.

Summary

Severity

8.6 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H

SSVC

Exploitation: none Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-04-01 17:59 UTC

CWE

CWE-20 - Improper Input Validation
CWE-400 - Uncontrolled Resource Consumption
CWE-915 - Improperly Controlled Modification of Dynamically-Determined Object Attributes

Assigner

GitHub_M mitre root Scorecard

References

3 references

URL	Tags
https://github.com/onnx/onnx/security/advisories/…	x_refsource_CONFIRM
https://github.com/onnx/onnx/pull/7751	x_refsource_MISC
https://github.com/onnx/onnx/commit/e30c6935d67cc…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
onnx	onnx	Affected: < 1.21.0

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2026-34445",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "yes"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-04-01T17:59:29.944812Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-04-01T18:00:14.120Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "onnx",
          "vendor": "onnx",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 1.21.0"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python\u2019s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX model file. It didn\u2019t check if the \"keys\" in the file were valid. Due to this, an attacker could craft a malicious model that overwrites internal object properties. This issue has been patched in version 1.21.0."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 8.6,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "LOW",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-20",
              "description": "CWE-20: Improper Input Validation",
              "lang": "en",
              "type": "CWE"
            }
          ]
        },
        {
          "descriptions": [
            {
              "cweId": "CWE-400",
              "description": "CWE-400: Uncontrolled Resource Consumption",
              "lang": "en",
              "type": "CWE"
            }
          ]
        },
        {
          "descriptions": [
            {
              "cweId": "CWE-915",
              "description": "CWE-915: Improperly Controlled Modification of Dynamically-Determined Object Attributes",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-04-01T17:30:19.994Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "name": "https://github.com/onnx/onnx/security/advisories/GHSA-538c-55jv-c5g9",
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/onnx/onnx/security/advisories/GHSA-538c-55jv-c5g9"
        },
        {
          "name": "https://github.com/onnx/onnx/pull/7751",
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/onnx/onnx/pull/7751"
        },
        {
          "name": "https://github.com/onnx/onnx/commit/e30c6935d67cc3eca2fa284e37248e7c0036c46b",
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/onnx/onnx/commit/e30c6935d67cc3eca2fa284e37248e7c0036c46b"
        }
      ],
      "source": {
        "advisory": "GHSA-538c-55jv-c5g9",
        "discovery": "UNKNOWN"
      },
      "title": "ONNX: Malicious ONNX models can crash servers by exploiting unprotected object settings."
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2026-34445",
    "datePublished": "2026-04-01T17:30:19.994Z",
    "dateReserved": "2026-03-27T18:18:14.894Z",
    "dateUpdated": "2026-04-01T18:00:14.120Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

GHSA-538C-55JV-C5G9

Vulnerability from github – Published: 2026-04-01 21:10 – Updated: 2026-04-01 21:10

Summary

ONNX: Malicious ONNX models can crash servers by exploiting unprotected object settings.

Details

Summary

The ExternalDataInfo class in ONNX was using Python’s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX model file. The problem? It didn’t check if the "keys" in the file were valid. Because it blindly trusted the file, an attacker could craft a malicious model that overwrites internal object properties.

Why its Dangerous

Instant Crash DoS: An attacker can set the length property to a massive number like 9 petabytes. When the system tries to load the model, it attempts to allocate all that RAM at once, causing the server to crash or freeze Out of Memory.

Access Bypass: By setting a negative offset -1, an attacker can trick the system into reading parts of a file it wasn't supposed to touch.

Object Corruption: Attackers can even inject "dunder" attributes like class to change the object's type entirely, which could lead to more complex exploits.

Fixed: https://github.com/onnx/onnx/pull/7751 object state corruption and DoS via ExternalDataInfo attribute injection

Severity

8.6 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 1.20.1"
      },
      "package": {
        "ecosystem": "PyPI",
        "name": "onnx"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.21.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-34445"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-20",
      "CWE-400",
      "CWE-915"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-04-01T21:10:52Z",
    "nvd_published_at": "2026-04-01T18:16:30Z",
    "severity": "HIGH"
  },
  "details": "### Summary\nThe ExternalDataInfo class in ONNX was using Python\u2019s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX model file. The problem? It didn\u2019t check if the \"keys\" in the file were valid. Because it blindly trusted the file, an attacker could craft a malicious model that overwrites internal object properties.\n\n### Why its Dangerous\n**Instant Crash DoS**: An attacker can set the length property to a massive number like 9 petabytes. When the system tries to load the model, it attempts to allocate all that RAM at once, causing the server to crash or freeze Out of Memory.\n\n**Access Bypass**: By setting a negative offset -1, an attacker can trick the system into reading parts of a file it wasn\u0027t supposed to touch.\n\n**Object Corruption**: Attackers can even inject \"dunder\" attributes like __class__ to change the object\u0027s type entirely, which could lead to more complex exploits.\n\n**Fixed**: https://github.com/onnx/onnx/pull/7751 object state corruption and DoS via ExternalDataInfo attribute injection",
  "id": "GHSA-538c-55jv-c5g9",
  "modified": "2026-04-01T21:10:52Z",
  "published": "2026-04-01T21:10:52Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/onnx/onnx/security/advisories/GHSA-538c-55jv-c5g9"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-34445"
    },
    {
      "type": "WEB",
      "url": "https://github.com/onnx/onnx/pull/7751"
    },
    {
      "type": "WEB",
      "url": "https://github.com/onnx/onnx/commit/e30c6935d67cc3eca2fa284e37248e7c0036c46b"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/onnx/onnx"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "ONNX: Malicious ONNX models can crash servers by exploiting unprotected object settings."
}

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.

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.

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.

Action not permitted

PYSEC-2026-2240

CVE-2026-34445 (GCVE-0-2026-34445)

GHSA-538C-55JV-C5G9

Summary

Why its Dangerous

Tags

Sightings

Nomenclature

Related by attack behaviour