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Out of bounds read in Tensorflow

Type: advisorySources: OSV

Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.

Products
Not specified
Affected versions
Not specified
Fixed versions
Not specified
CVEs
CVE-2022-23594
Confirmed fixed CVEs
Not specified
Updated in this feed
3 Nov 2026, 07:11 UTC · Revision 1

Source evidence

Out of bounds read in Tensorflow

core-osv · Original source

Published 4 Feb 2022, 22:32 UTC

Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.

osv:CVE-2022-23594
Complete source record
{
  "id": "ccc684c70dbb162292641a3b5fb879fb0d0803e83b3a94bd3a64b2ba45516277",
  "url": "https://osv.dev/vulnerability/CVE-2022-23594",
  "method": "structured",
  "payload": {
    "kind": "advisory",
    "title": "Out of bounds read in Tensorflow",
    "summary": "Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.",
    "evidence": "osv:CVE-2022-23594",
    "products": [],
    "severity": "high",
    "structured": {
      "id": "CVE-2022-23594",
      "aliases": [
        "BIT-tensorflow-2022-23594",
        "CVE-2022-23594",
        "GHSA-9x52-887g-fhc2",
        "PYSEC-2026-3157",
        "PYSEC-2026-3309",
        "PYSEC-2026-752"
      ],
      "metrics": [
        {
          "type": "CVSS_V3",
          "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H"
        }
      ],
      "affected": [
        {
          "ranges": [
            {
              "repo": "https://github.com/tensorflow/tensorflow",
              "type": "GIT",
              "events": [
                {
                  "introduced": "c256c071bb26e1e13b4666d1b3e229e110bc914a"
                },
                {
                  "fixed": "3f878cff5b698b82eea85db2b60d65a2e320850e"
                }
              ],
              "database_specific": {
                "extracted_events": [
                  {
                    "range": [
                      {
                        "introduced": "2.7.0"
                      },
                      {
                        "fixed": "2.8.0"
                      }
                    ],
                    "source": "AFFECTED_FIELD",
                    "strategy": "StringRangeExpression"
                  },
                  {
                    "cpe": "cpe:2.3:a:google:tensorflow:2.7.0:*:*:*:*:*:*:*",
                    "range": [
                      {
                        "introduced": "2.7.0"
                      },
                      {
                        "last_affected": "2.7.0"
                      }
                    ],
                    "source": "CPE_STRING"
                  }
                ]
              }
            }
          ],
          "versions": [
            "2.7.0"
          ],
          "database_specific": {
            "source": "https://storage.googleapis.com/cve-osv-conversion/osv-output/CVE-2022-23594.json"
          }
        }
      ],
      "modified": "2026-10-28T02:30:26.643056673Z",
      "provider": "osv",
      "references": [
        "https://github.com/tensorflow/tensorflow/tree/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/ir/importexport",
        "https://github.com/CVEProject/cvelistV5/tree/main/cves/2022/23xxx/CVE-2022-23594.json",
        "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9x52-887g-fhc2",
        "https://nvd.nist.gov/vuln/detail/CVE-2022-23594"
      ],
      "provider_metadata": {
        "cwe_ids": [
          "CWE-125"
        ],
        "cna_assigner": "GitHub_M",
        "osv_generated_from": "https://github.com/CVEProject/cvelistV5/tree/main/cves/2022/23xxx/CVE-2022-23594.json"
      }
    },
    "related_cves": [
      {
        "value": "CVE-2022-23594",
        "evidence": "/id"
      }
    ],
    "security_relevant": true,
    "severity_evidence": "/severity/0/score"
  },
  "source_id": "core-osv",
  "document_id": 493288,
  "published_at": "2022-02-04T22:32:11+00:00"
}