{"answers":{"affected":"22 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.","exploited":"It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-23573 exists.","fix":"Upgrade tensorflow to a fixed version for your release line: 2.5.3, 2.6.3 or 2.7.1.","what":"CVE-2022-23573 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 4 Feb 2022. Any signed-in user can exploit it over the network, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2022-23573 and fix it if it is affected.\n\nWhat it is: CVE-2022-23573 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 4 Feb 2022. Any signed-in user can exploit it over the network, with no action from the victim.\nExploitation: It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-23573 exists.\n\nAffected packages and fixed versions:\n- tensorflow (Bitnami): affected all versions; fixed in 2.5.3, 2.6.3, 2.7.1\n- tensorflow (PyPI): affected all versions; fixed in 2.5.3, 2.6.3, 2.7.1\n- tensorflow (PyPI): affected all versions; fixed in 2.5.3\n- tensorflow (PyPI): affected all versions; fixed in 2.5.3\n- tensorflow (PyPI): affected from 2.6.0; fixed in 2.6.3\n- tensorflow (PyPI): affected from 2.6.0; fixed in 2.6.3\n- tensorflow (PyPI): affected from 2.7.0; fixed in 2.7.1\n- tensorflow (PyPI): affected from 2.7.0; fixed in 2.7.1\n- tensorflow-cpu (PyPI): affected all versions; fixed in 2.5.3, 2.6.3\n- tensorflow-cpu (PyPI): affected all versions; fixed in 2.5.3\n- tensorflow-cpu (PyPI): affected all versions; fixed in 2.5.3\n- tensorflow-cpu (PyPI): affected from 2.6.0; fixed in 2.6.3\n\nTask:\n1. Search the dependency manifests and lockfiles (for example package.json, package-lock.json, requirements.txt, poetry.lock, uv.lock, pom.xml, build.gradle, go.mod, Cargo.lock, Gemfile.lock) and any Dockerfiles for the affected packages, including transitive dependencies.\n2. If an affected version is in use, upgrade it to the fixed version for its release line with the project's package manager, so the lockfile is regenerated. Prefer the smallest upgrade that fixes it.\n3. If there is no fix, or the upgrade would break the build, do not guess: explain the risk and the safest workaround instead.\n4. Run the tests and summarise what changed and why.\n5. If nothing in the project is affected, say so and change nothing.\n\nReference: https://insights.sbomify.com/CVE-2022-23573/ (Markdown: https://insights.sbomify.com/CVE-2022-23573/index.md)","id":"CVE-2022-23573","markdown":"https://insights.sbomify.com/CVE-2022-23573/index.md","name":"CVE-2022-23573","record":{"aliases":["BIT-tensorflow-2022-23573","GHSA-q85f-69q7-55h2","PYSEC-2022-137","PYSEC-2022-82","PYSEC-2026-3229"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["code_execution","denial_of_service"],"network_reachable":"yes","non_default_config":false,"product_category":"TensorFlow ML library","requires_auth":"low_privilege","summary":"Attackers can trigger undefined behavior and potential code execution via uninitialized memory access in TensorFlow's AssignOp implementation.","user_interaction":"not_required","vuln_class":"memory_corruption"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"GitHub_M","cpe":[{"product":"tensorflow","vendor":"google"}],"cvss":{"score":7.6,"severity":"high","source":"cna","vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","version":"3.1"},"cwes":[{"id":"CWE-908","name":"Use of Uninitialized Resource"}],"description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","ecosystems":["Bitnami","PyPI"],"exploits":{"exploitdb":0,"first_seen":null,"github_poc":0,"max_poc_stars":null,"metasploit":0,"nuclei":0,"refs":[],"tier":"weaponised"},"has_fix":true,"headline":"Uninitialized variable access in Tensorflow","id":"CVE-2022-23573","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":"google:tensorflow","products":["google:tensorflow"],"type":"memory_corruption","year":"2022"},"n_packages":22,"n_references":3,"next":{"headline":"Out of bounds read and write in Tensorflow","id":"CVE-2022-23574"},"packages":[{"ecosystem":"Bitnami","fixed":["2.5.3","2.6.3","2.7.1"],"introduced":["0","2.6.0","2.7.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:bitnami/tensorflow","source":"BIT-tensorflow-2022-23573"},{"ecosystem":"PyPI","fixed":["2.5.3","2.6.3","2.7.1"],"introduced":["0","2.6.0","2.7.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"PYSEC-2026-3229"},{"ecosystem":"PyPI","fixed":["2.5.3"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.5.3"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.6.3"],"introduced":["2.6.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.6.3"],"introduced":["2.6.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.7.1"],"introduced":["2.7.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.7.1"],"introduced":["2.7.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.5.3","2.6.3"],"introduced":["0","2.6.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"PYSEC-2022-82"},{"ecosystem":"PyPI","fixed":["2.5.3"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.5.3"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.6.3"],"introduced":["2.6.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.6.3"],"introduced":["2.6.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.7.1"],"introduced":["2.7.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.7.1"],"introduced":["2.7.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.5.3","2.6.3"],"introduced":["0","2.6.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"PYSEC-2022-137"},{"ecosystem":"PyPI","fixed":["2.5.3"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.5.3"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.6.3"],"introduced":["2.6.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.6.3"],"introduced":["2.6.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.7.1"],"introduced":["2.7.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q85f-69q7-55h2"},{"ecosystem":"PyPI","fixed":["2.7.1"],"introduced":["2.7.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q85f-69q7-55h2"}],"prev":{"headline":"Crash when type cannot be specialized in Tensorflow","id":"CVE-2022-23572"},"products":["tensorflow"],"published":"2022-02-04","related":[{"cvss_score":9.8,"headline":"TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation","id":"CVE-2023-25668","kev_date_added":null,"published":"2023-03-24"},{"cvss_score":9.8,"headline":"Integer Overflow or Wraparound in Google TensorFlow","id":"CVE-2018-7575","kev_date_added":null,"published":"2019-04-24"},{"cvss_score":9.3,"headline":"Arbitrary code execution due to YAML deserialization","id":"CVE-2021-37678","kev_date_added":null,"published":"2021-08-12"},{"cvss_score":9.1,"headline":"Path traversal in n/a","id":"CVE-2021-35958","kev_date_added":null,"published":"2021-06-30"},{"cvss_score":9,"headline":"Integer truncation in Shard API usage","id":"CVE-2020-15202","kev_date_added":null,"published":"2020-09-25"},{"cvss_score":9,"headline":"Data leak in Tensorflow","id":"CVE-2020-15205","kev_date_added":null,"published":"2020-09-25"},{"cvss_score":9,"headline":"Denial of Service in Tensorflow","id":"CVE-2020-15206","kev_date_added":null,"published":"2020-09-25"}],"sources":[{"class":"attribution","licence":"Apache-2.0","name":"Bitnami","prefix":"BIT"},{"class":"attribution","licence":"CVE Terms of Use (MITRE)","name":"CVE List","prefix":"CVE"},{"class":"attribution","licence":"CC BY 4.0","name":"GitHub Advisory Database","prefix":"GHSA"},{"class":"attribution","licence":"CC BY 4.0","name":"PyPA Advisory Database","prefix":"PYSEC"},{"class":"attribution","licence":"US public information, NVD API terms","name":"NVD"},{"class":"attribution","licence":"CWE Terms of Use","name":"CWE (MITRE)"},{"class":"attribution","licence":"FIRST CVSS licence","name":"CVSS (FIRST)"}],"ssvc":{"automatable":"no","exploitation":"poc","technical_impact":"partial"},"state":"PUBLISHED","updated":"2025-04-22","vendors":["tensorflow"]},"summary":"Attackers can trigger undefined behavior and potential code execution via uninitialized memory access in TensorFlow's AssignOp implementation.","url":"https://insights.sbomify.com/CVE-2022-23573/"}