{"answers":{"affected":"34 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.","exploited":"There is no known exploitation of CVE-2020-26271 in the wild and no public exploit code.","fix":"Upgrade tensorflow to a fixed version for your release line: 1.15.5, 2.0.4, 2.1.3 or 2.2.2.","what":"CVE-2020-26271 is a medium-severity memory corruption vulnerability in Google Tensorflow, published on 10 Dec 2020. It can only be exploited from the same machine or local network, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2020-26271 and fix it if it is affected.\n\nWhat it is: CVE-2020-26271 is a medium-severity memory corruption vulnerability in Google Tensorflow, published on 10 Dec 2020. It can only be exploited from the same machine or local network, with no action from the victim.\nExploitation: There is no known exploitation of CVE-2020-26271 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- tensorflow (Bitnami): affected all versions; fixed in 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2\n- tensorflow (PyPI): affected all versions; fixed in 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2\n- tensorflow (PyPI): affected all versions; fixed in 1.15.5\n- tensorflow (PyPI): affected all versions; fixed in 1.15.5\n- tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.4\n- tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.4\n- tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.3\n- tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.3\n- tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.2\n- tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.2\n- tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.2\n- tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.2\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-2020-26271/ (Markdown: https://insights.sbomify.com/CVE-2020-26271/index.md)","id":"CVE-2020-26271","markdown":"https://insights.sbomify.com/CVE-2020-26271/index.md","name":"CVE-2020-26271","record":{"aliases":["BIT-tensorflow-2020-26271","GHSA-q263-fvxm-m5mw","PYSEC-2020-257","PYSEC-2020-302","PYSEC-2020-337"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["data_read","other"],"network_reachable":"adjacent_or_local","non_default_config":false,"product_category":"TensorFlow library","requires_auth":"low_privilege","summary":"Loading a malicious saved model in TensorFlow can cause heap out-of-bounds access, potentially leaking memory addresses or causing crashes.","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":4.4,"severity":"medium","source":"cna","vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","version":"3.1"},"cwes":[{"id":"CWE-908","name":"Use of Uninitialized Resource"},{"id":"CWE-125","name":"Out-of-bounds Read"}],"description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","ecosystems":["Bitnami","PyPI"],"exploits":{"exploitdb":0,"first_seen":null,"github_poc":0,"max_poc_stars":null,"metasploit":0,"nuclei":0,"refs":[],"tier":"none_known"},"has_fix":true,"headline":"Heap out of bounds access in MakeEdge in TensorFlow","id":"CVE-2020-26271","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":"google:tensorflow","products":["google:tensorflow"],"type":"memory_corruption","year":"2020"},"n_packages":34,"n_references":2,"next":{"headline":"Electron vulnerable to ID collision when routing IPC messages to renderers containing OOPIFs","id":"CVE-2020-26272"},"packages":[{"ecosystem":"Bitnami","fixed":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"introduced":["0","2.0.0","2.1.0","2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:bitnami/tensorflow","source":"BIT-tensorflow-2020-26271"},{"ecosystem":"PyPI","fixed":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"introduced":["0","2.0.0","2.1.0","2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"PYSEC-2020-257"},{"ecosystem":"PyPI","fixed":["1.15.5"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["1.15.5"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.0.4"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.0.4"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.1.3"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.1.3"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.2.2"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.2.2"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.3.2"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.3.2"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"introduced":["0","2.0.0","2.1.0","2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"PYSEC-2020-302"},{"ecosystem":"PyPI","fixed":["1.15.5"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["1.15.5"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.0.4"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.0.4"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.1.3"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.1.3"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.2.2"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.2.2"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.3.2"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.3.2"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"introduced":["0","2.0.0","2.1.0","2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"PYSEC-2020-337"},{"ecosystem":"PyPI","fixed":["1.15.5"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["1.15.5"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.0.4"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.0.4"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.1.3"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.1.3"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.2.2"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.2.2"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.3.2"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-q263-fvxm-m5mw"},{"ecosystem":"PyPI","fixed":["2.3.2"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-q263-fvxm-m5mw"}],"prev":{"headline":"CHECK-fail in LSTM with zero-length input in TensorFlow","id":"CVE-2020-26270"},"products":["tensorflow"],"published":"2020-12-10","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":null,"exploitation":null,"technical_impact":null},"state":"PUBLISHED","updated":"2024-08-04","vendors":["tensorflow"]},"summary":"Loading a malicious saved model in TensorFlow can cause heap out-of-bounds access, potentially leaking memory addresses or causing crashes.","url":"https://insights.sbomify.com/CVE-2020-26271/"}