{"answers":{"affected":"22 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-2021-37690 in the wild and no public exploit code.","fix":"Upgrade tensorflow to a fixed version for your release line: 2.3.4, 2.4.3 or 2.5.1.","what":"CVE-2021-37690 is a medium-severity use after free vulnerability in Google Tensorflow, published on 12 Aug 2021. 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-2021-37690 and fix it if it is affected.\n\nWhat it is: CVE-2021-37690 is a medium-severity use after free vulnerability in Google Tensorflow, published on 12 Aug 2021. 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-2021-37690 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- tensorflow (Bitnami): affected from 2.3.0; fixed in 2.3.4, 2.4.3, 2.5.1\n- tensorflow (PyPI): affected all versions; fixed in 2.3.4\n- tensorflow (PyPI): affected all versions; fixed in 2.3.4\n- tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.4, 2.4.3\n- tensorflow (PyPI): affected from 2.4.0; fixed in 2.4.3\n- tensorflow (PyPI): affected from 2.4.0; fixed in 2.4.3\n- tensorflow (PyPI): affected from 2.5.0; fixed in 2.5.1\n- tensorflow (PyPI): affected from 2.5.0; fixed in 2.5.1\n- tensorflow-cpu (PyPI): affected all versions; fixed in 2.3.4\n- tensorflow-cpu (PyPI): affected all versions; fixed in 2.3.4\n- tensorflow-cpu (PyPI): affected from 2.3.0; fixed in 2.3.4, 2.4.3\n- tensorflow-cpu (PyPI): affected from 2.4.0; fixed in 2.4.3\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-2021-37690/ (Markdown: https://insights.sbomify.com/CVE-2021-37690/index.md)","id":"CVE-2021-37690","markdown":"https://insights.sbomify.com/CVE-2021-37690/index.md","name":"CVE-2021-37690","record":{"aliases":["BIT-tensorflow-2021-37690","GHSA-3hxh-8cp2-g4hg","PYSEC-2021-312","PYSEC-2021-603","PYSEC-2021-801"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["denial_of_service"],"network_reachable":"adjacent_or_local","non_default_config":false,"product_category":"TensorFlow machine learning library","requires_auth":"low_privilege","summary":"Attackers can trigger a use-after-free vulnerability in TensorFlow shape inference functions, causing a segmentation fault and denial of service.","user_interaction":"not_required","vuln_class":"use_after_free"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"GitHub_M","cpe":[{"product":"tensorflow","vendor":"google"}],"cvss":{"score":6.6,"severity":"medium","source":"cna","vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","version":"3.1"},"cwes":[{"id":"CWE-416","name":"Use After Free"}],"description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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":"none_known"},"has_fix":true,"headline":"Use after free and segfault in shape inference functions in TensorFlow","id":"CVE-2021-37690","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":"google:tensorflow","products":["google:tensorflow"],"type":"use_after_free","year":"2021"},"n_packages":22,"n_references":2,"next":{"headline":"Division by zero in LSH in TensorFlow Lite","id":"CVE-2021-37691"},"packages":[{"ecosystem":"Bitnami","fixed":["2.3.4","2.4.3","2.5.1"],"introduced":["2.3.0","2.4.0","2.5.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:bitnami/tensorflow","source":"BIT-tensorflow-2021-37690"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4","2.4.3"],"introduced":["2.3.0","2.4.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"PYSEC-2021-312"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4","2.4.3"],"introduced":["2.3.0","2.4.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"PYSEC-2021-603"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.3.4","2.4.3"],"introduced":["2.3.0","2.4.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"PYSEC-2021-801"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-3hxh-8cp2-g4hg"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-3hxh-8cp2-g4hg"}],"prev":{"headline":"Null pointer dereference in TensorFlow Lite MLIR optimizations","id":"CVE-2021-37689"},"products":["tensorflow"],"published":"2021-08-12","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":"Attackers can trigger a use-after-free vulnerability in TensorFlow shape inference functions, causing a segmentation fault and denial of service.","url":"https://insights.sbomify.com/CVE-2021-37690/"}