{"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-2021-37679 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-2021-37679 is a high-severity 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-2021-37679 and fix it if it is affected.\n\nWhat it is: CVE-2021-37679 is a high-severity 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-2021-37679 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-2021-37679/ (Markdown: https://insights.sbomify.com/CVE-2021-37679/index.md)","id":"CVE-2021-37679","markdown":"https://insights.sbomify.com/CVE-2021-37679/index.md","name":"CVE-2021-37679","record":{"aliases":["BIT-tensorflow-2021-37679","GHSA-g8wg-cjwc-xhhp","PYSEC-2021-301","PYSEC-2021-592","PYSEC-2021-790"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["data_read"],"network_reachable":"adjacent_or_local","non_default_config":false,"product_category":"TensorFlow machine learning library","requires_auth":"low_privilege","summary":"Attackers can leak heap memory information via uninitialized buffer contents when nesting tf.map_fn with RaggedTensors in TensorFlow.","user_interaction":"not_required","vuln_class":"information_disclosure"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"GitHub_M","cpe":[{"product":"tensorflow","vendor":"google"}],"cvss":{"score":7.1,"severity":"high","source":"cna","vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N","version":"3.1"},"cwes":[{"id":"CWE-681","name":"Incorrect Conversion between Numeric Types"},{"id":"CWE-125","name":"Out-of-bounds Read"}],"description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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":"Heap OOB in nested `tf.map_fn` with `RaggedTensor`s in TensorFlow","id":"CVE-2021-37679","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":"google:tensorflow","products":["google:tensorflow"],"type":"information_disclosure","year":"2021"},"n_packages":22,"n_references":2,"next":{"headline":"Division by zero in TFLite in TensorFlow","id":"CVE-2021-37680"},"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-2021-37679"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-g8wg-cjwc-xhhp"},{"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-2021-301"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-g8wg-cjwc-xhhp"},{"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-2021-592"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.3.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-g8wg-cjwc-xhhp"},{"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-2021-790"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.4.3"],"introduced":["2.4.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-g8wg-cjwc-xhhp"},{"ecosystem":"PyPI","fixed":["2.5.1"],"introduced":["2.5.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-g8wg-cjwc-xhhp"}],"prev":{"headline":"Arbitrary code execution due to YAML deserialization","id":"CVE-2021-37678"},"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 leak heap memory information via uninitialized buffer contents when nesting tf.map_fn with RaggedTensors in TensorFlow.","url":"https://insights.sbomify.com/CVE-2021-37679/"}