{"answers":{"affected":"16 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-2020-15214 in the wild and no public exploit code.","fix":"Upgrade tensorflow to a fixed version for your release line: 2.2.1 or 2.3.1.","what":"CVE-2020-15214 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 25 Sep 2020. An attacker can exploit it over the internet without logging in, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2020-15214 and fix it if it is affected.\n\nWhat it is: CVE-2020-15214 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 25 Sep 2020. An attacker can exploit it over the internet without logging in, with no action from the victim.\nExploitation: There is no known exploitation of CVE-2020-15214 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- tensorflow (Bitnami): affected from 2.2.0; fixed in 2.2.1, 2.3.1\n- tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.1, 2.3.1\n- tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.1\n- tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.1\n- tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.1\n- tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.1\n- tensorflow-cpu (PyPI): affected from 2.2.0; fixed in 2.2.1, 2.3.1\n- tensorflow-cpu (PyPI): affected from 2.2.0; fixed in 2.2.1\n- tensorflow-cpu (PyPI): affected from 2.2.0; fixed in 2.2.1\n- tensorflow-cpu (PyPI): affected from 2.3.0; fixed in 2.3.1\n- tensorflow-cpu (PyPI): affected from 2.3.0; fixed in 2.3.1\n- tensorflow-gpu (PyPI): affected from 2.2.0; fixed in 2.2.1, 2.3.1\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-2020-15214/ (Markdown: https://insights.sbomify.com/CVE-2020-15214/index.md)","id":"CVE-2020-15214","markdown":"https://insights.sbomify.com/CVE-2020-15214/index.md","name":"CVE-2020-15214","record":{"aliases":["BIT-tensorflow-2020-15214","GHSA-p2cq-cprg-frvm","PYSEC-2020-137","PYSEC-2020-294","PYSEC-2020-329"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["code_execution","denial_of_service"],"network_reachable":"yes","non_default_config":false,"product_category":"TensorFlow Lite library","requires_auth":"none","summary":"An out-of-bounds write in TensorFlow Lite allows attackers to cause crashes or potentially execute code via crafted models with unsorted segment IDs.","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":8.1,"severity":"high","source":"cna","vector":"CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H","version":"3.1"},"cwes":[{"id":"CWE-787","name":"Out-of-bounds Write"}],"description":"In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.","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":"Out of bounds write in tensorflow-lite","id":"CVE-2020-15214","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":"google:tensorflow","products":["google:tensorflow"],"type":"memory_corruption","year":"2020"},"n_packages":16,"n_references":3,"next":{"headline":"Context isolation bypass in Electron","id":"CVE-2020-15215"},"packages":[{"ecosystem":"Bitnami","fixed":["2.2.1","2.3.1"],"introduced":["2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:bitnami/tensorflow","source":"BIT-tensorflow-2020-15214"},{"ecosystem":"PyPI","fixed":["2.2.1","2.3.1"],"introduced":["2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"PYSEC-2020-137"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.2.1","2.3.1"],"introduced":["2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"PYSEC-2020-294"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.2.1","2.3.1"],"introduced":["2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"PYSEC-2020-329"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-p2cq-cprg-frvm"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-p2cq-cprg-frvm"}],"prev":{"headline":"Denial of service in tensorflow-lite","id":"CVE-2020-15213"},"products":["tensorflow"],"published":"2020-09-25","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":"An out-of-bounds write in TensorFlow Lite allows attackers to cause crashes or potentially execute code via crafted models with unsorted segment IDs.","url":"https://insights.sbomify.com/CVE-2020-15214/"}