{"answers":{"affected":"34 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 2 products.","exploited":"There is no known exploitation of CVE-2020-15208 in the wild and no public exploit code.","fix":"Upgrade tensorflow to a fixed version for your release line: 1.15.4, 2.0.3, 2.1.2 or 2.2.1.","what":"CVE-2020-15208 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-2020-15208 and fix it if it is affected.\n\nWhat it is: CVE-2020-15208 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-2020-15208 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- tensorflow (Bitnami): affected all versions; fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1\n- tensorflow (PyPI): affected all versions; fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1\n- tensorflow (PyPI): affected all versions; fixed in 1.15.4\n- tensorflow (PyPI): affected all versions; fixed in 1.15.4\n- tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.3\n- tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.3\n- tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.2\n- tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.2\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\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-2020-15208/ (Markdown: https://insights.sbomify.com/CVE-2020-15208/index.md)","id":"CVE-2020-15208","markdown":"https://insights.sbomify.com/CVE-2020-15208/index.md","name":"CVE-2020-15208","record":{"aliases":["BIT-tensorflow-2020-15208","GHSA-mxjj-953w-2c2v","PYSEC-2020-131","PYSEC-2020-288","PYSEC-2020-323"],"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":"Attackers can trigger out-of-bounds reads and writes in TensorFlow Lite by crafting tensors with mismatched dimensions, potentially leading to code execution or crashes.","user_interaction":"not_required","vuln_class":"memory_corruption"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"GitHub_M","cpe":[{"product":"tensorflow","vendor":"google"},{"product":"leap","vendor":"opensuse"}],"cvss":{"score":7.4,"severity":"high","source":"cna","vector":"CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N","version":"3.1"},"cwes":[{"id":"CWE-787","name":"Out-of-bounds Write"},{"id":"CWE-125","name":"Out-of-bounds Read"}],"description":"In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.","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":"Data corruption in tensorflow-lite","id":"CVE-2020-15208","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":"google:tensorflow","products":["google:tensorflow","opensuse:leap"],"type":"memory_corruption","year":"2020"},"n_packages":34,"n_references":4,"next":{"headline":"Null pointer dereference in tensorflow-lite","id":"CVE-2020-15209"},"packages":[{"ecosystem":"Bitnami","fixed":["1.15.4","2.0.3","2.1.2","2.2.1","2.3.1"],"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-2020-15208"},{"ecosystem":"PyPI","fixed":["1.15.4","2.0.3","2.1.2","2.2.1","2.3.1"],"introduced":["0","2.0.0","2.1.0","2.2.0","2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"PYSEC-2020-131"},{"ecosystem":"PyPI","fixed":["1.15.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["1.15.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.0.3"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.0.3"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.1.2"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.1.2"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow","purl":"pkg:pypi/tensorflow","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["1.15.4","2.0.3","2.1.2","2.2.1","2.3.1"],"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-2020-288"},{"ecosystem":"PyPI","fixed":["1.15.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["1.15.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.0.3"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.0.3"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.1.2"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.1.2"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-cpu","purl":"pkg:pypi/tensorflow-cpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["1.15.4","2.0.3","2.1.2","2.2.1","2.3.1"],"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-2020-323"},{"ecosystem":"PyPI","fixed":["1.15.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["1.15.4"],"introduced":["0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.0.3"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.0.3"],"introduced":["2.0.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.1.2"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.1.2"],"introduced":["2.1.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.2.1"],"introduced":["2.2.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":null,"source":"GHSA-mxjj-953w-2c2v"},{"ecosystem":"PyPI","fixed":["2.3.1"],"introduced":["2.3.0"],"last_affected":[],"package":"tensorflow-gpu","purl":"pkg:pypi/tensorflow-gpu","source":"GHSA-mxjj-953w-2c2v"}],"prev":{"headline":"Segfault and data corruption in tensorflow-lite","id":"CVE-2020-15207"},"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":"Attackers can trigger out-of-bounds reads and writes in TensorFlow Lite by crafting tensors with mismatched dimensions, potentially leading to code execution or crashes.","url":"https://insights.sbomify.com/CVE-2020-15208/"}