{"answers":{"affected":"3 open source packages ship an affected version, including keras. It affects 1 product.","exploited":"It is not confirmed as exploited in the wild, but public exploit code for CVE-2026-11816 exists.","fix":"Upgrade keras to 3.14.0 or later.","what":"CVE-2026-11816 is a high-severity path traversal vulnerability in keras-team/keras, published on 11 Jun 2026. An attacker can exploit it over the internet without logging in, and only if a user opens a file or follows a link."},"fix_prompt":"Check this codebase for CVE-2026-11816 and fix it if it is affected.\n\nWhat it is: CVE-2026-11816 is a high-severity path traversal vulnerability in keras-team/keras, published on 11 Jun 2026. An attacker can exploit it over the internet without logging in, and only if a user opens a file or follows a link.\nExploitation: It is not confirmed as exploited in the wild, but public exploit code for CVE-2026-11816 exists.\n\nAffected packages and fixed versions:\n- keras (PyPI): affected all versions; fixed in 3.14.0\n- keras (PyPI): affected all versions; fixed in 3.14.0\n- keras (PyPI): affected all versions; fixed in 3.14.0\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-2026-11816/ (Markdown: https://insights.sbomify.com/CVE-2026-11816/index.md)","id":"CVE-2026-11816","markdown":"https://insights.sbomify.com/CVE-2026-11816/index.md","name":"CVE-2026-11816","record":{"aliases":["DEBIAN-CVE-2026-11816","GHSA-hqp4-2352-xf5r","MINI-vgxg-gqvx-mx62","PYSEC-2026-2324","UBUNTU-CVE-2026-11816"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["code_execution","data_modify"],"network_reachable":"yes","non_default_config":true,"product_category":"Keras deep learning library","requires_auth":"none","summary":"Attackers can write arbitrary files outside the extraction directory via path traversal in Keras archive utilities, potentially leading to code execution or data corruption.","user_interaction":"required","vuln_class":"path_traversal"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"@huntr_ai","cpe":[{"product":"keras","vendor":"keras"}],"cvss":{"score":8.1,"severity":"high","source":"cna","vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N","version":"3.0"},"cwes":[{"id":"CWE-22","name":"Improper Limitation of a Pathname to a Restricted Directory"}],"description":"Keras versions prior to 3.14.0 are vulnerable to a path traversal issue in the archive extraction utilities located in `keras/src/utils/file_utils.py`. The functions `filter_safe_tarinfos()` and `filter_safe_zipinfos()` validate archive member paths against the process current working directory (CWD) instead of the actual extraction destination. When the process runs with CWD set to `/`, which is common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass the security check. Additionally, the zip filter contains a bug that causes an `AttributeError` when a blocked entry is encountered, leading to incomplete extraction. Furthermore, Python 3.11 installations lack the `filter=\"data\"` safety net, leaving them entirely reliant on the flawed CWD-based filter. Exploitation of this vulnerability can result in arbitrary file writes outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines.","ecosystems":["PyPI"],"exploits":{"exploitdb":0,"first_seen":null,"github_poc":0,"max_poc_stars":null,"metasploit":0,"nuclei":0,"refs":[],"tier":"weaponised"},"has_fix":true,"headline":"Path Traversal in keras-team/keras","id":"CVE-2026-11816","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"PyPI","product":null,"products":[],"type":"path_traversal","year":"2026"},"n_packages":3,"n_references":2,"next":{"headline":"CVE-2026-11817 CVE Record","id":"CVE-2026-11817"},"packages":[{"ecosystem":"PyPI","fixed":["3.14.0"],"introduced":["0"],"last_affected":[],"package":"keras","purl":null,"source":"GHSA-hqp4-2352-xf5r"},{"ecosystem":"PyPI","fixed":["3.14.0"],"introduced":["0"],"last_affected":[],"package":"keras","purl":"pkg:pypi/keras","source":"GHSA-hqp4-2352-xf5r"},{"ecosystem":"PyPI","fixed":["3.14.0"],"introduced":["0"],"last_affected":[],"package":"keras","purl":"pkg:pypi/keras","source":"PYSEC-2026-2324"}],"prev":{"headline":"Insecure Deserialization via MITM in Layer 7 Policy Manager","id":"CVE-2026-11815"},"products":["keras-team/keras"],"published":"2026-06-11","related":[{"cvss_score":9.8,"headline":"Arbitrary code injection vulnerability in Keras framework \u003c 2.13","id":"CVE-2024-3660","kev_date_added":null,"published":"2024-04-16"},{"cvss_score":8.8,"headline":"Deserialization of Untrusted Data in keras-team/keras","id":"CVE-2026-12481","kev_date_added":null,"published":"2026-07-03"},{"cvss_score":8.8,"headline":"Safe Mode Bypass in keras-team/keras","id":"CVE-2026-1462","kev_date_added":null,"published":"2026-04-13"},{"cvss_score":8.6,"headline":"Arbitrary Code execution in Keras Safe Mode","id":"CVE-2025-9906","kev_date_added":null,"published":"2025-09-19"},{"cvss_score":8.6,"headline":"Keras safe_mode bypass allows arbitrary code execution when loading a malicious model.","id":"CVE-2025-8747","kev_date_added":null,"published":"2025-08-11"},{"cvss_score":7.3,"headline":"Arbitary Code execution in Keras load_model()","id":"CVE-2025-9905","kev_date_added":null,"published":"2025-09-19"}],"sources":[{"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":"no","exploitation":"poc","technical_impact":"total"},"state":"PUBLISHED","updated":"2026-07-21","vendors":["keras-team"]},"summary":"Attackers can write arbitrary files outside the extraction directory via path traversal in Keras archive utilities, potentially leading to code execution or data corruption.","url":"https://insights.sbomify.com/CVE-2026-11816/"}