{"answers":{"affected":"3 open source packages ship an affected version, including keras.","exploited":"It is not confirmed as exploited in the wild, but public exploit code for CVE-2026-12570 exists.","fix":"Upgrade keras to 3.15.0 or later.","what":"CVE-2026-12570 is a medium-severity denial of service vulnerability in keras-team/keras, published on 10 Aug 2026. It can only be exploited from the same machine or local network, and only if a user opens a file or follows a link."},"fix_prompt":"Check this codebase for CVE-2026-12570 and fix it if it is affected.\n\nWhat it is: CVE-2026-12570 is a medium-severity denial of service vulnerability in keras-team/keras, published on 10 Aug 2026. It can only be exploited from the same machine or local network, 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-2026-12570 exists.\n\nAffected packages and fixed versions:\n- keras (PyPI): affected all versions; fixed in 3.15.0\n- keras (PyPI): affected all versions; fixed in 3.15.0\n- keras (PyPI): affected all versions; fixed in 3.15.0\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-2026-12570/ (Markdown: https://insights.sbomify.com/CVE-2026-12570/index.md)","id":"CVE-2026-12570","markdown":"https://insights.sbomify.com/CVE-2026-12570/index.md","name":"CVE-2026-12570","record":{"aliases":["DEBIAN-CVE-2026-12570","GHSA-74m6-m3xx-3vmj","MINI-wxmg-9p5w-p3r3","OESA-2026-3952","OESA-2026-3953","OESA-2026-4052","PYSEC-2026-3856","UBUNTU-CVE-2026-12570"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["denial_of_service"],"network_reachable":"adjacent_or_local","non_default_config":false,"product_category":"Keras deep learning library","requires_auth":"none","summary":"Attackers can cause denial of service via memory exhaustion by loading malicious Keras model files, affecting ML pipelines processing untrusted models.","user_interaction":"required","vuln_class":"denial_of_service"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"@huntr_ai","cpe":[],"cvss":{"score":5.5,"severity":"medium","source":"cna","vector":"CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H","version":"3.0"},"cwes":[{"id":"CWE-770","name":"Allocation of Resources Without Limits or Throttling"}],"description":"A vulnerability in keras-team/keras versions \u003c= 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.","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":"Denial of Service via HDF5 Shape Bomb in keras.models.load_model() in keras-team/keras","id":"CVE-2026-12570","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"PyPI","product":null,"products":[],"type":"denial_of_service","year":"2026"},"n_packages":3,"n_references":2,"next":{"headline":"Authentication Bypass Leading to Account Takeover","id":"CVE-2026-12571"},"packages":[{"ecosystem":"PyPI","fixed":["3.15.0"],"introduced":["0"],"last_affected":[],"package":"keras","purl":null,"source":"GHSA-74m6-m3xx-3vmj"},{"ecosystem":"PyPI","fixed":["3.15.0"],"introduced":["0"],"last_affected":[],"package":"keras","purl":"pkg:pypi/keras","source":"GHSA-74m6-m3xx-3vmj"},{"ecosystem":"PyPI","fixed":["3.15.0"],"introduced":["0"],"last_affected":[],"package":"keras","purl":"pkg:pypi/keras","source":"PYSEC-2026-3856"}],"prev":{"headline":"PTC Windchill and FlexPLM Improper Input Validation","id":"CVE-2026-12569"},"products":["keras-team/keras"],"published":"2026-08-10","related":[],"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":"CWE Terms of Use","name":"CWE (MITRE)"},{"class":"attribution","licence":"FIRST CVSS licence","name":"CVSS (FIRST)"}],"ssvc":{"automatable":"no","exploitation":"poc","technical_impact":"partial"},"state":"PUBLISHED","updated":"2026-08-10","vendors":["keras-team"]},"summary":"Attackers can cause denial of service via memory exhaustion by loading malicious Keras model files, affecting ML pipelines processing untrusted models.","url":"https://insights.sbomify.com/CVE-2026-12570/"}