{"answers":{"affected":"2 open source packages ship an affected version, including pytorch and torch. It affects 1 product.","exploited":"There is no known exploitation of CVE-2025-46150 in the wild and no public exploit code.","fix":"Upgrade pytorch to 2.7.0 or later.","what":"CVE-2025-46150 is a medium-severity vulnerability in n/a, published on 25 Sep 2025. An attacker can exploit it over the internet without logging in, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2025-46150 and fix it if it is affected.\n\nWhat it is: CVE-2025-46150 is a medium-severity vulnerability in n/a, published on 25 Sep 2025. 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-2025-46150 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- pytorch (Bitnami): affected from 2.6.0; fixed in 2.7.0\n- torch (PyPI): affected from 2.6.0; fixed in 2.7.0\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-2025-46150/ (Markdown: https://insights.sbomify.com/CVE-2025-46150/index.md)","id":"CVE-2025-46150","markdown":"https://insights.sbomify.com/CVE-2025-46150/index.md","name":"CVE-2025-46150","record":{"aliases":["BIT-pytorch-2025-46150","DEBIAN-CVE-2025-46150","ECHO-b465-9fdb-5efb","GHSA-v5f2-jg66-6h4h","PYSEC-2025-200","UBUNTU-CVE-2025-46150"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["other"],"network_reachable":"yes","non_default_config":true,"product_category":"PyTorch deep learning library","requires_auth":"none","summary":"Inconsistent results in FractionalMaxPool2d when using torch.compile may lead to unpredictable model behavior.","user_interaction":"not_required","vuln_class":"other"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"mitre","cpe":[{"product":"pytorch","vendor":"linuxfoundation"}],"cvss":{"score":5.3,"severity":"medium","source":"cisa-adp","vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N","version":"3.1"},"cwes":[],"description":"In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.","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":"Other in n/a","id":"CVE-2025-46150","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":null,"products":[],"type":"other","year":"2025"},"n_packages":2,"n_references":4,"next":{"headline":"Integer overflow in n/a","id":"CVE-2025-46152"},"packages":[{"ecosystem":"Bitnami","fixed":["2.7.0"],"introduced":["2.6.0"],"last_affected":[],"package":"pytorch","purl":"pkg:bitnami/pytorch","source":"BIT-pytorch-2025-46150"},{"ecosystem":"PyPI","fixed":["2.7.0"],"introduced":["2.6.0"],"last_affected":[],"package":"torch","purl":"pkg:pypi/torch","source":"PYSEC-2025-200"}],"prev":{"headline":"Denial of service in n/a","id":"CVE-2025-46149"},"products":["n/a"],"published":"2025-09-25","related":[{"cvss_score":9.8,"headline":"Deserialization in n/a","id":"CVE-2024-48063","kev_date_added":null,"published":"2024-10-29"},{"cvss_score":9.8,"headline":"PyTorch vulnerable to arbitrary code execution","id":"CVE-2022-45907","kev_date_added":null,"published":"2022-11-26"},{"cvss_score":9.3,"headline":"PyTorch: `torch.load` with `weights_only=True` leads to remote code execution","id":"CVE-2025-32434","kev_date_added":null,"published":"2025-04-18"},{"cvss_score":8.8,"headline":"PyTorch Vulnerable to Remote Code Execution via Untrusted Checkpoint Files","id":"CVE-2026-24747","kev_date_added":null,"published":"2026-01-27"},{"cvss_score":7.8,"headline":"Pytorch use-after-free vulnerability","id":"CVE-2024-31583","kev_date_added":null,"published":"2024-04-17"},{"cvss_score":7.5,"headline":"Denial of service in n/a","id":"CVE-2025-55551","kev_date_added":null,"published":"2025-09-25"},{"cvss_score":7.5,"headline":"Denial of service in n/a","id":"CVE-2025-55553","kev_date_added":null,"published":"2025-09-25"}],"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":"PyPA Advisory Database","prefix":"PYSEC"},{"class":"attribution","licence":"US public information, NVD API terms","name":"NVD"},{"class":"attribution","licence":"FIRST CVSS licence","name":"CVSS (FIRST)"}],"ssvc":{"automatable":"no","exploitation":"none","technical_impact":"partial"},"state":"PUBLISHED","updated":"2025-09-25","vendors":["n/a"]},"summary":"Inconsistent results in FractionalMaxPool2d when using torch.compile may lead to unpredictable model behavior.","url":"https://insights.sbomify.com/CVE-2025-46150/"}