{"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-2025-46153 in the wild and no public exploit code.","fix":"Upgrade pytorch to 2.7.0 or later.","what":"CVE-2025-46153 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-2025-46153 and fix it if it is affected.\n\nWhat it is: CVE-2025-46153 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-2025-46153 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-2025-46153/ (Markdown: https://insights.sbomify.com/CVE-2025-46153/index.md)","id":"CVE-2025-46153","markdown":"https://insights.sbomify.com/CVE-2025-46153/index.md","name":"CVE-2025-46153","record":{"aliases":["BIT-pytorch-2025-46153","DEBIAN-CVE-2025-46153","ECHO-df59-c098-b576","GHSA-h36m-9qjr-pv93","PYSEC-2025-202","UBUNTU-CVE-2025-46153"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["other"],"network_reachable":"yes","non_default_config":true,"product_category":"PyTorch machine learning library","requires_auth":"none","summary":"Inconsistent decomposition logic in PyTorch's bernoulli_p function affects dropout layers when fallback_random is enabled, potentially causing unexpected 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":[{"id":"CWE-1176","name":"Inefficient CPU Computation"}],"description":"PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallback_random=True.","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-2025-46153","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":null,"products":[],"type":"other","year":"2025"},"n_packages":2,"n_references":5,"next":{"headline":"SQL injection in n/a","id":"CVE-2025-46154"},"packages":[{"ecosystem":"Bitnami","fixed":["2.7.0"],"introduced":["2.6.0"],"last_affected":[],"package":"pytorch","purl":"pkg:bitnami/pytorch","source":"BIT-pytorch-2025-46153"},{"ecosystem":"PyPI","fixed":["2.7.0"],"introduced":["2.6.0"],"last_affected":[],"package":"torch","purl":"pkg:pypi/torch","source":"PYSEC-2025-202"}],"prev":{"headline":"Integer overflow in n/a","id":"CVE-2025-46152"},"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":"CWE Terms of Use","name":"CWE (MITRE)"},{"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 decomposition logic in PyTorch's bernoulli_p function affects dropout layers when fallback_random is enabled, potentially causing unexpected behavior.","url":"https://insights.sbomify.com/CVE-2025-46153/"}