{"answers":{"affected":"4 open source packages ship an affected version, including pytorch and torch. It affects 1 product.","exploited":"It is not confirmed as exploited in the wild, but public exploit code for CVE-2025-2149 exists.","fix":"Upgrade pytorch to 2.7.0 or later.","what":"CVE-2025-2149 is a low-severity vulnerability in PyTorch, published on 10 Mar 2025. It can only be exploited from the same machine or local network, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2025-2149 and fix it if it is affected.\n\nWhat it is: CVE-2025-2149 is a low-severity vulnerability in PyTorch, published on 10 Mar 2025. It can only be exploited from the same machine or local network, with no action from the victim.\nExploitation: It is not confirmed as exploited in the wild, but public exploit code for CVE-2025-2149 exists.\n\nAffected packages and fixed versions:\n- pytorch (Bitnami): affected from 2.6.0; fixed in 2.7.0\n- torch (PyPI): affected all versions up to 2.6.0; no fix listed\n- torch (PyPI): affected all versions up to 2.6.0; no fix listed\n- torch (PyPI): affected all versions up to 2.6.0-cu124; no fix listed\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-2025-2149/ (Markdown: https://insights.sbomify.com/CVE-2025-2149/index.md)","id":"CVE-2025-2149","markdown":"https://insights.sbomify.com/CVE-2025-2149/index.md","name":"CVE-2025-2149","record":{"aliases":["BIT-pytorch-2025-2149","DEBIAN-CVE-2025-2149","ECHO-a0c5-9cb9-6a53","GHSA-x3gm-94wq-g975","PYSEC-2025-190","UBUNTU-CVE-2025-2149"],"analysis":{"component_type":"library","fix_type":"upgrade","impacts":["other"],"network_reachable":"adjacent_or_local","non_default_config":false,"product_category":"PyTorch machine learning library","requires_auth":"low_privilege","summary":"Improper initialization in PyTorch's quantized sigmoid module allows local attackers to manipulate scale and zero-point arguments, potentially causing unexpected behavior.","user_interaction":"not_required","vuln_class":"other"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"VulDB","cpe":[{"product":"pytorch","vendor":"linuxfoundation"}],"cvss":{"score":2,"severity":"low","source":"cna","vector":"CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N","version":"4.0"},"cwes":[{"id":"CWE-665","name":"Improper Initialization"}],"description":"A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnq_Sigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zero_point leads to improper initialization. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used.","ecosystems":["Bitnami","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":"PyTorch Quantized Sigmoid Module nnq_Sigmoid initialization","id":"CVE-2025-2149","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"Bitnami","product":null,"products":[],"type":"other","year":"2025"},"n_packages":4,"n_references":5,"next":{"headline":"HGiga C\u0026Cm@il - Stored Cross-Site Scripting","id":"CVE-2025-2150"},"packages":[{"ecosystem":"Bitnami","fixed":["2.7.0"],"introduced":["2.6.0"],"last_affected":[],"package":"pytorch","purl":"pkg:bitnami/pytorch","source":"BIT-pytorch-2025-2149"},{"ecosystem":"PyPI","fixed":[],"introduced":["0"],"last_affected":["2.6.0"],"package":"torch","purl":null,"source":"GHSA-x3gm-94wq-g975"},{"ecosystem":"PyPI","fixed":[],"introduced":["0"],"last_affected":["2.6.0"],"package":"torch","purl":"pkg:pypi/torch","source":"GHSA-x3gm-94wq-g975"},{"ecosystem":"PyPI","fixed":[],"introduced":["0"],"last_affected":["2.6.0-cu124"],"package":"torch","purl":"pkg:pypi/torch","source":"PYSEC-2025-190"}],"prev":{"headline":"PyTorch Tuple torch.ops.profiler._call_end_callbacks_on_jit_fut memory corruption","id":"CVE-2025-2148"},"products":["PyTorch"],"published":"2025-03-10","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":"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":"partial"},"state":"PUBLISHED","updated":"2025-03-10","vendors":["n/a"]},"summary":"Improper initialization in PyTorch's quantized sigmoid module allows local attackers to manipulate scale and zero-point arguments, potentially causing unexpected behavior.","url":"https://insights.sbomify.com/CVE-2025-2149/"}