Package Instance
Lookup for vulnerable packages by Package URL.
GET /api/packages/46357?format=api
{ "url": "http://public2.vulnerablecode.io/api/packages/46357?format=api", "purl": "pkg:pypi/keras@3.11.0", "type": "pypi", "namespace": "", "name": "keras", "version": "3.11.0", "qualifiers": {}, "subpath": "", "is_vulnerable": true, "next_non_vulnerable_version": null, "latest_non_vulnerable_version": null, "affected_by_vulnerabilities": [ { "url": "http://public2.vulnerablecode.io/api/vulnerabilities/37184?format=api", "vulnerability_id": "VCID-1xj9-1kng-8ua4", "summary": "Allocation of Resources Without Limits or Throttling in the HDF5 weight loading component in Google Keras 3.0.0 through 3.13.0 on all platforms allows a remote attacker to cause a Denial of Service (DoS) through memory exhaustion and a crash of the Python interpreter via a crafted .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape.", "references": [ { "reference_url": "https://github.com/keras-team/keras/pull/21880", "reference_id": "", "reference_type": "", "scores": [ { "value": "7.5", "scoring_system": "cvssv3.1", "scoring_elements": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H" } ], "url": "https://github.com/keras-team/keras/pull/21880" } ], "fixed_packages": [ { "url": "http://public2.vulnerablecode.io/api/packages/47091?format=api", "purl": "pkg:pypi/keras@3.13.1", "is_vulnerable": true, "affected_by_vulnerabilities": [ { "vulnerability": "VCID-ptyp-n4df-aqf1" } ], "resource_url": "http://public2.vulnerablecode.io/packages/pkg:pypi/keras@3.13.1" } ], "aliases": [ "CVE-2026-0897", "PYSEC-2026-73" ], "risk_score": null, "exploitability": null, "weighted_severity": null, "resource_url": "http://public2.vulnerablecode.io/vulnerabilities/VCID-1xj9-1kng-8ua4" }, { "url": "http://public2.vulnerablecode.io/api/vulnerabilities/37113?format=api", "vulnerability_id": "VCID-dy5p-938j-d7fr", "summary": "The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True.\n\nOne can create a specially crafted .h5/.hdf5 model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed.\n\nThis is achieved by crafting a special .h5 archive file that uses the Lambda layer feature of keras which allows arbitrary Python code in the form of pickled code. The vulnerability comes from the fact that the safe_mode=True option is not honored when reading .h5 archives.\n\nNote that the .h5/.hdf5 format is a legacy format supported by Keras 3 for backwards compatibility.", "references": [ { "reference_url": "https://github.com/keras-team/keras", "reference_id": "", "reference_type": "", "scores": [], "url": "https://github.com/keras-team/keras" }, { "reference_url": "https://github.com/keras-team/keras/pull/21602", "reference_id": "", "reference_type": "", "scores": [ { "value": "7.3", "scoring_system": "cvssv3.1", "scoring_elements": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H" } ], "url": "https://github.com/keras-team/keras/pull/21602" }, { "reference_url": "https://github.com/keras-team/keras/security/advisories/GHSA-36rr-ww3j-vrjv", "reference_id": "", "reference_type": "", "scores": [ { "value": "7.3", "scoring_system": "cvssv3.1", "scoring_elements": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H" } ], "url": "https://github.com/keras-team/keras/security/advisories/GHSA-36rr-ww3j-vrjv" }, { "reference_url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9905", "reference_id": "CVE-2025-9905", "reference_type": "", "scores": [], "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9905" }, { "reference_url": "https://github.com/advisories/GHSA-36rr-ww3j-vrjv", "reference_id": "GHSA-36rr-ww3j-vrjv", "reference_type": "", "scores": [], "url": "https://github.com/advisories/GHSA-36rr-ww3j-vrjv" } ], "fixed_packages": [ { "url": "http://public2.vulnerablecode.io/api/packages/46360?format=api", "purl": "pkg:pypi/keras@3.11.3", "is_vulnerable": true, "affected_by_vulnerabilities": [ { "vulnerability": "VCID-1xj9-1kng-8ua4" } ], "resource_url": "http://public2.vulnerablecode.io/packages/pkg:pypi/keras@3.11.3" } ], "aliases": [ "CVE-2025-9905", "GHSA-36rr-ww3j-vrjv", "PYSEC-2025-123" ], "risk_score": null, "exploitability": null, "weighted_severity": null, "resource_url": "http://public2.vulnerablecode.io/vulnerabilities/VCID-dy5p-938j-d7fr" }, { "url": "http://public2.vulnerablecode.io/api/vulnerabilities/48078?format=api", "vulnerability_id": "VCID-zj76-dr8t-47d2", "summary": "Keras framework vulnerable to deserialization of untrusted data\nDeserialization of untrusted data can occur in versions of the Keras framework running versions 3.11.0 up to but not including 3.11.3, enabling a maliciously uploaded Keras file containing a TorchModuleWrapper class to run arbitrary code on an end user’s system when loaded despite safe mode being enabled. The vulnerability can be triggered through both local and remote files.", "references": [ { "reference_url": "https://github.com/keras-team/keras", "reference_id": "", "reference_type": "", "scores": [], "url": "https://github.com/keras-team/keras" }, { "reference_url": "https://github.com/keras-team/keras/pull/21575", "reference_id": "", "reference_type": "", "scores": [], "url": "https://github.com/keras-team/keras/pull/21575" }, { "reference_url": "https://hiddenlayer.com/sai_security_advisor/2025-10-keras", "reference_id": "", "reference_type": "", "scores": [], "url": "https://hiddenlayer.com/sai_security_advisor/2025-10-keras" }, { "reference_url": "https://nvd.nist.gov/vuln/detail/CVE-2025-49655", "reference_id": "CVE-2025-49655", "reference_type": "", "scores": [], "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-49655" }, { "reference_url": "https://github.com/advisories/GHSA-cvhh-q5g5-qprp", "reference_id": "GHSA-cvhh-q5g5-qprp", "reference_type": "", "scores": [], "url": "https://github.com/advisories/GHSA-cvhh-q5g5-qprp" } ], "fixed_packages": [ { "url": "http://public2.vulnerablecode.io/api/packages/46360?format=api", "purl": "pkg:pypi/keras@3.11.3", "is_vulnerable": true, "affected_by_vulnerabilities": [ { "vulnerability": "VCID-1xj9-1kng-8ua4" } ], "resource_url": "http://public2.vulnerablecode.io/packages/pkg:pypi/keras@3.11.3" } ], "aliases": [ "CVE-2025-49655", "GHSA-cvhh-q5g5-qprp" ], "risk_score": null, "exploitability": null, "weighted_severity": null, "resource_url": "http://public2.vulnerablecode.io/vulnerabilities/VCID-zj76-dr8t-47d2" } ], "fixing_vulnerabilities": [ { "url": "http://public2.vulnerablecode.io/api/vulnerabilities/37114?format=api", "vulnerability_id": "VCID-64yr-ww4w-ckdr", "summary": "The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True.\n\nOne can create a specially crafted .keras model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special config.json (a file within the .keras archive) that will invoke keras.config.enable_unsafe_deserialization() to disable safe mode. Once safe mode is disable, one can use the Lambda layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the keras.config.enable_unsafe_deserialization() needs to appear first in the archive and the Lambda with arbitrary code needs to be second.", "references": [ { "reference_url": "https://github.com/keras-team/keras/commit/713172ab56b864e59e2aa79b1a51b0e728bba858", "reference_id": "", "reference_type": "", "scores": [], "url": "https://github.com/keras-team/keras/commit/713172ab56b864e59e2aa79b1a51b0e728bba858" }, { "reference_url": "https://github.com/keras-team/keras/pull/21429", "reference_id": "", "reference_type": "", "scores": [ { "value": "7.3", "scoring_system": "cvssv3.1", "scoring_elements": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H" } ], "url": "https://github.com/keras-team/keras/pull/21429" }, { "reference_url": "https://github.com/keras-team/keras/releases/tag/v3.11.0", "reference_id": "", "reference_type": "", "scores": [], "url": "https://github.com/keras-team/keras/releases/tag/v3.11.0" }, { "reference_url": "https://github.com/pypa/advisory-database/tree/main/vulns/keras/PYSEC-2025-76.yaml", "reference_id": "", "reference_type": "", "scores": [], "url": "https://github.com/pypa/advisory-database/tree/main/vulns/keras/PYSEC-2025-76.yaml" }, { "reference_url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9906", "reference_id": "CVE-2025-9906", "reference_type": "", "scores": [], "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9906" }, { "reference_url": "https://osv.dev/vulnerability/CVE-2025-9906", "reference_id": "CVE-2025-9906", "reference_type": "", "scores": [], "url": "https://osv.dev/vulnerability/CVE-2025-9906" }, { "reference_url": "https://github.com/advisories/GHSA-36fq-jgmw-4r9c", "reference_id": "GHSA-36fq-jgmw-4r9c", "reference_type": "", "scores": [], "url": "https://github.com/advisories/GHSA-36fq-jgmw-4r9c" } ], "fixed_packages": [ { "url": "http://public2.vulnerablecode.io/api/packages/46357?format=api", "purl": "pkg:pypi/keras@3.11.0", "is_vulnerable": true, "affected_by_vulnerabilities": [ { "vulnerability": "VCID-1xj9-1kng-8ua4" }, { "vulnerability": "VCID-dy5p-938j-d7fr" }, { "vulnerability": "VCID-zj76-dr8t-47d2" } ], "resource_url": "http://public2.vulnerablecode.io/packages/pkg:pypi/keras@3.11.0" } ], "aliases": [ "CVE-2025-9906", "GHSA-36fq-jgmw-4r9c", "PYSEC-2025-76" ], "risk_score": null, "exploitability": null, "weighted_severity": null, "resource_url": "http://public2.vulnerablecode.io/vulnerabilities/VCID-64yr-ww4w-ckdr" } ], "risk_score": null, "resource_url": "http://public2.vulnerablecode.io/packages/pkg:pypi/keras@3.11.0" }