Security Bulletin
Summary
IBM Maximo Application Suite - Monitor Component uses keras-3.10.0-py3-none-any.whl, keras-2.14.0-py3-none-any.whl which are vulnerable to CVE-2025-12058, CVE-2025-12060, CVE-2025-9905, CVE-2025-9906. This bulletin contains information regarding the vulnerability and its fixture.
Vulnerability Details
CVEID: CVE-2025-12060
DESCRIPTION: The keras.utils.get_file API in Keras, when used with the extract=True option for tar archives, is vulnerable to a path traversal attack. The utility uses Python's tarfile.extractall function without the filter="data" feature. A remote attacker can craft a malicious tar archive containing special symlinks, which, when extracted, allows them to write arbitrary files to any location on the filesystem outside of the intended destination folder. This vulnerability is linked to the underlying Python tarfile weakness, identified as CVE-2025-4517. Note that upgrading Python to one of the versions that fix CVE-2025-4517 (e.g. Python 3.13.4) is not enough. One additionally needs to upgrade Keras to a version with the fix (Keras 3.12).
CWE: CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
CVSS Source: cve-coordination@google.com
CVSS Base score: 8.9
CVSS Vector: (CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X)
CVEID: CVE-2025-12058
DESCRIPTION: The Keras.Model.load_model method, including when executed with the intended security mitigation safe_mode=True, is vulnerable to arbitrary local file loading and Server-Side Request Forgery (SSRF).
This vulnerability stems from the way the StringLookup layer is handled during model loading from a specially crafted .keras archive. The constructor for the StringLookup layer accepts a vocabulary argument that can specify a local file path or a remote file path.
* Arbitrary Local File Read: An attacker can create a malicious .keras file that embeds a local path in the StringLookup layer's configuration. When the model is loaded, Keras will attempt to read the content of the specified local file and incorporate it into the model state (e.g., retrievable via get_vocabulary()), allowing an attacker to read arbitrary local files on the hosting system.
* Server-Side Request Forgery (SSRF): Keras utilizes tf.io.gfile for file operations. Since tf.io.gfile supports remote filesystem handlers (such as GCS and HDFS) and HTTP/HTTPS protocols, the same mechanism can be leveraged to fetch content from arbitrary network endpoints on the server's behalf, resulting in an SSRF condition.
The security issue is that the feature allowing external path loading was not properly restricted by the safe_mode=True flag, which was intended to prevent such unintended data access.
CWE: CWE-502: Deserialization of Untrusted Data
CVSS Source: cve-coordination@google.com
CVSS Base score: 5.9
CVSS Vector: (CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:P/VC:H/VI:L/VA:L/SC:H/SI:L/SA:L/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X)
CVEID: CVE-2025-9905
DESCRIPTION: The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True.
One can create a specially crafted .h5/.hdf5 model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed.
This 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.
Note that the .h5/.hdf5 format is a legacy format supported by Keras 3 for backwards compatibility.
CWE: CWE-913: Improper Control of Dynamically-Managed Code Resources
CVSS Source: NVD
CVSS Base score: 7.3
CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H)
CVEID: CVE-2025-9906
DESCRIPTION: The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True.
One 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.
CWE: CWE-502: Deserialization of Untrusted Data
CVSS Source: NVD
CVSS Base score: 7.3
CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H)
Affected Products and Versions
| Affected Product(s) | Version(s) |
| IBM Maximo Application Suite - Monitor Component | 9.1 |
| IBM Maximo Application Suite - Monitor Component | 9.0 |
| IBM Maximo Application Suite - Monitor Component | 8.11 |
| IBM Maximo Application Suite - Monitor Component | 8.10 |
Remediation/Fixes
| Remediated Product(s) | Version(s) |
| IBM Maximo Application Suite - Monitor Component | 9.1.7 (available from the Catalog under Update Available) |
| IBM Maximo Application Suite - Monitor Component | 9.0.17 (available from the Catalog under Update Available) |
| IBM Maximo Application Suite - Monitor Component | 8.11.25 (available from the Catalog under Update Available) |
| IBM Maximo Application Suite - Monitor Component | 8.10.27 (available from the Catalog under Update Availabe) |
Workarounds and Mitigations
None
Get Notified about Future Security Bulletins
References
Acknowledgement
Change History
30 Jan 2026: Initial Publication
*The CVSS Environment Score is customer environment specific and will ultimately impact the Overall CVSS Score. Customers can evaluate the impact of this vulnerability in their environments by accessing the links in the Reference section of this Security Bulletin.
Disclaimer
According to the Forum of Incident Response and Security Teams (FIRST), the Common Vulnerability Scoring System (CVSS) is an "industry open standard designed to convey vulnerability severity and help to determine urgency and priority of response." IBM PROVIDES THE CVSS SCORES ""AS IS"" WITHOUT WARRANTY OF ANY KIND, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. CUSTOMERS ARE RESPONSIBLE FOR ASSESSING THE IMPACT OF ANY ACTUAL OR POTENTIAL SECURITY VULNERABILITY. In addition to other efforts to address potential vulnerabilities, IBM periodically updates the record of components contained in our product offerings. As part of that effort, if IBM identifies previously unidentified packages in a product/service inventory, we address relevant vulnerabilities regardless of CVE date. Inclusion of an older CVEID does not demonstrate that the referenced product has been used by IBM since that date, nor that IBM was aware of a vulnerability as of that date. We are making clients aware of relevant vulnerabilities as we become aware of them. "Affected Products and Versions" referenced in IBM Security Bulletins are intended to be only products and versions that are supported by IBM and have not passed their end-of-support or warranty date. Thus, failure to reference unsupported or extended-support products and versions in this Security Bulletin does not constitute a determination by IBM that they are unaffected by the vulnerability. Reference to one or more unsupported versions in this Security Bulletin shall not create an obligation for IBM to provide fixes for any unsupported or extended-support products or versions.
Document Location
Worldwide
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Document Information
Modified date:
30 January 2026
Initial Publish date:
30 January 2026
UID
ibm17258418