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.

There are packages installed that are affected by a vulnerability referenced in the following CVE:

  - 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. (CVE-2025-12058)

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available.

  - 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. (CVE-2025-12058)

Note that Nessus relies on the presence of the package as reported by the vendor.

ID	Name	Product	Family	Severity
435855	SCA: security update for keras (GHSA-mq84-hjqx-cwf2)	Tenable Cloud Security	SCA Checks	medium
435855	SCA: security update for keras (GHSA-mq84-hjqx-cwf2)	Tenable Self-Hosted Container Security	SCA Checks	medium
271993	Linux Distros Unpatched Vulnerability : CVE-2025-12058	Nessus	Misc.	medium

Detections

Analytics

CVE-2025-12058

medium

Tenable Plugins

medium

medium

medium