CVE-2026-0897 is classified as a high-severity vulnerability affecting Google Keras versions 3.0.0 through 3.13.0 across all platforms. This vulnerability allows allocation of resources without limits or throttling in the HDF5 weight loading component. A remote attacker can exploit this flaw to cause a Denial of Service (DoS) by triggering memory exhaustion, leading to a crash of the Python interpreter. This is executed via a crafted .keras archive containing a valid model.weights.h5 file with a dataset that declares an extremely large shape.
The CVSS 4.0 base score of 7.1 indicates a high severity level, reflecting the potential impact on availability. The attack vector is network-based, and the complexity is low, meaning that exploitation can occur easily without requiring significant resources or specialized skills. Given the potential for service disruption, organizations utilizing affected versions of Google Keras are urged to take immediate action.
The urgency for defenders is underscored by the absence of a known vendor patch at this time. Organizations should monitor for updates and prepare to deploy corrective measures as soon as they become available. The risk to organizations includes potential interruptions in service, which could affect user experience and business operations.
Given the high exploitability of this vulnerability, organizations should proactively assess their use of Google Keras to identify any dependencies on the affected versions. Preparing for rapid remediation will be crucial to mitigate the risks associated with this vulnerability.
Vulnerability Details
The official description of CVE-2026-0897 states that it involves the allocation of resources without limits or throttling in the HDF5 weight loading component in Google Keras versions 3.0.0 through 3.13.0. This issue allows a remote attacker to exploit the vulnerability via crafted files that can exhaust memory and crash the Python interpreter.
The CVSS score for this vulnerability is recorded at 7.1, indicating a high severity level due to the significant availability impact. The affected products are specifically identified as Keras.
The CWE classification for this vulnerability is CWE-770, which pertains to allocation of resources without limits or throttling. Organizations using affected versions should prioritize remediation efforts.
Technical Analysis
The root cause of this vulnerability stems from insufficient resource allocation controls within the HDF5 weight loading component of Google Keras. Attackers can exploit this vulnerability remotely through crafted .keras files that declare excessively large shapes in their datasets. The attack vector is network-based, allowing attackers to send malicious files to the vulnerable application.
Exploitation does not require any privileges, as the vulnerability can be triggered by any remote user with access to the system. User interaction is passive, meaning that the act of opening the crafted file is sufficient for the attack to occur. The incident leads to a high impact on availability, as it can exhaust memory resources and cause the Python interpreter to crash.
Due to the low complexity of the attack, this vulnerability represents a significant risk to organizations using affected versions of Keras. Attackers may leverage this vulnerability to disrupt service, leading to downtime and potential data loss. Organizations should assess the impact of this vulnerability in their environment and implement necessary precautions.
Risk & Impact Analysis
The real-world risk associated with CVE-2026-0897 is substantial, especially for organizations that rely on Google Keras for machine learning and data processing tasks. Given the high CVSS score of 7.1, the vulnerability poses a significant threat to service availability. If exploited, the potential for downtime can severely impact business operations, especially in environments where Keras is integrated into critical systems.
The potential blast radius includes any application or service relying on Keras, leading to widespread service interruptions. The urgency of addressing this vulnerability is heightened by its exploitability. Organizations should consider this vulnerability a priority in their remediation cycles.
Given the lack of a patch, organizations must prepare for mitigation strategies, which may include implementing resource limits or monitoring memory usage closely to detect potential exploitation attempts. The absence of known public exploits increases the urgency for immediate remediation efforts.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | Yes |
Public PoC | Yes |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerability affects Keras versions from 3.0.0 to 3.13.0 on all platforms. If your organization is using any of these versions, it is crucial to assess your exposure and implement timely remediation.
Mitigation & Remediation
Organizations should prioritize patching immediately. Until a patch is available, consider implementing resource limits to mitigate the impact of this vulnerability. Monitoring memory usage can help detect potential exploitation attempts early.
For more comprehensive security assessments, organizations may want to explore our penetration testing services to identify similar weaknesses in their environments.
Detection Guidance
Organizations should monitor logs for unusual memory usage patterns and system crashes that may indicate an attempted exploitation of this vulnerability. Behavioral anomalies in applications using Keras may also serve as indicators of exploitation.
AppSecure Threat Intelligence Insight
CVE-2026-0897 highlights the ongoing challenges in managing resource allocation in machine learning frameworks. It serves as a reminder for organizations to implement adequate resource constraints and to stay informed about vulnerabilities in their technology stack. The trend of vulnerabilities that lead to Denial of Service attacks emphasizes the need for proactive security measures.
For further insights, organizations can refer to our vulnerability management program design to enhance their security posture.
Additionally, our article on penetration testing methodology provides guidance on how to effectively identify and mitigate vulnerabilities.
Finally, our resource on API penetration testing can help organizations assess security in their deployment of machine learning applications.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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