CVE-2026-11816: CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') in keras-team keras-team/keras
Keras versions prior to 3.14.0 are vulnerable to a path traversal issue in the archive extraction utilities located in `keras/src/utils/file_utils.py`. The functions `filter_safe_tarinfos()` and `filter_safe_zipinfos()` validate archive member paths against the process current working directory (CWD) instead of the actual extraction destination. When the process runs with CWD set to `/`, which is common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass the security check. Additionally, the zip filter contains a bug that causes an `AttributeError` when a blocked entry is encountered, leading to incomplete extraction. Furthermore, Python 3.11 installations lack the `filter="data"` safety net, leaving them entirely reliant on the flawed CWD-based filter. Exploitation of this vulnerability can result in arbitrary file writes outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines.
AI Analysis
Technical Summary
The vulnerability (CVE-2026-11816) in keras-team/keras affects versions before 3.14.0. It is a path traversal issue (CWE-22) in the archive extraction functions filter_safe_tarinfos() and filter_safe_zipinfos() located in keras/src/utils/file_utils.py. These functions validate archive member paths against the process's current working directory (CWD) instead of the actual extraction destination. When the CWD is set to '/', common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass security checks. The zip filter also has a bug causing an AttributeError when a blocked entry is encountered, resulting in incomplete extraction. Python 3.11 installations lack the filter="data" safety net, making them fully dependent on the flawed CWD-based filter. This vulnerability can be exploited to write arbitrary files outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines. The CVSS v3.0 score is 8.1 (High), with network attack vector, low attack complexity, no privileges required, user interaction required, unchanged scope, and high confidentiality and integrity impacts.
Potential Impact
Exploitation of this vulnerability allows an attacker to perform arbitrary file writes outside the intended extraction directory. This can lead to overwriting critical configuration files, injecting malicious code, or corrupting machine learning datasets and pipelines. The vulnerability affects confidentiality and integrity but does not impact availability. The high CVSS score reflects the significant risk of unauthorized data modification and potential system compromise in affected environments.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. There is no explicit mention of an official fix or patch availability in the provided vendor advisory content. Users should monitor the official Keras project and vendor advisories for updates. Until a patch is available, avoid running Keras archive extraction in environments where the current working directory is set to the filesystem root, such as certain Docker containers or CI/CD runners. Consider running extraction processes with a controlled working directory and apply additional manual path validation if possible.
CVE-2026-11816: CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') in keras-team keras-team/keras
Description
Keras versions prior to 3.14.0 are vulnerable to a path traversal issue in the archive extraction utilities located in `keras/src/utils/file_utils.py`. The functions `filter_safe_tarinfos()` and `filter_safe_zipinfos()` validate archive member paths against the process current working directory (CWD) instead of the actual extraction destination. When the process runs with CWD set to `/`, which is common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass the security check. Additionally, the zip filter contains a bug that causes an `AttributeError` when a blocked entry is encountered, leading to incomplete extraction. Furthermore, Python 3.11 installations lack the `filter="data"` safety net, leaving them entirely reliant on the flawed CWD-based filter. Exploitation of this vulnerability can result in arbitrary file writes outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines.
CVSS v3.0
Score 8.1high
Affected software
Run on your own infrastructure? Check whether these packages are installed with threat-finder — our free open-source scanner.
Weaknesses
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The vulnerability (CVE-2026-11816) in keras-team/keras affects versions before 3.14.0. It is a path traversal issue (CWE-22) in the archive extraction functions filter_safe_tarinfos() and filter_safe_zipinfos() located in keras/src/utils/file_utils.py. These functions validate archive member paths against the process's current working directory (CWD) instead of the actual extraction destination. When the CWD is set to '/', common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass security checks. The zip filter also has a bug causing an AttributeError when a blocked entry is encountered, resulting in incomplete extraction. Python 3.11 installations lack the filter="data" safety net, making them fully dependent on the flawed CWD-based filter. This vulnerability can be exploited to write arbitrary files outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines. The CVSS v3.0 score is 8.1 (High), with network attack vector, low attack complexity, no privileges required, user interaction required, unchanged scope, and high confidentiality and integrity impacts.
Potential Impact
Exploitation of this vulnerability allows an attacker to perform arbitrary file writes outside the intended extraction directory. This can lead to overwriting critical configuration files, injecting malicious code, or corrupting machine learning datasets and pipelines. The vulnerability affects confidentiality and integrity but does not impact availability. The high CVSS score reflects the significant risk of unauthorized data modification and potential system compromise in affected environments.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. There is no explicit mention of an official fix or patch availability in the provided vendor advisory content. Users should monitor the official Keras project and vendor advisories for updates. Until a patch is available, avoid running Keras archive extraction in environments where the current working directory is set to the filesystem root, such as certain Docker containers or CI/CD runners. Consider running extraction processes with a controlled working directory and apply additional manual path validation if possible.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- @huntr_ai
- Date Reserved
- 2026-06-09T16:16:56.354Z
- Cvss Version
- 3.0
- State
- PUBLISHED
- Remediation Level
- null
- Vendor Advisory Urls
- [{"url":"https://access.redhat.com/security/cve/CVE-2026-11816","vendor":"Red Hat"}]
Threat ID: 6a2ac2f3815e7002b8f50204
Added to database: 06/11/2026, 14:15:15 UTC
Last enriched: 07/23/2026, 00:37:10 UTC
Last updated: 07/31/2026, 19:22:57 UTC
Views: 65
Community Reviews
0 reviewsCrowdsource mitigation strategies, share intel context, and vote on the most helpful responses. Sign in to add your voice and help keep defenders ahead.
Want to contribute mitigation steps or threat intel context? Sign in or create an account to join the community discussion.
Actions
Updates to AI analysis require Pro Console access. Upgrade inside Console → Billing.
Need more coverage?
Upgrade to Pro Console for AI refresh and higher limits.
For incident response and remediation, OffSeq services can help resolve threats faster.
Latest Threats
Check if your credentials are on the dark web
Instant breach scanning across billions of leaked records. Free tier available.