https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Sat, 03 Oct 2026 16:47:17 +0000 https://cve.radiocsirt.org/vuln/bit-elasticsearch-2026-72642 <p><strong>Affected:</strong> Bitnami: elasticsearch</p> <p>The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.</p> <p><strong>Affected:</strong> Bitnami: elasticsearch</p> <p>The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.</p> https://cve.radiocsirt.org/vuln/bit-elasticsearch-2026-72642 https://cve.radiocsirt.org/vuln/euvd-2026-352378 EUVD-2026-352378 https://cve.radiocsirt.org/vuln/euvd-2026-352378 https://cve.radiocsirt.org/vuln/fkie_cve-2026-72642 <p>The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.</p> <p>The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.</p> https://cve.radiocsirt.org/vuln/fkie_cve-2026-72642 https://cve.radiocsirt.org/vuln/ghsa-2rm9-5cpg-f943 <p>The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.</p> <p>The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.</p> https://cve.radiocsirt.org/vuln/ghsa-2rm9-5cpg-f943 https://cve.radiocsirt.org/vuln/wid-sec-w-2026-2841 <p>Ein Angreifer kann mehrere Schwachstellen in Elasticsearch ausnutzen, um beliebigen Programmcode auszuführen, und um einen Denial of Service Angriff durchzuführen.</p> <p>Ein Angreifer kann mehrere Schwachstellen in Elasticsearch ausnutzen, um beliebigen Programmcode auszuführen, und um einen Denial of Service Angriff durchzuführen.</p> https://cve.radiocsirt.org/vuln/wid-sec-w-2026-2841