CVE-2024-7765
H2O 3.46.0.2
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2024-7765 is a high-severity Data Amplification (CWE-409) vulnerability in H2O H2O. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked in the top 49% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SC-5 (Denial-of-service Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-7765 affects h2oai/h2o-3 version 3.46.0.2 and involves a denial-of-service vulnerability triggered by uploading and repeatedly parsing a large GZIP file. This improper handling of highly compressed data causes significant data amplification, leading to memory exhaustion and a surge in concurrent slow-running jobs that render the server unresponsive. The issue is classified under CWE-409 with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).
Unauthenticated remote attackers can exploit this vulnerability with low complexity over the network. By supplying a malicious large GZIP file for repeated parsing, they trigger excessive resource consumption, resulting in denial of service through server unresponsiveness.
Mitigation details are available in the referenced advisory at https://huntr.com/bounties/0e58b1a5-bdca-4e60-af92-09de9c76a9ff.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6953
Vulnerability Data
In h2oai/h2o-3 version 3.46.0.2, a vulnerability exists where uploading and repeatedly parsing a large GZIP file can cause a denial of service. The server becomes unresponsive due to memory exhaustion and a large number of concurrent slow-running jobs. This issue…
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arises from the improper handling of highly compressed data, leading to significant data amplification.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: h2o
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation can reject or limit decompression of data whose expansion ratio exceeds safe thresholds.
DoS protection limits the resource-exhaustion impact when a decompression bomb is processed.
Resource allocation controls bound memory/CPU consumption that a data-amplification attack would otherwise exhaust.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure-development practices include input-validation and resource-limit checks that prevent improper handling of compressed data.
Runtime monitoring of compute resources can detect exhaustion caused by decompression bombs.
Capacity planning and monitoring directly limits the availability impact of data-amplification attacks.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing can uncover decompression-bomb vulnerabilities before release.
Redundancy helps availability but does not address the root cause of the weakness.
Monitoring can detect anomalous resource usage but does not prevent the weakness.
Secure development lifecycle includes input validation and resource-limit checks that mitigate data-amplification attacks.
Application security requirements can mandate limits on decompression size and ratio.
Secure architecture principles encourage defensive design against resource-exhaustion threats.