Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-7149 is a medium-severity Path Traversal (CWE-22) vulnerability. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 34th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SI-10 (Information Input Validation) — 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-2026-7149 is a path traversal vulnerability (CWE-22) discovered in the dexhunter kaggle-mcp project, affecting the prepare_kaggle_dataset function in the file src/kaggle_mcp/server.py. The issue arises from improper handling of the competition_id argument, allowing attackers to manipulate it and traverse to unintended paths. It impacts versions up to the commit 406127ffcb2b91b8c10e20e6c2ca787fbc1dc92d. The project follows a rolling release strategy, so specific affected or patched versions are not defined.
The vulnerability has a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L), indicating it can be exploited remotely over the network by unauthenticated attackers with low complexity and no user interaction required. Successful exploitation enables limited impacts on confidentiality, integrity, and availability, such as reading, writing, or modifying files outside the intended directory via path traversal.
Advisories from VulDB and the project's GitHub repository note that the vulnerability was reported early via issue #1, but the maintainers have not yet responded. No patches or mitigations are specified due to the rolling release model; security practitioners should monitor the repository at https://github.com/dexhunter/kaggle-mcp/ and issue tracker at https://github.com/dexhunter/kaggle-mcp/issues/1 for updates. The exploit has been publicly disclosed and is available for use.
In context, dexhunter kaggle-mcp relates to handling Kaggle datasets, which may involve machine learning workflows, though no real-world exploitation in the wild has been reported as of the CVE publication on 2026-04-27.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-25911
Vulnerability Data
A vulnerability has been found in dexhunter kaggle-mcp up to 406127ffcb2b91b8c10e20e6c2ca787fbc1dc92d. This vulnerability affects the function prepare_kaggle_dataset of the file src/kaggle_mcp/server.py. The manipulation of the argument competition_id leads to path traversal. The attack is possible to be carried out remotely.…
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The exploit has been disclosed to the public and may be used. This product adopts a rolling release strategy to maintain continuous delivery. Therefore, version details for affected or updated releases cannot be specified. The project was informed of the problem early through an issue report but has not responded yet.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: mcp
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
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.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
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 in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.