Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-28676 is a high-severity Path Traversal (CWE-22) vulnerability in Opensift Opensift. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 36th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Privacy and Disclosure 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-2026-28676 is a path injection vulnerability (CWE-22) affecting OpenSift, an AI study tool that processes large datasets via semantic search and generative AI. In versions prior to 1.6.3-alpha, multiple storage helpers employed path construction patterns that failed to consistently enforce base-directory containment. This flaw exposed file read, write, and delete operations to path-injection risks when malicious path-like inputs were supplied. The vulnerability carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).
An authenticated attacker with low privileges (PR:L) can exploit this over the network (AV:N) with low complexity (AC:L) and no user interaction (UI:N). Successful exploitation allows arbitrary file read, write, or delete operations outside the intended base directory, potentially leading to high impacts on confidentiality, integrity, and availability (C:H/I:H/A:H).
The issue was addressed in OpenSift version 1.6.3-alpha, as detailed in the project's GitHub security advisory (GHSA-ww4m-c7hv-2rqv), release notes, pull request #67, and fixing commits (1126e0a503876056a68a434e19f64158a5a4840b and de99b9c). Security practitioners should upgrade to at least 1.6.3-alpha to mitigate the risk.
As an AI-powered tool for dataset analysis, this vulnerability highlights path traversal risks in AI/ML workflows handling user-supplied inputs for storage operations, though no public evidence of real-world exploitation has been reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-9987
Vulnerability Data
OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Prior to version 1.6.3-alpha, multiple storage helpers used path construction patterns that did not uniformly enforce base-directory containment. This created path-injection risk in…
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file read/write/delete flows if malicious path-like values were introduced. This issue has been patched in version 1.6.3-alpha.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, generative ai
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.