Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-35485 is a high-severity Path Traversal (CWE-22) vulnerability. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 49th 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-2026-35485 is an unauthenticated path traversal vulnerability in the load_grammar() function of text-generation-webui, an open-source web interface for running Large Language Models. Versions prior to 4.3 are affected, where the vulnerability stems from a lack of server-side validation of dropdown values in Gradio, allowing arbitrary file reads on the server filesystem without extension restrictions.
An unauthenticated remote attacker can exploit this by sending POST requests with directory traversal payloads, such as "../../../etc/passwd", via the API endpoint. Successful exploitation results in the full contents of the targeted file being returned in the response, enabling unauthorized access to sensitive data like configuration files or system information.
The GitHub security advisory for text-generation-webui states that the vulnerability is fixed in version 4.3, recommending users upgrade to this or later versions for mitigation.
This issue is particularly relevant to deployments of AI/ML inference tools, as text-generation-webui is commonly used to host LLM interfaces exposed over networks. No real-world exploitation has been reported in available sources.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19669
Vulnerability Data
text-generation-webui is an open-source web interface for running Large Language Models. Prior to 4.3, an unauthenticated path traversal vulnerability in load_grammar() allows reading any file on the server filesystem with no extension restriction. Gradio does not server-side validate dropdown values,…
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so an attacker can POST directory traversal payloads (e.g., ../../../etc/passwd) via the API and receive the full file contents in the response. This vulnerability is fixed in 4.3.
- 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: gradio, text-generation-webui
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.