Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2025-48957 is a high-severity Relative Path Traversal (CWE-23) vulnerability in Astrbot Astrbot. 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 48th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
AstrBot is a large language model chatbot and development framework that contains a path traversal vulnerability in versions 3.4.4 through 3.5.12. The flaw, tracked under CWE-22 and CWE-23, permits unauthorized access to files outside intended directories and can expose sensitive information including API keys for LLM providers, account passwords, and other configuration data. It carries a CVSS 3.1 score of 7.5 reflecting network attack vector, low complexity, and no required authentication or user interaction.
An unauthenticated remote attacker can send crafted requests to the dashboard component over the network to read arbitrary files on the host system, achieving high-impact confidentiality exposure without affecting integrity or availability.
The vulnerability was corrected in pull request 1676 and released in version 3.5.13. Official guidance recommends upgrading to 3.5.13 or later; as a temporary workaround users may edit cmd_config.json to disable the dashboard feature. The associated GitHub security advisory and commit references provide the patch details and confirmation of the fix.
The affected software is an LLM chatbot framework, placing the issue in an AI/ML context, though the EPSS score has remained flat at 0.0106 with no indicated rise after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-16634
Vulnerability Data
AstrBot is a large language model chatbot and development framework. A path traversal vulnerability present in versions 3.4.4 through 3.5.12 may lead to information disclosure, such as API keys for LLM providers, account passwords, and other sensitive data. The vulnerability…
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has been addressed in Pull Request #1676 and is included in version 3.5.13. As a workaround, users can edit the `cmd_config.json` file to disable the dashboard feature as a temporary workaround. However, it is strongly recommended to upgrade to version v3.5.13 or later to fully resolve this issue.
- 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: large language model, llm
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.
Explicit validation of path inputs stops .. sequences from ever being interpreted by the file system.
Information-flow rules can be configured to reject traversals that would move data outside an approved directory boundary.
Least privilege reduces the set of reachable files even when a traversal succeeds.
Secure-engineering principles require safe pathname construction and input neutralization before any file operation.
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 SDLC practices directly require input validation and path sanitization that prevent relative traversal.
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, but does not itself implement the fix.
Secure development lifecycle mandates input validation and path-handling controls that directly prevent relative path traversal.
Application security requirements explicitly call for controls against path traversal and other injection flaws.
Secure architecture principles include directory isolation and canonicalization, reducing but not eliminating traversal risk.
Secure coding standards require neutralizing path traversal sequences, directly addressing CWE-23.
Information access restriction limits which files can be reached, mitigating impact but not preventing the traversal flaw.