Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-63665 is a critical-severity Code Injection (CWE-94) vulnerability in Gtedge Gt Edge Ai. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 37th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Mobile/Edge AI; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) 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-2025-63665 is a code injection vulnerability (CWE-94) affecting GT Edge AI Community Edition versions before v2.0.12. It enables attackers to execute arbitrary code by injecting a crafted JSON payload into the Prompt window. The issue carries a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), marking it as critical due to its network accessibility, low attack complexity, lack of required privileges or user interaction, and severe impacts across confidentiality, integrity, and availability.
Remote, unauthenticated attackers can exploit this vulnerability over the network without user interaction. By crafting and submitting a malicious JSON payload to the Prompt window, they achieve arbitrary code execution on the targeted system, potentially leading to full compromise.
Advisories from researcher p80n-sec detail the vulnerability, including proof-of-concept information. Mitigation requires upgrading to GT Edge AI Community Edition v2.0.12 or later, as earlier versions remain susceptible. Further technical details and reproduction steps are available at https://gist.github.com/p80n-sec/e5eefcef155e9dd14aaaaa49f9f94cd1 and https://github.com/p80n-sec/Vulnerability-Research/blob/main/CVE-2025-63665/CVE-2025-63665.md.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-204583
Vulnerability Data
An issue in GT Edge AI Community Edition Versions before v2.0.12 allows attackers to execute arbitrary code via injecting a crafted JSON payload into the Prompt window.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Mobile/Edge AI
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1427 — Prompt injection into LLM window reaches model then unsanitized output enables RCE.
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.