CVE-2026-45033
Github Copilot-Cli ≤ 1.0.43
Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/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-2026-45033 is a high-severity Incorrect Behavior Order (CWE-696) vulnerability in Github Copilot-Cli. Its CVSS base score is 8.5 (High).
Operationally, ranked at the 28th 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 Enterprise AI Assistants; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and RA-5 (Vulnerability Monitoring and Scanning) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-30055
Vulnerability Data
GitHub Copilot CLI brings AI-powered coding assistance directly to your command line. Prior to 1.0.43, a security vulnerability has been identified in GitHub Copilot CLI where a malicious bare git repository nested inside a project directory can achieve arbitrary code…
more
execution when the agent performs git operations. By exploiting git's automatic bare repository discovery during directory traversal, an attacker can set core.fsmonitor or other executable config keys to run arbitrary commands without user awareness or approval. The vulnerability arises because git's core.fsmonitor config key (and 15+ similar keys such as core.hookspath, diff.external, merge.tool, etc.) can specify arbitrary shell commands that git will execute as part of normal operations like status, diff, or rev-parse. This vulnerability is fixed in 1.0.43.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, github copilot
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation at post-design stages can discover incorrect ordering of related behaviors before deployment.
Vulnerability scanning may surface order-related weaknesses after code is built but does not address their root cause.
Mandating a documented development process and standards enforces review of behavior ordering within the software lifecycle.
Security and privacy engineering principles applied during design and implementation directly require correct sequencing of operations to avoid introducing order-dependent flaws.
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 enforce correct sequencing of security-relevant operations during design and coding.
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 can detect ordering flaws but does not prevent them during development.
Secure development life cycle mandates correct sequencing of security activities, directly preventing incorrect behavior order.
Secure system architecture and engineering principles require proper ordering of design and implementation steps.
Secure coding standards enforce correct execution order of security-critical operations.
Change management may catch order-related issues during reviews but does not address root cause.