CVE-2026-45539
Published: 15 May 2026
Summary
CVE-2026-45539 is a high-severity Link Following (CWE-59) vulnerability. Its CVSS base score is 7.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Compromise Software Dependencies and Development Tools (T1195.001); ranked at the 16.8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-30561
Vulnerability details
Microsoft APM is an open-source, community-driven dependency manager for AI agents. From 0.5.4 to 0.12.4, two primitive integrators in apm-cli enumerate package files with bare Path.glob() / Path.rglob() calls and read each match with Path.read_text(), transparently following symbolic links. A…
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symlink committed inside a remote APM dependency under .apm/prompts/<x>.prompt.md or .apm/agents/<x>.agent.md is preserved verbatim into apm_modules/ on clone and then dereferenced during integration, with the resolved content written as a regular file into the project's deploy directories. The package content_hash, the pre-deploy SecurityGate scan, and apm audit do not flag this. The deploy roots are not added to the auto-generated .gitignore, so the resulting files are staged by git add by default. This vulnerability is fixed in 0.13.0.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Symlink-following flaw in dependency unpack allows malicious package to write arbitrary files into victim project (CWE-59), directly enabling compromise of software dependencies.
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.