CVE-2026-43899
Published: 11 May 2026
Summary
CVE-2026-43899 is a critical-severity Improper Input Validation (CWE-20) vulnerability. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Client Execution (T1203); ranked at the 24.8th 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 LLM/Generative AI Risks risk domain.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-29336
Vulnerability details
DeepChat is an open-source artificial intelligence agent platform that unifies models, tools, and agents. Prior to v1.0.4-beta.1, An incomplete mitigation for CVE-2025-55733 leaves DeepChat vulnerable to an arbitrary protocol execution bypass (RCE). While the patch correctly restricted api.openExternal() inside the…
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renderer's preload/index.ts script, it structurally neglected to sanitize native Electron pop-up window handlers. An attacker or a compromised AI endpoint returning a Markdown link can trigger a target="_blank" native window interception in tabPresenter.ts, which forwards the malicious URL directly to shell.openExternal(url) and completely bypasses the isValidExternalUrl security boundary. This vulnerability is fixed in v1.0.4-beta.1.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, artificial intelligence
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Direct RCE via unsanitized Electron shell.openExternal on malicious target=_blank Markdown links from compromised AI responses, enabling client-side code execution.
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.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.