Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/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-2026-27004 is a medium-severity Generation of Error Message Containing Sensitive Information (CWE-209) vulnerability in Openclaw Openclaw. Its CVSS base score is 6.9 (Medium).
Operationally, ranked at the 1th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8410
Vulnerability Data
OpenClaw is a personal AI assistant. Prior to version 2026.2.15, in some shared-agent deployments, OpenClaw session tools (`sessions_list`, `sessions_history`, `sessions_send`) allowed broader session targeting than some operators intended. This is primarily a configuration/visibility-scoping issue in multi-user environments where peers are…
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not equally trusted. In Telegram webhook mode, monitor startup also did not fall back to per-account `webhookSecret` when only the account-level secret was configured. In shared-agent, multi-user, less-trusted environments: session-tool access could expose transcript content across peer sessions. In single-agent or trusted environments, practical impact is limited. In Telegram webhook mode, account-level secret wiring could be missed unless an explicit monitor webhook secret override was provided. Version 2026.2.15 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Insufficient information to map techniques.CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces per-session access policies so that tools such as sessions_list/history/send cannot target peer sessions beyond the intended scope.
Enforces information-flow rules that restrict transcript visibility between differently-trusted users in shared-agent deployments.
Limits the privileges granted to session tools and webhook monitors so they operate only on the accounts/sessions for which they were explicitly configured.
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.
Authentication directly verifies the source of users/services/hardware, mitigating origin validation failures.
Verifying identity assertions enforces origin validation for conveyed claims.
Secure SDLC practices directly require sanitized error handling to prevent sensitive data disclosure.
Documenting authorized flows supports origin validation by defining expected sources.
Protecting networks from unauthorized access requires origin checks on communication sources.
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.
Secure coding standards explicitly forbid exposing sensitive data in errors.
Security testing can detect error messages that leak sensitive information.
Logging policy can require suppression of sensitive data in error messages.
Network security controls enforce origin validation at network boundaries.
Security of network services includes validating the authenticity of service endpoints.
Network segregation reduces exposure but does not directly validate origins.