CVE-2026-35641
Openclaw ≤ 2026.3.24
Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:A/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-2026-35641 is a high-severity Acceptance of Extraneous Untrusted Data With Trusted Data (CWE-349) vulnerability in Openclaw Openclaw. Its CVSS base score is 8.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Subvert Trust Controls (T1553); ranked at the 3th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) 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-2026-35641 is an arbitrary code execution vulnerability affecting OpenClaw versions prior to 2026.3.24. The flaw exists in the local plugin and hook installation process, where attackers can craft a .npmrc file to override the git executable. This configuration is abused during npm install execution within the staged package directory, particularly when git dependencies are present, enabling the execution of arbitrary programs specified in the attacker-controlled .npmrc file. The vulnerability is rated 7.8 (High) under CVSS 3.1 (AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) and maps to CWE-349 (Acceptance of Extraneous Untrusted Data With Trusted Path).
A local attacker can exploit this vulnerability by placing a malicious .npmrc file in a location accessible during plugin or hook installation. No privileges are required (PR:N), but the attack demands local access (AV:L) and user interaction (UI:R), such as a user triggering an npm install in the affected directory. Successful exploitation leads to full arbitrary code execution with high impacts on confidentiality, integrity, and availability, allowing the attacker to run malicious programs on the victim's system.
Advisories from the OpenClaw GitHub security page (GHSA-m3mh-3mpg-37hw) and VulnCheck detail the issue and recommend upgrading to OpenClaw 2026.3.24 or later, where the vulnerability is addressed. Practitioners should review these references for full patch details and verify installations to prevent exploitation via malicious plugins or hooks.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-21436
Vulnerability Data
OpenClaw before 2026.3.24 contains an arbitrary code execution vulnerability in local plugin and hook installation that allows attackers to execute malicious code by crafting a .npmrc file with a git executable override. During npm install execution in the staged package…
more
directory, attackers can leverage git dependencies to trigger execution of arbitrary programs specified in the attacker-controlled .npmrc configuration file.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 5 hardening rules · 2 OS baselines
V1.2.2V10.4.7V3.7.3V5.3.1
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can block untrusted data from being accepted or processed as if it were trusted.
Input validation directly stops acceptance of untrusted data mixed into trusted inputs.
Associating security attributes with data allows the system to distinguish and reject extraneous untrusted portions.
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 address proper trust-boundary enforcement and input validation, preventing this class of weakness during development.
Cryptographic integrity checks on data-at-rest can detect tampering or substitution of untrusted content mixed with trusted data.
Cryptographic integrity mechanisms on data-in-transit can prevent acceptance of extraneous untrusted data by validating origin and detecting modification.
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 the weakness but does not itself implement preventive controls.
Secure development lifecycle mandates input validation and trust-boundary enforcement that directly prevents acceptance of untrusted data alongside trusted data.
Application security requirements explicitly call for strict separation and validation of trusted versus untrusted data sources.
Secure architecture principles require explicit trust boundaries and data-origin checks that mitigate mixing of trusted and untrusted inputs.
Secure coding standards mandate input sanitization and provenance checks that prevent acceptance of extraneous untrusted data.
Information access restriction limits who can supply data but does not address validation of data origin or trust level.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (1 rule)
- V-248574 YUM must be configured to prevent the installation of patches, service packs, device drivers, or OL 8 system components that have not been digitally signed using a certificate that is recognized and approved by the organization. prevents CWE-349