CVE-2026-92952 (GCVE-0-2026-92952)
Vulnerability from cvelistv5 – Published: 2026-09-17 13:46 – Updated: 2026-09-17 15:44
VLAI
EPSS
VEX
Title
vm2 3.11.4 through 3.11.6 Sandbox Symbol Filtering Bypass
Summary
vm2 versions 3.11.4 through 3.11.6 incompletely filter Node.js registered internal symbols across the sandbox boundary. The extraction filters in lib/setup-sandbox.js and the cross-realm symbol checks and write traps in lib/bridge.js use a fixed list of known dangerous registered symbols that omits nodejs.stream.disturbed and nodejs.stream.errored, which are exposed on host WebStream prototypes on newer Node.js releases (validated on Node.js v25.8.0). When the embedder exposes a host WebStream object and the host stream/web module to the sandbox, sandbox code can obtain the real host symbols via Object.getOwnPropertySymbols(streamWeb.ReadableStream.prototype) and use them as write keys on host stream objects, corrupting host-visible stream state — for example making stream.Readable.isDisturbed() return false for an already-consumed stream. This can bypass host logic that relies on Node's public stream-state helpers to enforce one-shot body consumption, reject errored streams, or decide whether a stream is safe to hand to another component. It is not a host code-execution primitive in the reported proof of vulnerability. This is an incomplete fix for the earlier nodejs.* symbol filtering issue. Fixed in vm2 3.11.7.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-17 15:43 UTC
CWE
- CWE-669 - Incorrect Resource Transfer Between Spheres
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/patriksimek/vm2/security/advis… | vendor-advisory |
| https://www.vulncheck.com/advisories/vm2-3.11.4-t… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| patriksimek | vm2 |
Affected:
3.11.4 , < 3.11.7
(semver)
Unaffected: 3.11.7 (semver) |
guessed |
Date Public
2026-08-24 00:00
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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