CVE-2026-48159 (GCVE-0-2026-48159)
Vulnerability from cvelistv5 – Published: 2026-08-10 17:55 – Updated: 2026-08-11 02:04
VLAI
EPSS
VEX
Title
use-reducer-async was vulnerable to malicious code execution via compromised commits
Summary
use-reducer-async is a React useReducer with async actions. Between 2026-05-18 16:29:52 and 2026-05-19 15:26:07, the default branch contained malicious commits da72edbde5705efcec6c62e0a3dcb73687b78dc8 through df07d5711458d8b46e11dd7afaaa21e88cafabfb that executed remote attacker-controlled code on developer machines during `npm install`. The commits were removed by force-push, but local clones, forks, and direct-SHA URLs may still contain them, and `npm install` against an affected checkout will still execute the code today. The package was not published to npm. `src/install.js` was added and wired into the `postinstall` script. It fetched a JavaScript payload from an attacker-controlled HTTPS endpoint (configurable via an environment variable), disabled TLS verification, and evaluated the response as code with `require` available. Execution was deliberately skipped on CI and cloud/serverless environments, targeting developer workstations. The second-stage payload was attacker-hosted and cannot be reconstructed. Assume full compromise of anything reachable from a Node process with the user's permissions. Those who ran `npm install` against an affected checkout on a developer machine on or after 2026-05-18 16:29:52 should treat the machine as compromised, rotate every credential the machine could reach, audit account activity since 2026-05-18 16:29:52, and clean local clones.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-11 02:03 UTC
CWE
- CWE-506 - Embedded Malicious Code
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/dai-shi/use-reducer-async/secu… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| dai-shi | use-reducer-async |
Affected:
>= da72edbde5705efcec6c62e0a3dcb73687b78dc8, <= df07d5711458d8b46e11dd7afaaa21e88cafabfb
|
guessed |
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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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