CVE-2023-44976 (GCVE-0-2023-44976)
Vulnerability from cvelistv5 – Published: 2025-08-01 00:00 – Updated: 2025-08-01 14:28Summary
Hangzhou Shunwang Rentdrv2 before 2024-12-24 allows local users to terminate EDR processes and possibly have unspecified other impact via DeviceIoControl with control code 0x22E010, as exploited in the wild in October 2023.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-08-01 14:27 UTC
CWE
- CWE-782 - Exposed IOCTL with Insufficient Access Control
Assigner
References
2 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Hangzhou Shunwang | Rentdrv2 |
Affected:
1aed62a63b4802e599bbd33162319129501d603cceeb5e1eb22fd4733b3018a3
(custom)
Affected: 9165d4f3036919a96b86d24b64d75d692802c7513f2b3054b20be40c212240a5 (custom) |
guessed |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 691d8c0c-0328-4274-94b7-8030852db8a5
Exploited: Yes
Timestamps
First Seen: 2026-06-01
Asserted: 2026-06-01
Scope
Notes: KEVIntel entry: Hangzhou Shunwang Rentdrv2 before 2024-12-24 allows local users to terminate EDR processes and possibly have unspecified other impact via... | Affected: Hangzhou Shunwang / Rentdrv2 | CVSS: 3.2 (LOW) | EPSS: 0.00165 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: kevintel
Details
| Feed | KEVIntel (kevintel.com) |
|---|---|
| Title | Hangzhou Shunwang Rentdrv2 before 2024-12-24 allows local users to terminate EDR processes and possibly have unspecified other impact via... |
| Vendor | Hangzhou Shunwang |
| Product | Rentdrv2 |
| Added Date | 2026-06-01T10:38:13.436Z |
| Cvss Score | 3.2 |
| Epss Score | 0.00165 |
| Cvss Severity | LOW |
| Epss Percentile | 0.06002 |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-06-23 14:03 UTC
| Updated: 2026-07-10 06:14 UTC
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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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